The causality between revenues and expenditure of the federal and provincial governments of Pakistan.
Article Type: Report
Subject: Budget deficits (Analysis)
Expenditures, Public (Analysis)
Fiscal policy (Analysis)
Tax collection
Author: Sadiq, Tahir
Pub Date: 12/22/2010
Publication: Name: Pakistan Development Review Publisher: Pakistan Institute of Development Economics Audience: Academic Format: Magazine/Journal Subject: Business, international; Social sciences Copyright: COPYRIGHT 2010 Reproduced with permission of the Publications Division, Pakistan Institute of Development Economies, Islamabad, Pakistan. ISSN: 0030-9729
Issue: Date: Winter, 2010 Source Volume: 49 Source Issue: 4
Product: Product Code: 9000144 Expenditures-Total Govt; 9210124 Expenditures-State Govt; 9100042 Fiscal Policy NAICS Code: 92113 Public Finance Activities
Geographic: Geographic Scope: Pakistan Geographic Code: 9PAKI Pakistan
Accession Number: 302769872
Full Text: This paper aims to identify the strategy for fiscal deficit reduction by studying the causal relationship between federal and provincial taxes and expenditure using the Granger Causality test for the period 1980-81 to 2009-10. The results indicate the absence of a strong causality in either direction between tax revenues and expenditure, thereby highlighting the weaknesses of fiscal management in the country.

1. INTRODUCTION

Large fiscal deficits and a growing debt burden have been a key element of the structural problems faced by the economy of Pakistan. During the last three years, for example, the budget deficit has averaged almost 6 percent of the GDP and the public debt has approached the level of 60 percent of the GDP. Targets agreed with IMF have been seriously violated and the SBA with the Fund has floundered because of the inability to control the fiscal deficit.

There is a growing perception that one of the root causes of inflation is the large borrowing from the Central Bank to finance the deficit. This has resulted in a popular demand for cutting down of unproductive expenditure and observing austerity along with implementation of a strong programme of reforms to raise the low tax to GDP ratio of the country by broad-basing the tax system and eliminating exemptions. The fundamental question is whether measures at reducing the fiscal deficit will have a, more or less, permanent impact. If an increase in tax revenue is accompanied subsequently by a rise in expenditure then the impact on the deficit is likely to be temporary or limited in character. Alternatively, if a cut in expenditure leads to a slackening of the fiscal effort then the gains are also not lasting in nature.

Therefore, a study of the direction of causality between tax revenue and expenditure is essential to determine the optimal strategy for deficit reduction. There is need to understand if governments in Pakistan first tax and then spend or first spend and then tax.

In other words, is there 'fiscal synchronisation' of the type pointed out by Frusternberg, et al. (1986)?

The paper is organised as follows: Section 2 reviews the literature on the relationship between taxation and expenditure. Section 3 describes the methodology and the data. Section 4 presents the results for the federal and the provincial governments combined, and Section 5 presents the conclusions and policy recommendations.

2. LITERATURE REVIEW

Different studies have been undertaken to understand the relationship between government revenue and expenditure. Three hypotheses have been postulated by Aziz, et al. (2000), first, a bi-directional relationship between expenditure and revenue, second, a unidirectional causality that runs from revenue to expenditure and, third, the causality from expenditure to revenue. All these hypotheses have important implications for the strategy to solve the budget deficit problem. Some support to the fiscal synchronisation hypothesis is given by Miller and Russek (1990) who concluded that there is bidirectional causality between taxes and government expenditures in the federal, state and local sectors of the USA. Kirchgassner and Prohl observe a bidirectional causality between revenue and expenditure both in the short run and long run for the Swiss federal government. Bohn (1991) shows that 50-65 percent of all deficits are caused by unexpected tax cuts and 65-70 percent are caused by high government expenditures, so there is a significant evidence in favour of both tax-and-spend and the spend-and-tax hypotheses. High deficits have been corrected by the combination of tax increase and cuts on expenditure. Payne (1998) shows that among 48 states of the USA, 24 support the tax-spend hypothesis, 8 the spend-tax hypothesis and 15 the hypothesis of fiscal synchronisation, which means revenue and expenditure are jointly determined.

