A small open economy DSGE model for
Pakistan.




Subject: 
Investment analysis
(International trade) Inflation (Finance) Interest rates Monetary policy 
Authors: 
Haider, Adnan Khan, Safdar Ullah 
Pub Date:  12/22/2008 
Publication:  Name: Pakistan Development Review Publisher: Pakistan Institute of Development Economics Audience: Academic Format: Magazine/Journal Subject: Business, international; Social sciences Copyright: COPYRIGHT 2008 Reproduced with permission of the Publications Division, Pakistan Institute of Development Economies, Islamabad, Pakistan. ISSN: 00309729 
Issue:  Date: Winter, 2008 Source Volume: 47 Source Issue: 4 
Topic:  Event Code: 640 Foreign trade; 900 Government expenditures 
Product:  Product Code: E561000 Interest Rates 


Accession Number:  228122190 
Full Text: 
This paper estimates a small open economy Dynamic Stochastic
General Equilibrium (DSGE) model for Pakistan using Bayesian simulation
approach. Model setup is based on new Keynesian framework, characterised
by nominal rigidity in prices with habit formation in household's
consumption. The core objective is to study whether an estimated small
open economy DSGE model provides a realistic behavior about the
structure Pakistan economy with fully articulated description of the
monetary policy transmission mechanism visavis domestic firm's
price setting behavior. To do so, we analyse the impulse responses of
key macro variables; domestic inflation, imported inflation, output,
consumption, interest rate, exchange rate, term of trade to different
structural/exogenous shocks. From several interesting results, few are;
(a) high inflation in Pakistan do not hit domestic consumption
significantly; (b) Central bank of Pakistan responds to high inflation
by increasing the policy rate by 100 to 200 bps; (c) exchange rate
appreciates in both the cases of high domestic and imported inflation;
(d) tight monetary policy stance helps to curb domestic inflation as
well as imported inflation but appreciates exchange rate significantly
(f) pass through of exchange rate to domestic inflation is very low;
finally parameter value of domestic price stickiness shows that around
24 percent domestic firms do not reoptimise their prices which implies
averaged price contract is about two quarters. JEL classifications: E32, E47, E52, F37, F47 Keywords: NewKeynesian Economics, Open Economy DSGE Models, Nominal Rigidities, Monetary Policy, Transmission Mechanism, Bayesian Approach The complex nature of DSGE models may have also limited their acceptance among policy makers, as notation can get very messy, thus creating a natural barrier for the communication of the results to policy makers, not to mention to the public. Furthermore, understanding the working of these models requires well trained macroeconomists with a modeling culture and strong statistical and computer programming skills. This also implies that central banks may need to invest additional resources to develop such models, something that might not always be considered as priority or simply resources might be scarce. Camilo E. Tovar (2008) 1. INTRODUCTION In recent years there has been a growing interest in academics, international policy institutions and central banks (1) in developing smalltomedium, even largescale, open economy macroeconomic models called Dynamic Stochastic General Equilibrium (DSGE) models based on newKeynesian framework. (2) The term DSGE was originally used by Kydland and Prescott (1982) in their seminal contribution on Real Business Cycle (RBC) model. The RBC model is based on neoclassical framework with microfounded optimisation behaviour of economic agents with flexible prices. One of the critical assumptions of this model is that fluctuations of real quantities are caused by real shock only; that is, only stochastic technology or government spending shocks play their role. Later research in DSGE models however included Keynesian shortrun macroeconomic features (called nominal rigidities), such as Calvo (1983) type staggered pricing behaviour and Taylor (1980) type wage contracts. Hence this new DSGE modeling framework labeled as newneoclassical synthesis or newKeynesian modeling paradigm. (3) This new approach combines microfoundations of both households and firms optimisation problems and with a large collection of both nominal and real (price/wage) rigidities that provide plausible shortrun dynamic macroeconomic fluctuations with a fully articulated description of the monetary policy transmission mechanism; see, for instance, Christiano, et al. (2005) and Smets and Wouters (2003). The key advantage of modern DSGE models, over traditional reduce form macroeconomic models, is that the structural interpretation of their parameters allows to overcome the famous Lucas critique (1976). (4) Traditional models contained equations linking variables of interest of explanatory factors such as economic policy variables. One of the uses of these models was therefore to examine how a change in economic policy affected these variables of interest, other things being equal. In using DSGE models for practical purposes and to recommend how central banks and policy institutions should react to the shortrun fluctuations, it is necessary to first examine the possible sources, (5) as well as to evaluate the degree of nominal and real rigidities present in the economy. In advanced economies, like US and EURO area, it is easy to determine the degree of nominal and real rigidities as these economies are fully documented. In developing economies like Pakistan, where most of economic activities are undocumented (also labeled as informal economy, black economy, or underground economy), it is very difficult to determine the exact degree of nominal and real rigidities present in the economy. However, one can approximate results using own judgments and through well defined survey based methods. (6) Broadly, this paper carries two dimensional motivation agenda. First, in emerging market economies with complex structures, one of the enduring research questions is to construct and estimate a valid microfounded economic model featured with nominal rigidities. This issue is really focusable as such economic model which comprehensively explores the transmission mechanism of economic behaviours in the developing economies is scarcely available. Problems in these dimensions are sometimes quite natural for example due to unavailability of high frequency data or because of a major share of the undocumented economy in the observed economic data. This study comes forward to meet this challenge partially (through formal economy channel) by utilising and constructing (7) the high frequency available data (quarterly basis) in the DSGE microfounded model for Pakistan economy. This study uses a simplified version of small open economy DSGE model consistent with Kolasa (2008), Liu (2006), Gali and Monacelli (2005) and Lubik and Schorfheide (2005). The overall model specification is restricted with few sources of nominal rigidities, a linear production function in labour, and a simple role for the central bank with its two main objectives of price stability and economic growth. Furthermore, foreign sector economy is considered as completely exogenous with its two key variables, output (to capture foreign productivity shock) and real interest rate (foreign monetary policy shock). Using historical data on quarterly basis by applying Bayesian estimation approach visavis combining with the prior information available in existing literature on Pakistan, this model provide several interesting results, (8) which are discussed in later sections of this paper. The rest of the paper is organised as follows: section two lay out the structure of the model; section three discusses the estimation methodology; section four carries out empirical results; section five concludes and review literature and model canonical representation are provided in appendix. 2. STRUCTURE OF THE MODEL In this section, we derive a smallscale openeconomy DSGE model for Pakistan. Following mainly Kolasa (2008), Liu (2006), Gali and Monacelli (2005) and Lubik and Schorfheide (2005), the models structure begins with the worldeconomy as inhabited by a continuum of infinitelived households, (indexed by i [member of] [0, 1]) who take decisions on consumption and savings, and set wages in a staggered fashion. (9) There is a set of firms that produce differentiated varieties of tradable intermediate goods. They have monopoly power over the varieties they produce and set prices in a staggered way. Another group of firms are importers that distribute domestically different varieties of foreign intermediate goods. These firms have monopoly power over the varieties they distribute, and also set prices in a Calvotype staggered fashion. Finally, we assume symmetric preferences and technologies and allowing potentially rich exchange rate dynamics under the assumption of complete international asset markets. 2.1. Domestic Households Preferences The domestic economy is inhabited by a representative household who derives its utility from consumption [C.sub.t], and leisure 1  [L.sub.t]. Its preferences are described by an intertemporal utility function (10): [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (1) Where, [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. Where [[beta].sub.t] [member of] (0,1) is the intertemporal discount factor which describe rate of time preferences, [sigma] is the inverse of the elasticity of intertemporal substitution in consumption and [phi] is the inverse of wage elasticity of labour supply. We introduce external habit formation for the optimisation household as [H.sub.t] = h[C.sub.t1] with degree of intensity (11) indexed by h, where [C.sub.t1] is the aggregate part of consumption index. As usual, it is assumed that, [sigma] > 0 and [phi] > 1. The variable [C.sub.t] is defined as the composite consumption index of foreign and domestically produced goods: [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (2) Where [eta] > 0 is the elasticity of intratemporal substitution between a bundle of home goods [C.sub.H,t] and a bundle of foreign goods [C.sub.F,t], while [alpha] [member of] (0, 1) is the trade share also measures the degree of openness. The aggregate consumption indices [C.sub.H,t] and [C.sub.F,t] are defined respectively as: [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (3) Where [C.sub.H,t](i) and [C.sub.F,t](i) are respectively the domestic households consumption levels of home ith good, with i [member of] [0, n] and foreign ith good, with i [member