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: 00309729 
Issue:  Date: Winter, 2010 Source Volume: 49 Source Issue: 4 
Product:  Product Code: 9000144 ExpendituresTotal Govt; 9210124 ExpendituresState 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 198081 to 200910. 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 broadbasing 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 bidirectional 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 5065 percent of all deficits are caused by unexpected tax cuts and 6570 percent are caused by high government expenditures, so there is a significant evidence in favour of both taxandspend and the spendandtax 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 taxspend hypothesis, 8 the spendtax 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 causality between taxes and expenditures for federal and provincial governments combined of Pakistan was studied by Hussain (2005) for the period 19732003. 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 cointegration 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 Cointegration 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, 198081 to 200910, 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. Nontax 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 nonstationarity, 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 ongoing 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 200304 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 200910. 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 200203. 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 200304 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 taxtoGDP 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 TodaYamamoto Approach. Pertanika Journal of Social Science and Humanities 8, 4550. Aisha, Z. and S. Khatoon (n.d.) Government Expenditure and Tax Revenue Causality and Cointegration; The Experience of Pakistan 19722007. 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, 630639. Barro, J. R. (1979) On the Determination of the Public Debt. Journal of Political Economy 87: 5, 940971. Bohn, H. (1991) Budget Balance Through Revenue or Spending Adjustments? Some Historical Evidence for the United States. Journal of Monetary Economics 27, 333359. 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, 179188. Granger, J. W. C. (1969) Investigating Causal Relationship by Econometric Models and Cross Spectural Methods. Econometrica 37: 3, 424438. 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, 243255. Miller, M. S. and F. S. Russek (1990) Cointegration and Errorcorrection Models: The Temporal Causality Between Government Taxes and Spending. Southern Economic Journal, Economic 57: 1, 221229. Moalusi, K. D. (2007) Causal Link between Government Spending and Revenue: A Case Study of Botswana. (Fordham Economic Discussion Paper Series, No. dp 200707). Owoye, O. (1995) The Causal Relationship between Taxes and Expenditures in the G7 Countries: Cointegration and Error Correction Models. Applied Economic Letters 2, 1922. Payne, J. E. (1998) The Taxspend Debate: Time Series Evidence from State Budgets. Public Choice 95, 307320. Schwarz, G. (1978) Estimating the Dimension of a Model. The Annals of Statistics 6:2, 461464. 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 nonexistence 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 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 19992000) 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 pvalues 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 RSquared 0.888 Mean dependent var Adjusted RSquared 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 HannanQuinn criter Fstatistic 102.982 DurbinWatson stat Prob (Fstatistic) 0.000 Variable tStatistic Prob. C 0.492 0.627 PCRTE (1) 4.587 0.0001 PCRTTR (1) 1.633 0.115 RSquared 5682.454 Adjusted RSquared 1281.317 S.E. of Regression 15.132 Sum Squared Resid 15.274 Log Likelihood 15.177 Fstatistic 1.918 Prob (Fstatistic) 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 RSquared 0.928 Mean dependent var Adjusted RSquared 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 HannanQuinn criter Fstatistic 167.303 DurbinWatson stat Prob(Fstatistic) 0.000 Variable tStatistic Prob. C 0.311 0.758 PCRTTR(1) 7.313 0.000 PCRTE(1) 0.514 0.611 RSquared 2851.148 Adjusted RSquared 556.236 S.E. of Regression 13.022 Sum Squared Resid 13.164 Log Likelihood 13.067 Fstatistic 2.182 Prob(Fstatistic) Table 4 Results of the Granger Causality Test Between Revenues and Current Expenditure Independent variables pvalues 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 RSquared 0.904 Mean dependent var Adjusted RSquared 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 HannanQuinn criter. Fstatistic 75.391 DurbinWatson stat Prob(Fstatistic) 0.000 Variable tStatistic Prob. C 1.248 0.224 PCRTCE(1) 3.070 0.005 PCRTTR(1) 0.815 0.423 PCRTTR(2) 1.207 0.239 RSquared 4561.522 Adjusted RSquared 1079.009 S.E. of Regression 14.711 Sum Squared Resid 14.901 Log Likelihood 14.769 Fstatistic 1.900 Prob(Fstatistic) 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 RSquared 0.929 Mean dependent var Adjusted RSquared 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 HannanQuinn criter Fstatistic 169.896 DurbinWatson stat Variable tStatistic Prob. C 0.532 0.599 PCRTTR(1) 6.418 0.000 PCRTCE(1) 0.801 0.430 RSquared 2851.148 Adjusted RSquared 556.236 S.E. of Regression 13.008 Sum Squared Resid 13.150 Log Likelihood 13.052 Fstatistic 2.142 Table 6 Results of the Granger Causality Test between Revenues and Development Expenditure Independent variables pvalues 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 RSquared 0.702 Mean dependent var Adjusted RSquared 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 HannanQuinn criter Fstatistic 30.637 DurbinWatson stat Prob (Fstatistic) 0.000 Variable tStatistic Prob. C 0.390 0.700 PCRTDE (1) 7.268 0.000 PCRTTR (1) 0.584 0.564 RSquared 1188.028 Adjusted RSquared 360.107 S.E. of Regression 13.572 Sum Squared Resid 13.713 Log Likelihood 13.616 Fstatistic 1.514 Prob (Fstatistic) 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 RSquared 0.927 Mean dependent var Adjusted RSquared 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 HannanQuinn criter Fstatistic 165.745 DurbinWatson stat Prob (Fstatistic) 0.000 Variable tStatistic Prob. C 0.335 0.741 PCRTTR (1) 17.420 0.000 PCRTDE (1) 0.194 0.848 RSquared 2851.148 Adjusted RSquared 556.236 S.E. of Regression 13.031 Sum Squared Resid 13.173 Log Likelihood 13.075 Fstatistic 2.223 Prob (Fstatistic) 
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