Some of the studies have shown that there is unidirectional causality from government revenues to expenditures. Marlow and Manage (1987) found a unidirectional causality from tax revenues to expenditures on the state data of USA for all almost lag structures. For local governments they find causality from revenues to expenditure for the shortest lag length of two years, while for other lags revenue and expenditure appear independent of each other. Moalusi (2007) finds unidirectional causality from revenue to expenditure in Botswana. Owoye (1995) demonstrates that there is bidirectional causality between expenditures and taxes in five countries of G7, but in Italy and Japan causality is from taxes to expenditures.

The third hypothesis of first spend and tax later is also supported by many studies. For example, Barro (1979) indicated that during war and post war periods there is an impact of temporary increase in government expenditures on public debt which eventually leads to a rise in taxes.

The causality between taxes and expenditures for federal and provincial governments combined of Pakistan was studied by Hussain (2005) for the period 1973-2003. The author concludes that there is unidirectional causality from government expenditure to revenue. He offers two simultaneous solutions, first, to expand the tax base and ensure higher collection of taxes and second to cut the excess current expenditures. Further the work of Aisha, et al. supported spend and tax hypothesis in case of Pakistan as taxes revenues are determined by government expenditure. The authors performed a co-integration test which suggests that there exists a long run relationship between revenue and expenditure in Pakistan.

3. METHODOLOGY AND DATA

Various approaches can be adopted to study the relationship between revenues and expenditure, including Co-integration test, Granger causality test, Error correction model and Vector Autoregressive mode (VAR). Granger (1969) argued the revenues may be explained by past revenues and expenditures. If the past values of expenditure explain current revenues then there exists causality expenditure to revenue. If the opposite is the case then the flow of causation is from revenue to expenditure.

The simple model which tests the causal relationship between revenues and expenditures presented by Granger (1969) is as follows:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (1)

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (2)

Here the error terms, [[epsilon].sub.t] and [[eta].sub.t], are uncorrelated series with means that E [[[epsilon].sub.t], [[eta].sub.t]]=0. The ms are the given lag lengths. In the above equations if bj is not equal to zero it implies that direction of causality is from Y to X and similarly if cj is not equal to zero than the causality is from X to Y. If both bj and cj are not equal to zero there is a bidirectional causality between X and Y and if both bj and cj are equal to zero there exist no causal relationship between Xt and Yt.

For our research, X corresponds to expenditure and Y to tax revenues. The expenditure variable is designated as EXP and the revenue variable as REV.

Lag lengths, m, of the above equations are determined through Akaike Information Criterion (1969) and Schwarz Criterion (1978). Initially Equation 1 of expenditure is regressed on the lagged variables of expenditure, excluding revenue. Appropriate lag is selected where AIC are SC are minimum. Keeping this lag fixed, lags for the revenue have been introduced until AIC and SC are minimised. Same procedure is applied to Equation 2 for the determination of the optimal lag lengths of expenditure and causing revenue.

The null and alternate hypotheses for the equation 1 are as follows:

Ho: REV does not Granger Cause EXP.

H1: REV does Granger Cause EXP.

For the Equation 2 null and alternate hypotheses are as follows:

Ho: EXP does not Granger cause REV.

H1: EXP does Granger Cause REV.