of] [n, 1]. It is also assumed that parameter, [epsilon] > 0 is the elasticity of intratemporal substitution among goods produced to be same in two countries. Under the supposition of CES, continuous time aggregator from Equation (3) further yields respective demand functions for [C.sub.H,t] and [C.sub.F,t]. These demand functions obtained after optimal allocation for good i over continuous time scale. The demand functions are: [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (4) Where [P.sub.H,T](i) and [P.sub.F,T](i) are prices of domestic and foreign good i respectively. Under the assumption of symmetry across i household allocate aggregate expenditure based on the following demand functions: [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (5) Where [P.sub.H,t] and [P.sub.F,t] are domestic and foreign prices indices and [P.sub.t] [equivalent to] [[(1  [alpha][P.sup.1[eta].sub.H,t] + [alpha][P.sup.1[eta].sub.F,t].sup.1/1[eta]] is the consumer price index (CPI). The household does want to maximise its utility level subject to the following budget constraints at time t: [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (6) Where [Q.sub.t,t+1] is defined as a stochastic discount factor for assessing consumption streams (12) (or asset price kernel) with the property that the price in period t of any bond portfolio with random values [D.sub.t] (denotes nominal payoffs from a portfolio of assets at time t  1) in the following period is given by: [B.sub.t] = [E.sub.t][[Q.sub.t,t+1] [D.sub.t+1]] (13) [W.sub.t] is the nominal wage for labour services provided to firms. Since total consumption expenditure for the domestic household is given by [P.sub.H,T][C.sub.H,t] + [P.sub.F,T][C.sub.F,t] = [P.sub.t][C.sub.t]. Hence in the aggregate, household faces the budget constraint as: [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (7) Consider [[XI].sub.t] is the marginal utility of income and labourleisure choice (14) is followed by the intratemporal optimality condition: [[XI].sub.tt] = [P.sub.t]/[W.sub.t], Therefore, intertemporal consumption choice is obtained after maximising the life time utility function subject to budget constraint (7). So optimisation problem yields the following FOCs are: [C.sub.t]  h[C.sub.t1])  [sigma] [W.sub.t]/[P.sub.t] = [L.sup.[phi].sub.t] (8) By equating marginal rates of substitution to relative prices, yields the optimal portfolio choice as: [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (9) The Equation (9) can also be translated into [[DELTA].sub.t] form as: [Q.sub.t, t+1] = [beta][E.sub.t]{([P.sub.t]/[P.sub.t+1])([[XI].sub.tt+1]/[[XI].sub.tt])} (10) Since monetary authority's main instrument is assumed to be short term nominal interest rates as: [R.sub.t] = [E.sub.t][1/[Q.sub.t,t+1]), so Equation (10)can also be represented as: [beta][R.sub.t][E.sub.t] {([P.sub.t]/[P.sub.t+1])([[XI].sub.tt+1]/[[XI].sub.tt])} = 1 (11) Further, Equations (5), (8) and (9) can also be expressed in simple loglinearisation form as: [c.sub.H,t] = (1  [alpha])[[eta]([p.sub.H,t]  [p.sub.t]) + [c.sub.t]] and [c.sub.F,t] = [alpha][[eta]([p.sub.F,t]  [p.sub.t]) + [c.sub.t]] (12) [w.sub.t]  [p.sub.t] = [phi][l.sub.t] + [sigma]/1  h [c.sub.t] (13) [E.sub.t][c.sub.t+1] (1  h/[sigma])([r.sub.t]  [E.sub.t][[pi].sub.t+1]) = [c.sub.t] (14) Where, is [[pi].sub.t+1] = [p.sub.t+1]  [p.sub.t] is CPI inflation and [c.sub.t] = 1/1  h([c.sub.t]  h[c.sub.t1]) is simple logform of consumption variable. 2.2. Domestic Producers and Firms The domestic economy is also inhabited by domestic producers, own identical monopolistically competitive firms, producing differentiated goods. There is also a continuum of firms, indexed by j [member of] (0, 1) where each firm maximises its profits, subject to an isolated demand curve (5) and use only a homogenous type of labour for production. Consider domestic firms operate the same CRStechnology (i.e., firms have access to a linear production technology) that uses labour as its only input: [Y.sub.H,t] = [A.sub.t][L.sub.t](j) (15 Where, [A.sub.t] is the country specific labor productivity shock. We define aggregate output as: [Y.sub.t] = [[[[integral].sup.1.sub.0][Y.sub.t][(j).sup.(1[rho])dj].sup.1/(1[rho])] (16) The loglinear aggregate production function can be written as: [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (17) Let, ln([A.sub.t]) = [a.sub.t], then (14) can be represented as: [y.sub.t] = [a.sub.t] + [l.sub.t] (18) If [TC.sub.t] represents the real total cost, then: [TC.sub.t] = [W.sub.t]/[P.sub.H,t] [Y.sub.t]/[A.sub.t] (19) By differentiating w.r.t. [Y.sub.t] (19) gives real marginal cost as: [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (20) This implies that real marginal cost is positively related with real wages and negatively with labor factor productivity. 2.2.1. CalvoType Price Setting Behaviour For our model, Calvo (1983) type staggeredprice setting is assumed. This means that domestic differentiating goods are defined subject to Calvotype pricesetting. Consider, at each period, only 1  [[theta].sub.t] fraction of randomly selected domestic firms set prices optimally, while [[theta].sub.t] [member of] [0,1] firms keep their prices unchanged. (15) As a result the average duration of a price is given by 1/1  [[theta].sub.t]. This implies that 0t firms are assumed to reset their prices, [P.sup.l.sub.t](j) by indexing it to last period inflation. Therefore, [[theta].sub.t] becomes a natural index of price stickiness. The index of domestic prices (16) is defined as: [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (21) Where [P.sub.H,t](J) = [P.sub.H,t](k) for all continuum of firms j, k. Let each home firm j sets a new price [P.sup.*.sub.H,t](J) in order to maximise the present market value of its stream of expected future profits. Therefore domestic price level can be defined as: [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (22) In aggregate, firms reoptimise their prices and maximise their profits after setting the new price [P.sup.*.sub.H,t](j) at time t as: [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (23) With respect to [P.sup.*.sub.H,t](j) Subject to the following demand function: [Y.sub.H,t+k] [less than or equal to] ([C.sub.H,t+k] + [C.sup.*.sub.H,t+k])[[[P.sup.*.sub.H,t]/[P.sub.H,t+k]].sup. [epsilon]] Where [NMC.sub.H,t+k] is the nominal marginal cost and demand of firm's product drives both from domestic consumption, [C.sub.H,t] as well as foreign consumption, [C.sub.F,t]. The first order condition with (23) takes the form: [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (24) Where [epsilon]/[epsilon]  1 is considered as desired or frictionless markup. (17) The above condition (24) is linearised around zeroinflation steadystate. So optimal condition (24) can be rewrite after dividing by [P.sub.H,t1] as: [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (25) Letting, [[pi].sub.H,t+k] = [P.sub.H,t+k]/[P.sub.H,t1] and [MCH.sub.H,t+k] = [NMC.sub.H,t+k]/[P.sub.H,t+k] which is a real marginal cost in period t + k. Hence, Equation (25) can be written as: [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (26) From (8) we can incorporate the value of [Q.sub.t,t+k] = [[beta].sup.k] [E.sub.t]{([P.sub.t]/[P.sub.t+k])([C.sub.t+k]/[C.sub.t]).sup.[sigma]]} in Equation (26) as: [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (27) Since [P.sub.t]/[C.sup.[sigma].sub.t] is independent of summation and its values are known at time t, so (27) yields: [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (28) In the zero inflation steadystate, [P.sup.*.sub.H,t]/[P.sub.H,t1] = 1 and [[pi].sub.H,t+1] = 1. So loglinear form of (28) at zero inflation steadystate is: [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (29) Where [mc.sub.t+k] denotes log deviation of marginal cost from its steady state value. The first order Taylor expansion of (29) yields: [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (30) Combining the loglinear of Equation (30) with the result (22) yields the New Keynesian Phillips Curve (NKPC): [[pi].sub.H,t] = [beta](1  [[theta].sub.H])[E.sub.t]{[[pi].sub.H,t+1]} + [[theta].sub.H][[pi].sub.H,t1] + [[lambda].sub.H][mc.sub.t] (31) Where, [[lambda].sub.H] = (1  [[theta].sub.H])(1  [beta][[theta].sub.H])/[[theta].sub.H]. The NKPC Equation (31) implies that home country's inflation dynamics drives from both forward looking and backward looking components. The above NKPC representation also called a hybrid version of NKPC with forward looking and backward looking behaviour. It further shows that real marginal cost is also a main determinant of domestic inflation. 2.3. Import Goods Retailers Following Gali and Monacelli (2005) and Monacelli (2005), we assume that the lawofone price (LOP) holds at the wholesale level for imports. But, endogenous fluctuations from purchasing power parity (PPP) in the short run arise due to the existence of monopolistically competitive importers. Since, they keep domestic import prices over and above the marginal cost. As a result, the LOP fails to hold at the retail level for domestic imports. Importers purchase foreign goods at worldmarket prices [P.sup.*.sub.F,t](j) so that the law of one price holds at the border. These purchased foreign goods are then sell to domestic consumers and a markup is charged over their cost, which creates a wedge between domestic and import prices of foreign goods when measured in the same currency. Therefore, law of one price (l.o.p.) gap can be defined as: (18) [[psi].sub.F,t] = [P.sup.*.sub.t]/[e.sub.t][P.sub.F,t] (32) Where [e.sub.t] is the nominal exchange rate. Following a similar staggeredpricing argument (29) as defined in the case of domestic producer, the optimal price setting behaviour for the domestic monopolistically competitive importer could be defined as: [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (33) Where, [[theta].sub.F] [member of] [0, 1] is the stickiness parameter of importer retailers that cannot reoptimise their prices every period. However, in order to maximise profits, domestic retailers set domestic currency price of imported goods as a markup over [[psi].sub.F,t], as they are concerned with the law of one gap and future path of imported inflation. Therefore, endogenous fluctuations from PPP occurred which provides a mechanism for incomplete passthrough in the shortrun. This mechanism finally results in a new Keynesian Phillips curve relationship. Hence, Equation (31) can be defined in term of [[pi].sub.F,t] as: [[pi].sub.F,t] = [beta](1  [[theta].sub.F])[E.sub.t]{[[pi].sub.F,t+1]) + [[theta].sub.F][[pi].sub.F,t1] + [[lambda].sub.F][[psi].sub.F,t] (34) Where [[lambda].sub.F] = (1  [[theta].sub.F])(1  [beta][[theta].sub.F])/[[theta].sub.F]. Since consumer price index (CPI) is defined as: [P.sub.t] [equivalent to] [[(1  [alpha])[P.sup.1[eta].sub.H,t] + [alpha][P.sup.1[eta].sub.F,t]].sup.1/1[eta]], therefore using (31) and (34) the loglinear form of overall inflation is defined as: [[pi].sub.t] [equivalent to] [(1  [alpha])[[pi].sub.H,t] + [alpha][[pi].sub.F,t]] (35) The above functional form of overall inflation with specifications (31) and (34) completes inflation dynamics for a small open economy like Pakistan. 