If bj = 0 of Equation 1 and cj [not equal to] 0 of Equation 2, it implies there is a unidirectional causality from expenditure to revenue. Similarly if bj [not equal to] 0 of Equation 1 and cj = 0 of Equation 2 implies unidirectional causality from revenue to expenditure. If both bj [not equal to] 0 of Equation 1 and cj [not equal to] 0 of Equation 2 implies a bidirectional causality, finally if bj = 0 of Equation 1 and cj = 0 of Equation 2 implies no link between expenditure and revenue. We also expect that [summation] aj <1, [summation] bj
Data on federal and provincial tax revenues, current and development expenditure have been taken for the period, 1980-81 to 2009-10, from Pakistan Economic Survey and the State Bank of Pakistan. Revised estimates for the last year have been obtained from the website of fiscal operations maintained by the Ministry of Finance, Islamabad. Non-tax revenues, which include interest income, profits and dividends and miscellaneous receipts, have been excluded from the analysis as they are mostly exogenous in character. The series have been converted into real percapita magnitudes in order to avoid problems of non-stationarity, and are presented in Table 1.

4. EMPIRICAL RESULTS

4.1. Tax Revenue and total Expenditure

The regression results of causality between total tax revenues and total expenditures of the federal and provincial governments combined are given below. Total tax revenue consists of federal total tax and provincial total tax revenues. Total expenditure is the sum of federal and provincial current and total development expenditure. The results show that there does not exist any causal relationship between total government revenue and total expenditure. The null hypothesis that total revenue does not Granger cause total expenditure is accepted against the alternate that total revenue does Granger cause total expenditure at 5 percent significance level. Similarly, the null hypothesis that total tax expenditure does not Granger cause total revenue is also accepted against the alternate that total expenditure does Granger cause total revenue.

One of the principal reasons for the lack of responsiveness of expenditure to changes in revenue is the downward rigidity in major expenditure heads like defense, debt servicing, costs of civil administration, etc. Development expenditure is more discretionary in character but in the presence of a large throw forward of on-going development schemes it is difficult to cut back the size of the PSDP in the short run.

On the taxation size the inability to mobilise revenue quickly in the event of slippages on the expenditure side is due to the absence of a tax culture given the large size of the informal economy, presence of strong lobbies, low efficiency of tax administration and low elasticity of the tax system.

The failure in raising tax revenues in the presence of a rapidly growing trend in expenditure is vividly demonstrated by the experience after 2003-04 when the fiscal deficit was at its historically lowest level of 2.4 percent of the GDP. The emergence of the War on Terror and the resulting rise in security spending along with more recent problem of large subsidies to public sector enterprises and introduction of transfer payments have increased public expenditure by almost three percentage points of the GDP in the last six years. But the tax- to -GDP ratio has remained stagnant at about 10 percent of the GDP and, consequently, the fiscal deficit has risen to 6.3 percent of the GDP by 2009-10.

Results of the Granger Causality test between total tax revenues and total expenditures are given in Table 2. The underlying regressions are presented in Table 3.

Where

PCRTE = Real percapita expenditure,

PCRTTR = Real percapita tax revenues.

It may be noticed that, although not statistically significant, there appears to be some evidence of weak causation from tax revenues to expenditure. Hussain (2005) had concluded that there was causality from expenditure to revenue in the Pakistani context for an earlier period upto 2002-03. Clearly, the relationship has broken down due to the developments thereafter as described above.

4.2. Tax Revenue and Current Expenditure

We now test for the relationship between total tax revenue and total current expenditure. The results clearly show that there does not exist a causal relationship between total tax revenue and total current expenditures. The null hypothesis that total revenue does not Granger cause total current expenditure is accepted against the alternate that total revenue does Granger cause total current expenditure at 5 percent level of significance. Similarly the null hypothesis that total current expenditure does not Granger cause total revenue is also accepted against the alternate that total current expenditure does Granger cause total revenue.

Results of the Granger Causality test between total tax revenues and current expenditures are given in Table 4. The underlying regressions are presented in Table 5.

The results of regressions are given in Table 5.

where PCRTCE = Real percapita current expenditure

4.3 Tax Revenue and Development Expenditure

The results of the Granger Causality Test of the relationship between total tax revenue and development expenditure is shown below.

Null hypothesis that total revenue does not Granger cause total development expenditure is accepted against the alternate that total revenue does Granger cause total development expenditure at 5 percent level of significance. Similarly, the null hypothesis that total development expenditure does not Granger cause total revenue is also accepted against the alternate that total development expenditure does Granger cause total revenue. The underlying regressions between total tax revenues and development expenditure are presented in Table 7. Where PCRTDE = Real percapita Development expenditure.

Contrary perhaps to expectations, even the relatively discretionary part of expenditure on development is not related to tax revenues. As highlighted in Table 1, development expenditure has shown a steady declining trend in real percapita terms from 1992 to 2002, and thereafter a rising trend. This trend has proceeded independently of the trend in tax revenues.

5. CONCLUSIONS AND RECOMMENDATIONS

The Granger Causality test between total tax revenues and total expenditure of the federal and provincial governments combined has revealed the absence of any significant relationship. Extension of the test to determine the causality between tax revenues and the two major components of expenditure, viz., current expenditure and development expenditure, has also been unsuccessful.

The implication of these findings is that successive governments of Pakistan have been unstable to control the size of the fiscal deficits during the periods when public expenditure has been rising sharply, as happened, for example, after 2003-04 by responding with efforts at mobilising additional resources through the tax system. Alternatively, when revenues were stagnant in the late 90s adequate efforts were not made to control the level of public expenditure. These failures highlight the weaknesses in fiscal management in country.

However, there is a positive downside to the findings. The absence of any causality between tax revenues and expenditure does indicate that if vigorous efforts are made now to raise the tax-to-GDP ratio then this need not translate into increase in expenditure and there is, therefore, the likelihood of success of this strategy in reducing the fiscal deficit. Alternatively, if expenditure, especially on the current side, is curtailed then this is unlikely to be accompanied by any slackening of the fiscal effort. It is clear that the time has come for containing the fiscal deficit on both the revenue and expenditure front and thereby reducing inflationary pressures in the economy.

REFERENCES

Abdul Aziz, M., S. M. Habibullah, A. W. N. Saini, and M. Azali (2000) Testing for Causality Between Taxation and Government Spending: An Application of Toda-Yamamoto Approach. Pertanika Journal of Social Science and Humanities 8, 45-50.

Aisha, Z. and S. Khatoon (n.d.) Government Expenditure and Tax Revenue Causality and Cointegration; The Experience of Pakistan 1972-2007. Karachi: Research Institute, University of Karachi. http://www.pide.org.pk/psde/25/pdf/Day3/Zinaz%20Aisha.pdf

Akike, H. (1969) Fitting Autoregressive Models of Prediction. Annals of the Institute of Statistical Mathematics 2, 630-639.

Barro, J. R. (1979) On the Determination of the Public Debt. Journal of Political Economy 87: 5, 940-971.

Bohn, H. (1991) Budget Balance Through Revenue or Spending Adjustments? Some Historical Evidence for the United States. Journal of Monetary Economics 27, 333-359.

Furstenberg, G. M., J. R. yon Green, and H. J. Jeong (1986) Tax and Spend, or Spend and Tax. The Review of Economics and Statistics 68: 2, 179-188.

Granger, J. W. C. (1969) Investigating Causal Relationship by Econometric Models and Cross Spectural Methods. Econometrica 37: 3, 424-438.

Hussain, H. M. (2005) On the Causal Relationship between Government Expenditure and Tax Revenue in Pakistan. Social Policy and Development Centre.

Kirchgassner, G. and S. Prohl (n.d.) Causality between Expenditures and Revenues: Empirical Evidence from Switzerland. Link, http://www.sgvs.ire.eco.unisi.ch/ papers/Kirchg%C3%A4ssner_Prohl_Causality_SGVS06.pdf.

Marlow, M. L. and N. Manage (1987) Expenditures and Receipts: Testing for Causality in State and Local Government Finances. Public Choice 53, 243-255.

Miller, M. S. and F. S. Russek (1990) Co-integration and Error-correction Models: The Temporal Causality Between Government Taxes and Spending. Southern Economic Journal, Economic 57: 1, 221-229.