2.4. Foreign Sector Economy In this section we drive the open economy dynamics between inflation; terms of trade; real exchange rate; international risk sharing and uncovered interest parity. Since e, is nominal exchange rate. We defined home country real exchange rate as: [RER.sub.t] [equivalent to] [e.sub.t]P.sub.t/[P.sup.*.sub.t] (36) Similarly, counterpart of home country, foreign country real exchange rate is the inverse of (36). Due to law of one price gap, term of trade between home and foreign countries may differ. Therefore, domestic term of trade (TOT) [S.sub.t] and foreign TOT [S.sup.*.sub.t] can be defined as: [S.sub.t] [equivalent to] [P.sub.F,t]/[P.sub.H,t] and [S.sup.*.sub.t] [equivalent to] [P.sup.*.sub.H,t]/[P.sup.*.sub.F,t] (37) The domestic TOT is thus the price of foreign goods (imports) per unit of domestic goods (exports) and foreign TOT is domestic goods per unit the price of foreign goods. Both Terms of trade coincide inversely only if passthrough is perfect. But in case of imperfect passthrough, the relationship between law of one price gaps and terms of trade can be defined as: [[psi].sub.F,t]/[S.sub.t] [equivalent to] [[psi].sup.*.sub.H,t]/[S.sup.*.sub.t] (38) As loglinearising of CPI formula around the steadystate yields the following relationship: [p.sub.t] [equivalent to] [(1  [alpha])[p.sub.H,t] + [alpha][p.sub.F,t]] and loglinear form of TOT [S.sub.t] as: [S.sub.t] [equivalent to] [p.sub.F,t]  [p.sub.H,t]. Solving both simultaneously as: [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (39) Equation (39) in first difference form can be represented in inflation notation as: [[??].sub.t] [equivalent to] [[[??].sub.H,t] + [alpha][DELTA][S.sub.t]] (40) Solving (35) and (40) we have; [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (41) This shows that domestic TOT is positively related with foreign inflation and its own lag and negatively with domestic inflation. The real exchange rate of (36) in loglinear form [q.sub.t] can be presented after solving (32), (36) and (37) as: [q.sub.t] = [[??].sub.t]  (1  [alpha])[s.sub.t] (42) Where [[psi].sub.t] [equivalent to] ln([[PSI].sub.t]) = [p.sup.*.sub.t]  [p.sub.F,t]  [e.sub.t] is LOP gap. If this is equal to one then import price index is equal to foreign price index divided by nominal exchange rate. The Equation (42) shows that real exchange rate negatively related with both law of one price gap as well as terms of trade. The loglinear transformation of (36) yields nominal exchange rate relationship as: [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (43) Since, under the assumption of complete international financial markets implies perfect risksharing between households in both countries. This means that the expected nominal return from riskfree bounds in home currency terms must be same as the expected domestic currency returns from foreign bonds. So, [E.sub.t][Q.sub.t,t+1] = ([E.sub.t][Q.sup.*.sub.t,t+1] [e.sub.t+1]/[e.sub.t]) ... ... ... (44) Using this notion (44), we can extent (9) as: [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (45) [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (46) The loglinear form of (46) gives a relationship between marginal utilities across countries adjust for purchasing power as: [[XI].sub.t] = [[XI].sup.*.sub.t]  [q.sub.t] ... ... ... (47) The assumption of complete international asset market also holds another relationship called uncovered interest parity condition (UIP). [E.sub.t] {[Q.sub.t,t+1]([R.sub.t]  [R.sup.*.sub.t] [e.sup.t]/[e.sub.t+1])} = ... ... ... (48) The loglinear representation of (48) around steadystate yields the following relationship: [r.sub.t]  [r.sup.8.sub.t] = [E.sub.t][DELTA][[??].sub.t+1] ... ... ... (49) This equation implies that the interest rate differential is related with expected future exchange rate depreciation, which defined as uncovered interest parity. Similarly, expression (49) can also be written as: ([r.sub.t]  [[pi].sub.t+1])  ([r.sup.*.sub.t]  [[pi].sup.*.sub.t+1]) = [E.sub.t][DELTA][q.sub.t+1] (50) This equation implies that expected changes in real exchange rate determine by current real interest rate differentials with negative sings. 2.5. Monetary Policy Reaction Function It is assumed that domestic visavis foreign central banks follow Taylortype reaction functions. Since the basic objective of the central bank is to stabilise both output and inflation. So to specify this reaction function it needs to adjusts nominal interest rate in response to deviations of inflation, a measure of output and exchange rate depreciation from their targets. Following Clarida, Gali, and Gertler (2001), simple reaction function can be defined as: [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (51) Where [[rho].sub.r] is the degree of interest rate smoothing and [[phi].sub.1], [[phi].sub.2] are relative weights on inflation and output growth respectively. It should be note that this model is estimated using a speed limit policy rather than the traditional Taylorrule based output and inflation. A recent study Malik and Ahmed (2007) argues that State Bank of Pakistan do not follow a simple Taylortyperule, as SBP also considers various other macroeconomic factors, like exchange rate smoothing, etc., while conducting its monetary policy. Following this approach, we initially included these factors into (51), but due to identification issues we again restricted with the simple version, as describes above. 2.6. General Equilibrium Using the above model setup, we can drive general equilibrium dynamics around their steadystate level. The general equilibrium is achieved from goods market equilibrium and labour market equilibrium. The goods market equilibrium derived from aggregate demand side forces and labour market equilibrium dynamics emerge from aggregate supply side forces. So, the general equilibrium of the whole model is achieved from these market equilibriums. 2.6.1. Aggregate Demand Side: Goods Market Equilibrium and ISCurve To find goods market equilibrium, output is equating with domestic consumption, government investment and foreign consumption of domestic produced goods. Hence, market clearing condition is; [Y.sub.H,t] = [C.sub.H,t] + [Y.sup.*.sub.H,t] ... ... ... (52) Since, [C.sub.H,t] = (1  [alpha]) [([P.sub.H,t]/[P.sub.t]).sup.[eta]] and [C.sup.*.sub.H,t]  (1  [alpha])[([e.sub.t] [P.sub.H,t]/[P.sup.*.sub.t]).sup.[eta]] [C.sup.*.sub.t], the loglinear form of this setup is given as: [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (53) Final representation after solving (53) simultaneously as: [[??].sub.H,T] = [alpha][eta][[psi].sub.t] + (1  [alpha]) {[c.sub.t] + [alpha][eta][s.sub.t]} + [alpha]{[eta]([s.sub.t] + [psi].sub.t]) + [c.sup.*.sub.t]} ... ... ... (54) or [y.sub.t] = (2  [alpha])[alpha][eta][s.sub.t] + (1  [alpha])[c.sub.t] + [alpha][eta] [[psi].sub.t] + [alpha][y.sup.*.sub.t] ... ... ... (55) It should also be note that if we plug value of ct is equal to zero then this model converges to closed economy. 2.6.2. Aggregate Supply Side: Marginal Cost and Inflation Dynamics Since we already derived domestic firm's price setting behaviour in terms of NKPC in (29) as: [[pi].sub.H,t] = [beta](1  [[theta].sub.H]) [E.sub.t] {[[pi].sub.H,t+1]} + [[theta].sub.H] [[[pi].sub.H,t1] [[lambda].sub.H] [mc.sub.t] Where [[lambda].sub.H] = (1  [[theta].sub.H])(1  [beta][[theta].sub.H]) and real marginal cost is [m.sub.ct] = [W.sub.t]  [P.sub.H,t]  [[alpha].sub.t]. Assuming symmetrical equilibrium, real marginal cost can also be rewrite as: [m.sub.ct] = ([v.sub.t]  [p.sub.t]) + ([P.sub.t]  [P.sub.H,t])  [a.sub.t] ... ... ... (56) Using (13) and (39) the above expression can also be written as: [m.sub.ct] = [phi][n.sub.t] [alpha][s.sub.t] + [sigma]/1  h ([c.sub.t]  [hc.sub.t1])  [a.sub.t] ... ... ... (57) Since, simple loglinear representation of CobDouglas production function with one input (labour) is: [Y.sub.t] = [n.sub.t] + [a.sub.t] ... ... ... (58) Hence, the final representation of (57) is given as: [mc.sub.t] = [[phi][y.sub.t] + [alpha][s.sub.t] + [sigma]/1  h ([c.sub.t]  [hc.sub.t1])  (1 + [phi] [a.sub.t] ... ... ... (59) This model is finally solved using the general methodology provided in Klein (2000). This methodology also considered the autoregressive shocks as exogenous processes. The detail list of endogenous variables and exogenous processes are described in Appendix Table B 1 of AppendixB. 3. THE EMPIRICAL ANALYSIS This section briefly outlines the empirical setup by illustrating data, choice of priors and estimation methodology used in this paper. 3.1. Data To estimate the model parameters, data over the quarterly frequencies from 1984:Q1 to 2007:Q4 (post floating period) is used on eight macroeconomic variables: domestic output ([y.sub.t]); foreign output ([y.sup.*.sub.t]); domestic overall inflation ([[pi].su.t]); imported inflation ([[pi].sub.F,t]);domestic interest rate ([r.sub.t]); foreign real interest rate ([r.sup.*.sub.t]); real exchange rate ([q.sub.t]); and term of trade ([s.sub.t]). Since the model has implications for the logdeviations from the steadystate of all these variables, so we preprocess the data before the estimation stage. Details on the construction and the sources of the data set are provided in AppendixA. Pair wise correlation matrix of above mentioned variables is also available in Table A2 of AppendixA. These correlations are consistent with the standard theory. 3.2. Choice of Priors According to the Schorfiede (2000), priors can be gleaned from personal introspection to reflect strongly held beliefs about the validity of economic theories. Priors also reflect researcher confidence about the likely location of structural parameter of the model. In practice, priors are chosen based on observation, facts and from existing empirical literature. For our study, two parameters [alpha] and [beta] fixed (19) at 0.35 and 0.95. For parameter [alpha] (degree of openness) which is consistent with the average trade to GDP ratio during the sample period. This parameter value can also be depict from Figure A3 of AppendixA. The parameter value of discount factor ([beta]) is set in order to obtain historical mean of the nominal interest rate in the steady state. The degree of habit persistence (h) in consumption is set as 0.5 with standard deviation equal to 0.2. As usual in the literature, the inverse elasticity of intermporal substitution in consumption ([sigma]) assumed to follow normal distribution with prior means 1.0 and standard deviations equal to 0.4. The elasticity of intratemporal substitution between a bundle of home goods ([eta]) and the inverse of wage elasticity of labour supply ([phi]) are assumed to follow gamma distributions with prior means 1.0 and standard deviations equal to 0.4. See for instance, Smets and Wouters (2003). Following Ireland (2004) and Lubik and Schorfiede (2005) the parameters measuring the degree of Clavo price stickiness ([[theta].sub.H] ) and ([[theta].sub.F] ) are assumed to have the same mean value equal to 0.50 with standard deviation equal to 0.25. (20) In the case of Pakistan, the average frequency of price change of various commodities and average prices (CPI) fall within the interval from 0.45 to 0.55 as shown in the Figures Al and A2 of AppendixA. So the prior value of ([[theta].sub.H] ) is also consistent with the Pakistan's data. The priors on the coefficients in the monetary policy reaction functions are standard: a relatively high prior mean on the inflation coefficient ([[phi].sub.1]) with mean 1.5 and standard deviation equal to 0.25 and slightly low output growth coefficient ([[theta].sub.2]) with mean 025 and standard deviation equal to 0.10. The persistence coefficient domestic and foreign monetary policy reaction function is set to 0.5 with standard deviation equal to 0.20. Finally all other priors mean values with their standard deviations are available in first column of Table B3 in AppendixB. 3.3. Bayesian Estimation Approach In empirical literature, there are numerous strategies used to determine the parameters of newKeynesian DSGE models. These ranging from pure calibration, e.g., Kydland and Prescott (1982), Monacelli (2005); over generalised method of moments (GMM) for estimation of general equilibrium relationships, e.g., Christiano and Eichenbaum (1992); to fullinformation based maximum likelihood estimation as in Altug(1989), Mcgrattan (1994), Leeper and Sims (1994), Kim (2000) and Irland (2000). Other studies also proposed mixed strategies like limitedinformation based methods to explore a key question whether a DSGE model matches the data with some certain dimensions. For example, Canova (2002) and Christiano, et al. (2005) used minimum distance based criterion to estimate VAR and DSGE model impulse response functions. Further methodological debate can be referred using the following studies by Diebold (1998), RugeMurcia (2003) and Tovar (2008). Other than these proposed estimation and calibration strategies, this study uses another estimation approach called Bayesian estimation approach. This alternative approach is a combination of calibration and estimation of selected model parameters. The fundamental advantage of this approach is a batter adaption of the model to the conditions in the given economy, see e.g., Smets and Wouters (2003). In any empirical modeling exercise, there are three possible sources of uncertainty; the model itself; the parameterisation condition of the model and the data. The debate on the issue of uncertainty is the most important as it provide a difference between frequentist (classical) and Bayesian approach. In classical approach the probability of the occurrence of an event, i.e., the measurement of uncertainty is associated with its frequency. However, in Bayesian approach, the probability of an event is determined by two components; the subjective believe (prior) and the frequency of that event. For further detail on this notion, see for instance Gelman (2006) and Koopman, et al. (2007). The seminal work on DSGE modeling used this approach started with the study by LandonLane (1998), DeJong, et al. (2000), Schorfheide (2000) and Otrok (2001). This approach has been generalised by Lubik and Schorfheide (2005) who estimate a DSGE model without providing restrictions to the determinacy region of the parameter space. Almost all recent studies on DSGE model has been used this approach, e.g., Smets and Wouters (2003), Laforte (2004), Onatski and Williams (2004), Ratto, et al. (2008), Adolfson, et al. (2008) and Kolasa (2008). In practical sense, we try to fit out referenced model, which consists in placing a prior distribution [rho]([GAMMA]) on structural parameters F, the estimate of which are then updated using the data [Y.sup.T] according to the Bayes rule: p([GAMMA]/[Y.sub.T]) = p([Y.sub.T]/[GAMMA])/p([Y.sub.T])[varies] L([GAMMA]/[Y.sup.T])p([GAMMA]) ... ... ... (60) Where p([Y.sup.T]/[GAMMA]) = L([GAMMA]/[Y.sup.T]) is the likelihood function p ([GAMMA]/[Y.sup.T]) is the posterior distribution of parameters and p([Y.sup.T]) is the marginal likelihood defined as: p([GAMMA]/[Y.sub.T]) = [integral] p ([Y.sub.T]/[GAMMA])p([GAMMA])d[GAMMA] ... ... ... (61) Any DSGE model forms a linear system with rational expectations, the solution to which is of the form: [R.sub.t] = [B.sub.1]([GAMMA])[R.sub.t1] + [B.sub.2]([GAMMA]) [[mu].sub.t] ... ... ... (62) [[mu].sub.t] = [B.sub.3]([GAMMA])[[mu].sub.t1] + [B.sub.4]([GAMMA]) [[epsilon].sub.t ... ... ... (63) Where [R.sub.t] is a vector of endogenous variables, [[mu].sub.t] is a vector of stochastic disturbances and a, is a vector of innovations to stochastic shocks and coefficient matrices Ai depending on the parameters of the model. The measurement Equations (62) and (63) linking observable variables used in the estimation with endogenous variables can be written as: [Y.sub.T] = [CR.sub.t] ... ... ... (64) Where, C is the deterministic matrix. The Equations (62), (63) and (64) form the statespace representation of the model. The likelihood of which can be evaluated using Kalman filter. The analytical solution of the whole system may not be obtain in general, however the sequence of posterior draws can be obtain using MarkovChainMonteCarlo (MCMC) simulation methodology. This methodology is briefly discussed in Lubik and Schorfheide (2005), Gelman, et al. (2006) and Koopman, et al. (2007). For our open economy DSGE model the random walk MetropolisHastings algorithm is used to generate MorkovChains (MC) for the model parameters. 3.4. Fitness and Stability of Model Structural Parameter Following Global Sensitivity Analysis (GSA) toolkit, (21) we assess the fitness and stability of model structural parameters and structural shocks. This toolkit consists of MATLAB programme routines, which used Smirnovtest for stability analysis. Ratto (2008) provided detail discussion on using this toolkit with various applied examples. 4. ESTIMATION RESULTS In this section the estimation results from the small open economy DSGE model are discussed. First we shell analyse the parameter estimates and then we shell discuss model impulse response functions with all their possible dynamics. 4.1. Parameter Estimates In line with Bayesian estimation approach by combining the suitable priors with the likelihood leads to an analyticallyintractable posterior density. In order to sample from the posterior, random walk MetropolisHastings algorithm is used to generate 150,000 draws from the posteriors. We reported the posterior results (parameter estimates) in the second column of Table B3 of AppendixB. Furthermore, Figure B 1 of AppendixB displays kernel estimates of the priors and the posteriors of each parameter. These results show that prior and posterior means are in most the cases considerably away from each other. The parameter (h) is equal to 0.36 which is a bit lower than its prior mean of 0.5. This parameter value implies that degree of habit persistence in consumption is quite low as compared with advance economies; see for instance, Lubik and Schorfeide (2005). The parameter estimates of inverse elasticity of intermporal substitution in consumption ([sigma]), the elasticity of intratemporal substitution between a bundle of home goods ([eta]) and the inverse of wage elasticity of labour supply ([phi]) are 0.84, 1.01, 0.98 respectively. It should also be noted that high value of ([sigma]) show that household are more willingness to accept deviation from a uniformed pattern of consumption over time. This high value of inverse elasticity of intermporal substitution in consumption is also consistent with the low value of habit persistence as noted above. These parameter values are not apart from their prior means. The posterior estimates of Calvo price stickiness provide reasonable notion about frequencies of price change which is the probability of not changing price in a given quarters. The estimated values of ([[theta].sub.H]) and ([[theta].sub.F]) are 0.24 and 0.76 respectively, which shows the proportion of firms that do not reoptimise their prices in a given quarters. Furthermore, comparatively lower value of ([[theta].sub.H] ) shows domestic firms reoptimise their prices in a given quarters frequently. These staggered price coefficients imply that the average duration of price contracts is around two quarters for domestic firms and three to four quarters for import retailers. This duration is calculated as: 1/(1[theta]). These results are also consistent with da Silveira (2006) in the case of Brazil (emerging market economy) and Smets and Wouters (2003) in the case of US. The posterior estimates of Central Bank reaction function provide a reasonable description of monetary policy design in Pakistan during the sample period. The posterior estimate of inflation coefficient ([[phi].sub.1]) is 1.17 which is slightly low from its prior mean and output growth coefficient ([[phi].sub.2]) is 0.72 which is above from its prior mean. This also shows that policymaker in Pakistan put more weight on growth objectives as compared with other developing economies. A recent empirical study by Malik and Ahmed (2007) argued that coefficient values (weights) as suggested by Taylor (1993) are not suitable for Pakistan's monetary policy reaction function. However, our estimated values of monetary policy reaction function are approximately closed to Taylor rule. Finally, the posterior mean for the degree of interest rate smoothing is estimated to be 0.94 which is quite high degree of smoothness as compare with its prior mean. The overall results of reaction function show the effectiveness of monetary policy design in Pakistan with price stability as its primary objective consistent with the economic growth objectives. Finally all posterior estimates with their 95 percent confidence interval are available in second column of Table B3 in AppendixB. 