Moalusi, K. D. (2007) Causal Link between Government Spending and Revenue: A Case Study of Botswana. (Fordham Economic Discussion Paper Series, No. dp 2007-07).

Owoye, O. (1995) The Causal Relationship between Taxes and Expenditures in the G7 Countries: Co-integration and Error Correction Models. Applied Economic Letters 2, 19-22.

Payne, J. E. (1998) The Tax-spend Debate: Time Series Evidence from State Budgets. Public Choice 95, 307-320.

Schwarz, G. (1978) Estimating the Dimension of a Model. The Annals of Statistics 6:2, 461-464.

Comments

(1) The author attempts to identify strategy of fiscal deficit reduction by analysing casual relationship between taxes and expenditure of federal and provincial government.

(2) It is a good attempt as Pakistan is facing large and persistent deficit resulting in large debt accumulation. The study adds to the existing literature as it investigates the relation between revenue and expenditure including provincial revenue and expenditure.

(3) This analysis is important especially in the context of fiscal decentralisation that has allowed the provinces a greater role in fiscal affairs. (4) There is some room for improvement. The author has used data from 1980 to 2009, however to implement Engle Granger, we need a long time series to get reliable results.

(5) It would be more meaningful analysis if the author further examines the components of revenues and expenditures.

(6) Moreover, it would be useful if the economic results are explained in some detail. Only finding the non-existence and granger causality is not sufficient to establish that this is the reason of fiscal deficit.

(7) There are some editorial corrections e.g., the author has not properly formatted the tables and has posted the computer output.

(8) Finally let me say that I am glad that young researchers are taking interest in topical issues and are presenting their papers at large conferences such as Pakistan Society of Development Economists. This trend, I believe, is important to groom young researchers and give them an opportunity to get feedback from the fellow researchers and leading scholars.

Thank you.

Ejaz Ghani

Pakistan Institute of Development Economics,

Islamabad.

Tahir Sadiq is Lecturer, Department of Economics, Beaconhouse National University, Lahore.

Author's Note: The author is thankful to Hafiz A. Pasha, Professor, Dean, School of Social Sciences, Beaconhouse National University Lahore for his enormous help at each stage of the research.
Table 1
Percapita Real Tax Revenue and Expenditure of the Federal and
Provincial Government Combined (At Constant Prices of 1999-2000)

          Per Capita     Per Capita     Per Capita     Per Capita
             Real           Real           Real           Real
          Total Rev      Total Exp     Current Exp      Dev Exp
Years      (PCRTTR)       (PCRTE)        (PCRTCE)       (PCRTDE)

1981         2182           4104           2851           1254
1982         2088           3767           2616           1152
1983         2184           4025           2852           1172
1984         2186           3971           2935           1037
1985         2020           4139           3029           1110
1986         2157           4617           3243           1373
1987         2230           4987           3731           1256
1988         2420           5424           4019           1405
1989         2536           5323           4050           1273
1990         2650           5355           4001           1354
1991         2470           5494           4119           1375
1992         2776           5969           4273           1696
1993         2739           5748           4491           1256
1994         2630           5275           4242           1033
1995         2951           5344           4316           1027
1996         3088           5699           4663           1037
1997         2852           5194           4373           821
1998         2714           5518           4612           906
1999         2693           5229           4420           809
2000         2786           5579           4814           765
2001         2926           5132           4599           533
2002         2852           5209           4722           487
2003         3111           5732           5052           680
2004         3291           5721           4638           1083
2005         3401           6005           4647           1357
2006         3681           6853           5058           1795
2007         3676           8020           6128           1892
2008         3864           8899           7244           1655
2009         3832           8046           6496           1550
2010         3879           8518           6954           1563

Table 2
Results of the Granger Causality between Tax Revenues and Total
Expenditure

               Independent Variables           p-values

Dependent       Lag of         Lag of        Lag       Lag
Variable     Expenditure      Revenue        Exp       Rev

Percapita         1              1         0.0001     0.115
Real Exp

Percapita         1              1          0.611     0.000
Real Rev

Dependent
Variable      Inference     Causality

Percapita    Accept null   No causation
Real Exp     hypothesis

Percapita    Accept null
Real Rev     hypothesis

Table 3
Results of the Regressions between Tax Revenues and Total
Expenditure

Dependent Variable: PCRTE
Sample (Adjusted): 1982 to 2010
Included Observations: 29 after Adjustments

Variable                Coefficient         Std. Error

C                         -220.685            448.894
PCRTE (-1)                 0.761               0.166
PCRTTR (-1)                0.606               0.371
R-Squared                  0.888        Mean dependent var
Adjusted R-Squared         0.879        S.D. dependent var
S.E. of Regression        445.170      Akaike info criterion
Sum Squared Resid       5152586.000      Schwarz criterion
Log Likelihood            -216.421      Hannan-Quinn criter
F-statistic               102.982       Durbin-Watson stat
Prob (F-statistic)         0.000

Variable                t-Statistic            Prob.

C                          -0.492              0.627
PCRTE (-1)                 4.587              0.0001
PCRTTR (-1)                1.633               0.115
R-Squared                                    5682.454
Adjusted R-Squared                           1281.317
S.E. of Regression                            15.132
Sum Squared Resid                             15.274
Log Likelihood                                15.177
F-statistic                                    1.918
Prob (F-statistic)

Dependent Variable: PCRTE
Sample (Adjusted): 1982 to 2010
Included Observations: 29 after Adjustments

Variable                Coefficient         Std. Error

C                          48.577             156.293
PCRTTR(-1)                 0.945               0.129
PCRTE(-1)                  0.030               0.058
R-Squared                  0.928        Mean dependent var
Adjusted R-Squared         0.922        S.D. dependent var
S.E. of Regression        154.996      Akaike info criterion
Sum Squared Resid        624620.200      Schwarz criterion
Log Likelihood            -185.825      Hannan-Quinn criter
F-statistic               167.303       Durbin-Watson stat
Prob(F-statistic)          0.000

Variable                t-Statistic            Prob.

C                          0.311               0.758
PCRTTR(-1)                 7.313               0.000
PCRTE(-1)                  0.514               0.611
R-Squared                                    2851.148
Adjusted R-Squared                            556.236
S.E. of Regression                            13.022
Sum Squared Resid                             13.164
Log Likelihood                                13.067
F-statistic                                    2.182
Prob(F-statistic)

Table 4
Results of the Granger Causality Test Between Revenues and Current
Expenditure

                   Independent variables            p-values

Dependent           Lag of         Lag of        Lag       Lag
Variable         Expenditure      Revenue        exp       rev

Percapita Real        1              2          0.005     0.239
Current Exp

Percapita Real        1              1          0.430     0.000
Rev

Dependent
Variable            Inference         Causality

Percapita Real     Accept null      No causation
Current Exp        hypothesis

Percapita Real     Accept null
Rev                hypothesis

The results of regressions are given in Table 5.

Table 5
Results of Regressions of Tax Revenue and Current Expenditure

Dependent Variable: PCRTE
Sample (Adjusted): 1982 to 2010
Included Observations: 29 after Adjustments

Variable              Coefficient          Std. Error

C                       -502.572            402.829
PCRTCE(-1)               0.529               0.172
PCRTTR(-1)               0.397               0.487
PCRTTR(-2)               0.587               0.486
R-Squared                0.904         Mean dependent var
Adjusted R-Squared       0.892         S.D. dependent var
S.E. of Regression      354.477      Akaike info criterion
Sum Squared Resid       3015693        Schwarz criterion
Log Likelihood          -201.950      Hannan-Quinn criter.
F-statistic              75.391        Durbin-Watson stat
Prob(F-statistic)        0.000

Variable              t-Statistic            Prob.