4.2. Parameter Fitness and Stability Results Parameter's stability and fitness results are provided in FigureB2 of AppendixB. The dstat of Smirnovtest is also provided for each parameter, which shows the significance of for individual parameter into the whole model. Furthermore, cumulative plots for stability and instability behaviour provide us useful information for the fitness of each structural parameter. Figure B2 shows that all model parameters are stable and properly fitter with respect to the data. Similar to structural parameters we also assessed the fitness of structural shocks. The dstat results visavis cumulative plots show that all structural shocks are fitted but with some degree of instability. This might be due to some degree of seasonality which still exists in the quarterly constructed data. 4.3. Impulse Response Analysis Figure B3 of AppendixB shows the impulse response functions for model endogenous variables in response to the various structural shocks. (22) These impulse response functions provide several interesting results. First figure plots the impulse response to positive domestic labour productivity shock. Following this shock, domestic output initially increases up to two quarters and decrease slightly before staying above trend until eight quarters later. The later decrease in output shows that agent's substitution between working and leisure dominates the lower cost of production that arises from the increase in productivity. Secondly consumption falls initially up to one quarter then increases but increment is steady and almost around its steady path. Inflation on the other hand falls initially as the higher labour productivity supports to minimise the cost of production before returning close to steady state eight quarters later. (23) All other variables fall initially and returning close to zero up to four to six quarters later. Second figure plots the impulse response to a positive domestic inflation shock. (24) Following this shock, domestic output initially fall, up to two quarters and then returning close to its steady state four to six quarters later. Secondly consumption also falls initially up to one quarter but its decline magnitude is relatively less as compared with domestic output. This also shows that high inflation in Pakistan do not hit domestic consumption significantly. Thirdly, positive shock in domestic inflation decreases the degree of domestic competitiveness. Furthermore, the central bank of Pakistan responds to the higher rate of inflation by increasing the interest rate by 100 to 200 basis points. In response to this monetary tightening domestic output decreasing up to one to two quarters but this decline impact is very nominal. Exchange rate on the other hand appreciates in response to positive domestic supply shock. Third figure plots the impulse response to a positive imported inflation shock. The impact of this shock on the model endogenous variables is quite different as compared with domestic inflation or supply shock. In response to this shock domestic inflation increases, as higher import prices pushing up the cost of production causes as a surge in domestic inflation. Term of trade increases as foreign prices increases relative to domestic prices. The economic interpretation of this surge in the degree of competiveness is that domestic agents substitute out of foreign produced goods into home produced goods in response to import inflation shock which causes expenditure switching effect and hence leads to a surge in domestic terms of trade. The central bank of Pakistan responds to the higher rate of imported inflation by increasing the interest rate by 150 to 250 basis points as compared with domestic inflation case. This also leads an exchange rate appreciation but this appreciation is higher than in the case of domestic inflation. Forth figure plots the impulse response to a positive interest rate shock which also considered as a domestic monetary policy shock. Following the increase in the interest rate, domestic inflation, imported inflation, degree of international competitiveness and domestic output decreases; exchange rate appreciates before returning to equilibrium. Consumption on the other hand increases by one percent and returning close to its steady state up to four to six quarters. These results reasonably capture the effectiveness of monetary policy as it shows to achieve its basic objectives, with some nominal tradeoffs, in terms of output decline and exchange rate appreciation. Furthermore, due to continuous domestic supply and foreign price shocks there needs to further tightening of monetary is order to curb these frights. Fifth figure plots the impulse response to a positive exchange rate shock. This shock transmits from uncovered interest parity condition (25) to rest of the model. In response to this shock domestic inflation, output, interest rate decreases but the decrement impact in all the variables is very nominal. For monetary policy perspective, interest rates decline by 50 basis points. This also indicates a monetary expansion in the case of surge in UIP condition. (26) Lastly, this shock decreases the degree of international competitiveness and increases consumption up to six and two percent respectively. Sixth figure plots the impulse response to a positive term of trade shock. Following this shock, all variables show a minor surge except imported inflation which shows a decline behaviour and return to zero up to four quarters later. This shock also causes an exchange rate appreciation. Lastly for monetary policy perspective, interest rate shows a positive response to this shock up to 10 basis points and then returns to its equilibrium path up to two quarters later. This small monetary tightening helps to offset the adverse impact in term of domestic inflation and exchange rate appreciation. Final two figures show impulse responses to a positive foreign output shock and foreign monetary policy shock. Due to these positive shocks, all domestic endogenous variables behave according to the theory. This also represents the effectiveness of model, which is quite useful for policy decision making. 5. CONCLUSION In this paper, we estimate a small open economy DSGE model for Pakistan. The model setup is based on new Keynesian framework characterised by nominal rigidity in prices with habit formation in household's consumption. This framework allows us to include microeconomic foundations of optimum behaviour of the economic agents; domestic households, domestic firms, monetary authority and foreign sector economy, into the system. It is also considered that the foreign sector is completely exogenous to the system. In our empirical section, some parameters has been calibrated, e.g., degree of openness, discount factor, inverse elasticity of intertemporal substitution; the remaining parameters has been estimated using the Bayesian simulation approach, which combines prior information from preliminary estimates and from historical data covering period 1984:Q1 to 2007:Q4. The model ability to describe the dynamic structure of Pakistan economy has been analysed by means of impulseresponse functions. The estimation results of structural parameters and model impulse response functions yield useful quantitative visavis qualitative information. The exogenous shocks impact on endogenous system variables in the right direction, so that the model seems to be helpful as a complementary tool for monetary policy analysis in the Pakistan economy. From several interesting results, few are; (a) high inflation in Pakistan do not hit domestic consumption significantly; (b) Central bank of Pakistan responds to" high inflation by increasing the policy rate by 100 to 200 bps; (c) exchange rate appreciates in both the cases of high domestic and imported inflation; (d) tight monetary policy stance helps to curb domestic inflation as well as imported inflation but appreciates exchange rate significantly (f) pass through of exchange rate to domestic inflation is very low; finally parameter value of domestic price stickiness shows that around 24 percent domestic firms do not reoptimise their prices which implies averaged price contract is about two quarters. Finally, this model is still in progress. After relaxing some key assumptions and incorporating fiscalside dynamics, this model will be more robust for policy decision making and future forecasting of key macroeconomic variables. APPENDIXA [FIGURE A1 OMITTED] [FIGURE A2 OMITTED] [FIGURE A3 OMITTED] [FIGURE A4 OMITTED] APPENDIXB B1. LogLinearisation and canonical representation of the model This section proceeds by a model solution methodology with the loglinearisation and canonical representation of the model along with its foreign sector economy, (27) In order to solve the model, we first state the first order nonlinear dynamic system that characterises the competitive equilibrium. In order to calculate the steady state we transform the system equations into their deterministic steady state representation and solve using numerical methods. Then we loglinearise around the deterministic steady state where [[??].sub.t] =log([x.sub.t])  log([bar.x]). At this stage the system is expressed in terms of relative deviations from the steady state. After solving the model using the method of Klein (2000) (28) we obtain matrices M and H which generate the dynamic solution by iterating on the following two equations: [Y.sub.t] = H[x.sub.t] ... ... ... (b1) [x.sub.t+1] = M[x.sub.t] + R[[eta].sub.t+1 ... ... ... (b2) Where Y is a vector composed by control, costate and flow variables, x is a vector of endogenous and exogenous states, H characterises the policy function and M the state transition matrix, [[eta].sub.t+1] is an innovation vector and R is a matrix composed of zeros, ones or a parameter instead of a one. This matrix determines which variables are hit by the shock and in what magnitude. Given a set of values of the parameters of the model, this state space representation will help us to compute the relevant statistics of the model such as the spectrum of the data, the likelihood function, among others. The small open economy model consists of eleven equations for endogenous variables and three equations for the exogenous processes. The canonical representation of the whole model in loglinearised form is available in Table B2. [FIGURE B1 OMITTED] [FIGURE B2 OMITTED] [FIGURE B3 OMITTED] [FIGURE B4 OMITTED] [FIGURE B5 OMITTED] APPENDIXC Authors' Note: Views expressed here are those of the authors and not necessarily of the State Bank of Pakistan or Bond University, Australia. Any errors or omissions in this paper are the responsibility of the authors. The authors are grateful to M. Ali Choudhary for his insightful comments on the earlier draft of this paper. They are also thankful to Macro Ratto, Martin Melecky, Nikolay Iskrev, Philip Liu, Rafael Wouters and Zulfiqar Hyder for their support, guidance and helpful discussions. REFERENCES Adolfson, M., S. Laseen, J. Linde, and M. Villani (2007a) RAMSES: A New General Equilibrium Model for Monetary Policy Analysis. Risksbank. 