C                        -1.248              0.224
PCRTCE(-1)               3.070               0.005
PCRTTR(-1)               0.815               0.423
PCRTTR(-2)               1.207               0.239
R-Squared                                   4561.522
Adjusted R-Squared                          1079.009
S.E. of Regression                           14.711
Sum Squared Resid                            14.901
Log Likelihood                               14.769
F-statistic                                  1.900
Prob(F-statistic)

Dependent Variable: PCRTE
Sample (Adjusted): 1982 to 2010
Included Observations: 29 after Adjustments

Variable              Coefficient          Std. Error

C                        87.083             163.671
PCRTTR(-1)               0.901               0.140
PCRTCE(-1)               0.057               0.071
R-Squared                0.929         Mean dependent var
Adjusted R-Squared       0.923         S.D. dependent var
S.E. of Regression      153.894      Akaike info criterion
Sum Squared Resid      615767.100      Schwarz criterion
Log Likelihood          -185.618      Hannan-Quinn criter
F-statistic             169.896        Durbin-Watson stat

Variable              t-Statistic       Prob.

C                        0.532          0.599
PCRTTR(-1)               6.418          0.000
PCRTCE(-1)               0.801          0.430
R-Squared                              2851.148
Adjusted R-Squared                     556.236
S.E. of Regression                      13.008
Sum Squared Resid                       13.150
Log Likelihood                          13.052
F-statistic                             2.142

Table 6
Results of the Granger Causality Test between Revenues and
Development Expenditure

                     Independent variables            p-values

Dependent             Lag of         Lag of        Lag       Lag
Variable           Expenditure      Revenue        Exp       Rev

Percapita Real          1              1          0.000     0.564
Development Exp

Percapita Real          1              1          0.848     0.000
Rev

Dependent
Variable              Inference         Causality

Percapita Real       Accept null      No causation
Development Exp      hypothesis

Percapita Real       Accept null
Rev                  hypothesis

Table 7
Results of Regressions Between Tax Revenues and Development
Expenditure

Dependent Variable: PCRTE
Sample (Adjusted): 1982 to 2010
Included Observations: 29 after Adjustments

Variable                  Coefficient          Std. Error

C                            84.698              217.407
PCRTDE (-1)                  0.832                0.114
PCRTTR (-1)                  0.044                0.076
R-Squared                    0.702         Mean dependent var
Adjusted R-Squared           0.679         S.D. dependent var
S.E. of Regression          203.970       Akaike info criterion
Sum Squared Resid           1081700         Schwarz criterion
Log Likelihood              -193.787       Hannan-Quinn criter
F-statistic                  30.637        Durbin-Watson stat
Prob (F-statistic)           0.000

Variable                  t-Statistic             Prob.

C                            0.390                0.700
PCRTDE (-1)                  7.268                0.000
PCRTTR (-1)                  0.584                0.564
R-Squared                                       1188.028
Adjusted R-Squared                               360.107
S.E. of Regression                               13.572
Sum Squared Resid                                13.713
Log Likelihood                                   13.616
F-statistic                                       1.514
Prob (F-statistic)

Dependent Variable: PCRTE
Sample (Adjusted): 1982 to 2010
Included Observations: 29 after Adjustments

Variable                  Coefficient          Std. Error

C                            55.537              165.926
PCRTTR (-1)                  1.008                0.058
PCRTDE (-1)                  -0.017               0.087
R-Squared                    0.927         Mean dependent var
Adjusted R-Squared           0.922         S.D. dependent var
S.E. of Regression          155.670       Akaike info criterion
Sum Squared Resid            630064         Schwarz criterion
Log Likelihood              -185.950       Hannan-Quinn criter
F-statistic                 165.745        Durbin-Watson stat
Prob (F-statistic)           0.000

Variable                  t-Statistic         Prob.

C                            0.335            0.741
PCRTTR (-1)                  17.420           0.000
PCRTDE (-1)                  -0.194           0.848
R-Squared                                    2851.148
Adjusted R-Squared                           556.236
S.E. of Regression                            13.031
Sum Squared Resid                             13.173
Log Likelihood                                13.075
F-statistic                                   2.223
Prob (F-statistic)
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