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Journal of Monetary Economics 37,345370. Adnan Haider (1) Well known DSGE models developed by most of ,the central banks and international policy institutions as noted by Tovar (2008) are (a) Bank of Canada ('TotEM), (b) Bank of England (BEQM), (c) Central bank of Brazil (SAMBA), (d) Central bank of Chile (MAS), (e) Central bank of Peru (MEGAD), (f) European Central bank (NAWM), (g) Norges Bank (NEMO), (h) S veriges Riksbank (RAMSES), (i) US Federal Reserve (SIGMA) and (j) IMF (GEM and GIMF). A bird's eye vie w of various country specific DSGE models is also provided in Table C 1 of AppendixC. (2) For recent contributions that estimate small open economies, see Adolfson, et al. (2008), Dib, et al. (2008), Justiniano and Preston (2004), Liu (2006) and Lubik and Schortheide (2005). (3) In macroeconomic literature, the terms "newKeynesian" or "new neoclassical synthesis" are being used synonymously; see, Clarida, Gali and Getler (1999), Gali and Getler (2007), Goodfriend (2007), Goodfriend and King (1997), Mankiw (2006) and Romer (1993). (4) Lucas (1976) and Lucas and Sargent (1979 argue that if private agents behave according to a dynamic optimisation approach and use available information rationally, they should respond to economic policy announcements by adjusting their supposedly behavior. Hence reduced form parameter results are subject to Lucas critique. But, DSGE models are based on optimising agents; deep parameters of these models are therefore less susceptible to this critique. (5) Christanio, et al. (2005) and Smets and Wouters (2003) argued that endogenous persistence mechanism, such as habit formation and price indexation, must be added to the basic DSGE model in order to reproduced the observed output and inflation persistence. (6) See, for instance; Kwapil, et al. (2005), Copaciu, et al. (2005), and Bosch (2007). (7) For detail description, see Table AI of Appendix A. (8) Using Global Sensitivity Analysis (GSA) toolkit, we computed model parameter stability estimates, which are also provided in the AppendixB of this paper. (9) Each household lives in one of two countries, individual defined on the interval, i [member of] [0, n] lives in the homecountry, and remaining on the interval i [member of] [0, n] lives in the foreigncountry. The value of n measures the relative size of the homecountry. (10) We do not include real money balances (M/P) into our utility function. Because DSGE models assume nominal shortterm interest rate as the monetary policy instrument, so that money supply is considered as endogenous; see for instance, Woordford (2003). In the case of Pakistan, this critical assumption also holds as a recent empirical study by Omer and Saqib (2008) argue that money supply in Pakistan is endogenous. (11) It also shows habit persistence parameter to reproduce observed output, rages from 0 [less than or equal to] h [less than or equal to] 1. (12) In terms of this discount factor, the riskless short term nominal interest rate [R.sub.t] corresponds to the solution to the equation: 1/[R.sub.t] = [E.sub.t] ([Q.sub.t t+1]). (13) [Q.sub.t t+1] remains a stochastic variable at time t, and [E.sub.t] denotes expectations conditional upon the state of the world at time t. (14) To drive, FOCS from objective function subject to budget constraint, it is assumed that inverse of wage elasticity of labour supply is zero. (15) [[theta].sub.t], firms adjust prices according to steady state inflation rate [[pi].sub.t]. This notion introduces inflation persistence by allowing for price indexation to previous inflation. (16) The degree of price stickiness is assumed to be same as the fraction of past inflation indexation. The reason of this crude assumption is that it validates a basic rationale of Phillips curve. "In the longrun Phillips Curve is vertical", see for instance, Gali and Gertler (1999). (17) In the limiting case with no price rigidities (say, [theta] = 0), the previous condition collapses to the familiar optimal pricesetting condition under flexible prices. See., Gali (2008). (18) If PPP holds, then l.o.p gap is translated into [[psi].sub.F,t] = 1. This implies that passthrough from exchange rate movements to the domestic currency prices of imports is imperfect as importers adjust their pricing behaviour to extract optimal revenue from consumers. See, Monacelli (2005). (19) These fixed parameters are also known as stick priors in Bayesian sense. (20) For US economy price stickiness parameter value is also taken as 0.5, see for instance Lubik and Schorfiede (2005). (21) http://eemc.jrc.ec.europa.eu//softwareDYNAREDowload.htm (22) The impulse responses to a one unit increase in the various structural shocks are calculated using 10,000 random draws from the posterior distribution of the model parameters. Initially we draw posterior distributions using 1.5 million Markov chains. But for impulse responses we use only limited random draws due to computational complexity. (23) In this case, the monetary authority can afford to loosen monetary policy to bring inflation back to zero. (24) As inflation dynamics modeled with the New Keynesian Philips Curve, so this shock is also considered as a supply shock. (25) Adolfson, et al. (2008) noted that the uncovered interest rate parity (UIP) condition is a key equation in open economy DSGE models. It shows the difference between domestic and foreign nominal interest rates equals the expected future change in the nominal exchange rate. The UIP condition is a key equation in open economy models not only for the exchange rate but also for many macroeconomic variables, since there is a lot of internal propagation of exchange rate movements working through fluctuating relative prices. There is, however, strong empirical evidence against the standard UIP condition, see for instance, e.g., Eichenbaum and Evans, (1995); Faust and Rogers, (2003). Moreover, a DSGE model with a standard UIP condition cannot account for the socalled 'forward premium puzzle' recorded in the data, i.e. that a currency whose interest rate is high tends to appreciate which implies that the risk premium must be negatively correlated with the expected exchange rate depreciation see, e.g., Fama, (1984); Froot and Frankel (1989). (26) Figure A4 of AppendixA, plots the residuals of uncovered interest rate parity (UIP) condition generated from Pakistan's data by utilising theory based and regression based methodologies, see, Lubik and Schorfeide (2005) for further detail. This figure also provides a historical description of monetary expansion and tightness in the case of surge and decline in UIP. The recent negative values of UIP show the tight monetary policy stance which is in line with the standard macroeconomic theory. (27) The foreign sector economy consists of two main Equations; (a) output and (b) real interest rate as a proxy of foreign monetary policy instrument. This sector is assumed to be completely exogenous to the small open economy, Pakistan. (28) Any other method can also be used to solve the loglinear approximation to the rational expectations solution, e.g., Sims (2002). Table A1 Description of Variables S. No Variable * Description / Source 1 [y.sub.t] Quarterly real GDP per capita as a proxy of domestic output. We follow Kemal and Arby (2004) to construct this series. We initially convert original series into new base (Year 2000=100). Since it is an interpolated series from annual frequency data, so we also perform necessary seasonal adjustments using moving average methodology. Finally, for stationarity purpose we detrend this series from its linear trend. ** 2 [[pi].sub.t] Overall domestic inflation. This series is the annual growth rates in consumer price index (CPI) for Pakistan. Data source of this variable is FBS, Islamabad, Pakistan. 3 [[pi].sub.F,t] Imported Inflation as a proxy of foreign inflation. This series is the annual growth rates in unit value of import index (UVIM). This series is taken from IFSCD June 2008 version. 4 [q.sub.t] Real exchange rate. This series is calculated by multiplying nominal exchange rate with Pak US price ratios where CPI of both countries is a suitable proxy of respected prices. Data source of this variable is IFSCD June 2008 version. 5 [r.sub.t] Nominal interest rate. Short term money market rate is taken as the proxy of nominal interest rate. Data source of this variable is Statistical Bulletins of the State Bank of Pakistan. 6 [s.sub.t] Term of Trade (ToT). This series is calculated by taking the ratio of the unit value of import index (UVIM) and unit value of export index (UVEX). Data source of this series is IFSCD June 2008 version. 7 [y.sup. *.sub.t] Foreign Output. The series is taken as annual growth rate in U.S. real GDP per capita. This is obtained from IFSCD June 2008 version. 8 [r.sup. *.sub.t] Foreign real interest rates. This series is calculated by subtracting nominal US money market rates from expected inflation. Data source of this variable is IFSCD June 2008 version. * For stationary purpose, all series are converted into detrended form. This is done by subtracting each series from its linear trend. ** Detrended output is also considered as a proxy of output gap, see for instance, Bukhari and khan (2008). Table A2 Pairwise Correlation Matrix [y.sub.t] [y.sup.*.sub.t] [[pi].sub.t] [y.sub.t] 1.00 [y.sup.*.sub.t] 0.23 1.00 [[pi].sub.t] 0.05 0.18 1.00 [[pi].sub.F,t] 0.05 0.28 0.08 [r.sub.t] 0.28 0.12 0.11 [r.sup.*.sub.t] 0.16 0.06 0.05 [q.sub.t] 0.21 0.31 0.75 [s.sub.t] 0.06 0.02 0.24 [[pi].sub.F,t] [r.sub.t] [r.sup.*.sub.t] [y.sub.t] [y.sup.*.sub.t] [[pi].sub.t] [[pi].sub.F,t] 1.00 [r.sub.t] 0.13 1.00 [r.sup.*.sub.t] 0.08 0.58 1.00 [q.sub.t] 0.07 0.17 0.10 [s.sub.t] 0.28 0.46 0.49 [q.sub.t] [s.sub.t] [y.sub.t] [y.sup.*.sub.t] [[pi].sub.t] [[pi].sub.F,t] [r.sub.t] [r.sup.*.sub.t] [q.sub.t] 1.00 [s.sub.t] 0.21 1.00 Table B1 Description of Model Endogenous and Exogenous Variables 1. List of endogenous variables: {[y.sub.t]; [y.sup.*.sub.t]; [[pi].sub.t]; [[pi].sub.F,t]; [r.sub.t]; [r.sup.*.sub.t]; [q.sub.t]} 2. List of endogenous state variables: {[psi].sub.t]; [c.sub.t]; [mc.sub.t]; [[pi].sub.H,t]; [s.sub.t]} 3. List of model endogenous innovations [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] 4. List of model exogenous shocks: [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] Table B2 Canonical Representation of the Model S. No. Description Model LogLinearised Equation(s) 1. Goods Market [y.sub.t] = (2  [alpha]) Clearing [alpha][eta][s.sub.t] Condition 2. Firm Marginal [mc.sub.t] = [phi][y.sub.t] + [alpha] Cost [s.sub.t] + [sigma]/1h ([c.sub.t]  [hc.sub.ta])  (1 + [phi]) [[alpha].sub.t] 3. Domestic Inflation [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] 4. Imported Inflation [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] 5. Overall Inflation [[pi].sub.t] = [(1  [alpha]) [[pi].sub.H,t] + [alpha][[pi].sub.F,t]] 6. Monetary Policy [r.sub.t] = [[rho].sub.r][r.sub.t1] + Reaction Function (1  [[rho].sub.r]) ([[phi].sub.l] [[??].sub.t] + [[phi].sub.2][DELTA] [[??].sub.t]) + [[rho].sup.r.sub.t] 7. Uncovered [E.sub.t][DELTA][q.sub.t+1] =  Interest Parity ([r.sub.t]  [[pi].sub.t+1])  Condition ([r.sup.*.sub.t]  [[pi].sup.*.sub.t+1]) + [[rho].sup.q.sub.t] 8. Term of Trade [s.sub.t]=[s.sub.t1] + [[??].sub.F,t]  with Measurement [[??].sub.H,t] + [[rho].sup.s.sub.t] Error 9. Law of One Price [[??].sub.t] = [q.sub.t]  Gap (1  [alpha]) [s.sub.t]) 10. Consumption [E.sub.t] ([c.sub.t+1]  [hc.sub.t])  Euler Equation (1h/[sigma]) ([r.sub.t]  [E.sub.t][[pi].sub.t+1]) = [c.sub.t]  [hc.sub.t1] 11. International Risk [y.sup.*.sub.t]  [hy.sup.*.sub.t1]  Sharing Condition (1h/[sigma]) [q.sub.t] = [c.sub.t]  [hc.sub.t1] 12. Exogenous [MATHEMATICAL EXPRESSION NOT Processes REPRODUCIBLE IN ASCII] * Table Key: All exogenous processes follow recursive equilibrium law of motion. Table B3 Model Prior and Posterior Distribution Results Prior Distributions Parameters Distribution Mean Std_Dev alpha beta 0.35 0.20 H beta 0.50 0.20 sigma normal 1.00 0.40 eta gamma 1.00 0.40 phi gamma 1.00 0.40 thetah beta 0.50 0.25 thetaf beta 0.50 0.25 phi1 gamma 1.50 0.25 phi2 gamma 0.25 0.10 rhor beta 0.50 0.20 rhorst beta 0.50 0.20 rhoa beta 0.50 0.20 laml beta 0.50 0.20 sig_a normal 2.00 0.50 sig_s normal 2.00 0.50 sig_q normal 2.00 0.50 sig_pi normal 2.00 0.25 sig_pif normal 1.00 0.20 sig_r normal 1.00 0.20 sig_rst normal 0.50 0.20 sig_yst normal 1.00 0.20 Posterior Distribution Distribution Mean 5% Percentile 95% Percentile beta 0.23 0.19 0.24 beta 0.36 0.33 0.37 normal 0.84 0.80 0.86 gamma 1.01 1.00 1.08 gamma 0.98 0.91 1.04 beta 0.24 0.21 0.36 beta 0.76 0.68 0.82 gamma 1.17 1.10 1.23 gamma 0.72 0.65 0.78 beta 0.94 0.87 1.00 beta 0.43 0.36 0.49 beta 0.51 0.44 0.57 beta 0.36 0.29 0.42 normal 2.04 1.98 2.11 normal 1.92 1.86 1.99 normal 2.04 1.98 2.11 normal 2.02 1.96 2.09 normal 1.62 1.56 1.69 normal 1.28 1.22 1.35 normal 0.50 0.44 0.57 normal 1.63 1.57 1.70 Table Key: (a/) The posterior mean of all the estimation parameters are delivered by a 150,000 runs of MetropolisHastings algorithm. (b/) We use two MATLAB toolboxes; Dynare 4.0 and Uhlig toolkit version 4.1 to estimate our model. Both toolkits 19 are freely available on internet. (29) (c/) The parameter beta which is discount factor is fixed at 0.95. Table C1 A Quick View of Empirical Evidence on DSGE Model Country Authors Authors Model Description Canada Dib. This study develops Gammoudi on the basis of New and Moran Keynesian model for (2008) Canada. This model in particular computes out of sample forecasts and compares its forecasts with those arising from VAR models. It shows that the forecasts are favorably valid with that of the benchmark, particularly as the forecasting horizon increases. Thus the study deduces that the model could become a useful forecasting tool for Canadian economy. Central Europe Sadeq, T. This paper uses a Transition (2008) small open economy Economies DSGE model for central Europe Transition economies, EU15: Czech Republic, Hungary, Poland, Slovakia and Slovenia. The objective is to analyse the general model convergence issues. Poland Kolasa, M. This paper presents a (2008) twocountry model linking Poland and the euro area Australia Buncic and This paper provides an Melecky open economy New (2008) Keynesian policy model for Australian economy. It focuses to observe the importance of external shocks on macroeconomic fluctuations as compared to the impact of domestic shocks. United Kingdom DiCecio and This study replicates Nelson the DSGE model of (2007) Christiano, Eichenbaum and Evans (2005), in which both the nominal frictions and dynamics in preferences and productions are incorporated. LowIncome Peiries and This paper presents Countries Saxegard DSGE model to (2007) evaluate monetary policy tradeoffs in low income countries under certain assumptions. The model is estimated on data for Mozambique in sub Sahara Africa except South Africa. New Zealand Liu (2006) This study designs DSGE based New Keynesian framework to describe the key features of a small open economy. Particularly the model focuses on the transmission mechanism of monetary policy to provide a tool for basic policy simulations. This model, however, shows the capacity to simulate the monetary paths and to analyse the policy outcome in uncertainty. Brazil da Silveria, This paper presents a M.A.C. small open economy (2006) DSGE model for Barazilian economy with special reference to monetary policy analysis. A distinctive feature of the model is that the terms of trade enters directly into the new Keynesian Phillips curve as a new pushing cost variable feeding theinflation, so that there is no more the direct relationship between marginal cost and output gapthat characterises the closed economies. Chile Medina and This study presents Soto (2006) DSGE model for policy analysis and simulations. The main characteristics of this model are: wages and prices are sticky with adjustment costs in investment and habit persistence in consumption behavior; exchange rate pass through to import prices is imperfect. On the supply side a domestic sector where firms produce tradable goods and the commodity export sector. Colombia Hamman, This study develops Perez and DSGE model for small Rodriguez open economy of (2006) Colombia. This model take in to account two main sectors categorised as tradable and nontradable sectors with three agents; households, firms and government sector. Finally this model exhibits two features; first nominal rigidities in the form of Calve pricing in the non tradable sector and second perfect/imperfect pass through of exchange rate movements into imported goods prices. Latin America Tovar (2006) This study is focused on the analysis of effects of currency devaluations on output in Chile, Colombia and Mexico using an estimated DSGE model. This study also Provides comparison across these three economies by utilising the estimated parameters. Czech Republic Benes, This is a small open Hledik and economy DSGE model. Vavra (2005) The characteristics of this model are so broad with the innovative Benes, features. These are international currency pricing scheme permitting flexible calibration of import and export price elasticities along with the disconnect of nominal exchange rate. United States Negro, This paper presents the Euro Area Schorfheide, modified version of Smets and DSGE model for Euro Wouters Negro, area. This model (2005) introduces stochastic trends so that it can be fitted to unfiltered time series observations. It contains a large number of nominal and real frictions and various structural shocks. Euro Area Wouters and Authors develop the Smets (2003) DSGE model with stick prices and wages for the euro area. This model includes many other features such as habit formation, costs of adjustment in capital accumulation and the variable of capacity utilisation. Country Data Description Canada This study includes the sample of 1981:1 to 2004:4. Since the model is driven by four shocks thus it is estimated using data for four series. The variables are output in terms of real domestic demand, inflation, a short term interest rate and real money balances. Central Europe Quarterly data for Transition the sample range Economies 1996:2 to 2007:2 has been used for empirical analysis. Variables from each country is selected. These inlcude real GDP, household consumption, nominal wages, CPI Inflation, and nominal short term interest rates. Poland The sample period is 1997:1 to 2006:4. The model uses GDP growth, consumption, CPI inflation, real wages, investment, nominal exchange rates and interest rates variables. Australia For empirical purpose quarterly data has been used ranging from 1983/84:1 to 2005:4. Variables are foreign interest rate, the foreign inflation, foreign output gap, domestic interest rate, domestic inflation, domestic output gap, real exchange rate and nominal exchange rate series. United Kingdom The sample period is 1979:2 to 2005:4. Variables are UK treasury bill rate, real GDP, private household consumption, gross fixed capital formation, business investment as an alternative investment series, productivity and inflation. LowIncome This model is estimated Countries on quarterly data covering the period of 1996:1 to 2005:4. Variable are GDP, consumption, exports, imports, the real exchange rate, inflation, export price inflation, import price inflation, M2, currency in circulation, deposit rates, lending rates, foreign currency reserves, government spending, and lending to the private sector. New Zealand Data from 1991QI to 2004Q4 for New Zealand is used. Key variables are GDP, overall inflation, import inflation, nominal interest rate, competitive price index, real exchange rate, foreign output, and foreign real interest rate. Brazil This model is estimated on quarterly data of the Barzilian and U.S. economies for the periods from 1999 Q3 to 2005 Q3. Variables included real GDP, CPI Inflation, 3 month T. Bill rate, Real Exchange Rate as a proxy of short term interest rates, Term of Trade, U.S. real per capita GDP and U.S. CPI Inflation. Chile Quarterly data for the period of 1990: 1 to 2005: 4 has been used. Variables include real GDP, consumption, investment, exports; commodity production by using naturalresources based GDP as a proxy, short run real interest rates, a measure of core inflation as a proxy for inflation, the real exchange rate, nominal devaluation, and real wages. It also include real foreign GDP, foreign inflation weighted average of inflation in trade partners, foreign interest rate and the international price of copper deflated by the foreign price index. Colombia Quarterly data with the range of 1987:1 to 2005:4 has been used in estimation. The variables are inflation, nominal interest rate, and real output and exchange rate. These variables are transformed according to the characteristics of the model. Latin America Seasonally adjusted quarterly series have been used with the range from 1989:1 to 2005:4. The variables are inflation, output, labor, private consumption, changes of the nominal exchange rate, interest rate, and the level of nominal exchange rate. Czech Republic This paper uses quarterly data with the sample range 1996:1 to 2004:4 for Czech economy. The main variables are GDP, import prices, export prices, investment, labor, consumption expenditures, labor participation, wage rate, exchange rate, interest rate, and inflation. United States Quarterly data for the Euro Area sample range 1986:1 to 2002:4 has been used for empirical analysis. Variables are GDP per capita, investment, hourly nominal wages, GDP deflator, M2 per capita, and nominal short term interest rates. Euro Area The key variables used in this study are GDP, consumption, investment, prices, real wages, employment and the nominal interest. Country Estimating Technique Canada This studyuses slightly different estimation strategy as compared with others for estimating DSGE models. For example it points out that this estimation shows an advantage of estimating and forecasting for the log levels of the data, rather than forecasts for detrended series. The method of estimation is Maximum likelihood. It also describes about the impulse response drawn from the estimates. Central Europe This model is estimated by Transition utilising the Bayesian Economies techniques utilising information from the previous studies as priors. Poland This open economy DSGE framework is empirically evaluated through calibrations and estimated by the Bayesian approach utilising information from the previous studies as priors. Australia In the estimation section this study mentions different weaknesses of different methods to estimate this NKPM. Therefore, authors prefer to estimate this model in Bayesian framework. United Kingdom In the first stage authors estimate monetary policy shock from a VAR and then use minimum distance estimation procedures for estimating this DSGE model. LowIncome This DSGE framework is Countries empirically evaluated through calibrations and estimated by the Bayesian approach utilising information from the previous studies as priors. New Zealand Similar to many other empirical studies Liu (2006) estimates the DSGE for small open economy in Bayesian framework. This method provides comparison between nonnested models and parameter uncertainty explicitly. The Bayesian inferences are in terms of probabilistic statements rather than the notional repeated samples of classical hypothesis testine procedures. Brazil This small open economy DSGE framework is empirically evaluated through calibrations and estimated by the Bayesian approach utilising information from the previous studies as priors. Chile The Bayesian methodology is applied to jointly estimate the parameters of this DSGE model. This study takes into account the information of Priors from the earlier empirical studies for Chile, or imposes diffuse Priors by setting a relatively large standard deviation for the corresponding density function. By using the estimated Posteriors this study provides analysis of impulseresponse for a shock to the exported commodity good, foreign output and a monetary shock. Colombia In this study three methods are reviewed and used in estimating the DSGE model. These methods are Calibration, Minimum Distance Spectral Analysis and the Bayesian technique. Latin America This DSGE model is estimated by the Maximum Likelihood method. This study claims that this method is optimal in estimating DSGE model for small open economy. Estimation through this technique however creates problem of stochastic singularity. Therefore, additional shocks were created to address this problem. In the second stage estimation is done by introducing measurement errors. Czech Republic The empirical analysis of this DSGE model is presented in terms of calibration strategy and impulseresponse setup. United States This DSGE model is Euro Area estimated by applying the VAR framework. Euro Area This model is estimated by utilising the Bayesian techniques. As a part of the empirical strategy study quantifies the structural shocks and their contribution to business cycle fluctuations. Country Concluding Remarks Canada Through this aspect of model building study shows with sure that the out of sample forecasts are relatively more appealing than any other model in comparison. For some of the variables such as interest rate and output in fact have very good level of accuracy in forecasting. The forecasting power however for inflation is not so strong yet it is not significantly less than those of the benchmark VARs. In the last this study introduces several dimensions for improvements in the model for future work. Central Europe The estimation results of this Transition illustrate some differences Economies from the Euro area results in structural parameters. However, the results exhibit some similarities across countries, notably in some shocks volatilities and high habit formation of consumption. The results illustrate also an important degree of rigidity of imported goods prices, which implies a low passthrough of the exchange rate fluctuations. Finally, we study the Ramsey optimal allocation, in a timeless perspective, of the estimated model for each country in order to analyse the convergence criteria of entrance in the European exchange rate mechanism Poland Overall, results of this model can be seen as rather inconclusive about the differences in parameters describing agent's decision making in Poland and in the euro area. Australia The empirical estimates suggest that domestic and foreign demand shocks and to some extent the domestic supply shocks are the most influential in Australian business cycle. The effect of real exchange rate on output is somewhat mild. Inflation appears very sensitive to the domestic supply shocks. The impact of domestic monetary policy however on inflation is also mild. United Kingdom This study finds that the results are consistent to policy regime changes. These regime changes include shifts in the role assigned to monetary policy, for example policy changes made investment decision more closely based on the market forces. It also shows that price stickiness is more than wage stickiness as a major source of nominal rigidity in the UK. LowIncome This paper calls itself the first Countries attempt at estimating DSGE model for SSA country and projects it as the benchmark for lowincome countries. Results show that a exchange rate peg is significantly less successful than inflation targeting at stabilising the real economy due to higher interest rate volatility. New Zealand The main empirical findings are; a) the intertemporal consumption substitutability is very little which implies that the New Zealand does not produce close substitutes of the foreign goods. b) Immobile labor force is backed by the low elasticity of labor supply decisions. c) Price contracts were estimated around four quarters for import retailers and five quarters for domestic producers. e) Impulse response functions depict the dynamic behavior of shocks and the monetary transmission mechanism for the rest of economy. Brazil The empirical part of the paper yields promising qualitative results. The main empirical findings are: (i) a higher TOT improves its external competitiveness, shiffngthe world demand towards its goods. The consequent higher output heats the labor market, pushing the real wage and marginal cost up. (ii) Ceteris paribus, a higher TOT increases the real wage and marginal cost in terms of the domestic goods, leading each firm to adjust its nominal price up in order to increase its relative pricein terms of the other domestic goodand thereby preserve their markup. Chile Wages are optimally set with the span of eight years while the prices of domestic goods take several years. Prices of imported goods take three quarters. Results also depict the habit persistence in consumption and adjustment costs in investment are the relevant features. Impulse response shows that a commodity price shock generates soft consumption and investment booms and a GDP expansion. It also shows a real exchange rate appreciation lowers inflation and reduces employment. It depicts that a monetary policy shock generates positive responses of GDP, consumption and investment, and a fall in inflation. Colombia This model show that the policy shocks explain only 3.7 percent variation in inflation, 2.2 percent in real exchange rate and just 0.1 percent in output. The largest source of variation comes from the shocks in the TFP of the nontraded sector. Foreign shocks are also taken into account, terms of trade account for 62 percent in the variation of real exchange rate and about third of volatility in output, interest rates and inflation. It is also discussed that the DSGE model outcome does not show good degree of forecasting ability as compared with MTYNO. Latin America The estimates and the impulse response analysis shows that during the last two decades devolutionary policy shocks have been on average expansionary, in terms of output. It also depict that contractionary balance sheet transmission mechanism has been dominated by the expenditureswitching effect. While the balance sheet transmission mechanism has been weaker in Mexico than in Chile and Colombia. Czech Republic This model policy reaction with a parameterised forecast horizon and a generalised capital accumulation equation with imperfect intertemporal substitution of investment provide useful forecast of Czech Republic monetary policy decision variables. United States This study instead of some Euro Area focused conclusion provides some choices of inferences by showing comparisons of the values of priors. Euro Area This study suggested that there is large degree of price and wage stickiness in the euro area. Model based output and interest rate gap show a considerable uncertainty around it. There is not observed the liquidity impact and expectations take time to adjust and the output effects are much smaller. 
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