1 / 30

Dissertation Paper Real effective exchange rates and their influence on Romania’s trade with European Union Countries

ACADEMY OF ECONOMIC STUDIES, BUCHAREST DOCTORAL SCHOOL OF FINANCE AND BANKING DOFIN. Dissertation Paper Real effective exchange rates and their influence on Romania’s trade with European Union Countries. MSc Student :Grigorescu Madalina Supervisor : Professor Moisa Altar. Topics. Objectives

snow
Télécharger la présentation

Dissertation Paper Real effective exchange rates and their influence on Romania’s trade with European Union Countries

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. ACADEMY OF ECONOMIC STUDIES, BUCHAREST DOCTORAL SCHOOL OF FINANCE AND BANKING DOFIN Dissertation PaperReal effective exchange rates and their influence on Romania’s trade with European Union Countries MSc Student :Grigorescu Madalina Supervisor : Professor Moisa Altar

  2. Topics • Objectives • Introduction • Review of the literature • Theoretical models & formulas • Empirical analysis • Conclusions

  3. Objectives • To determine REER based on CPI and PPI indices weighted by the export volume of Romania to European Union countries • To providean empirical investigation on the Romania’s REER influence on its trade with European Union countries • Export • Import • Trade balance graph

  4. Introduction: Real Effective Exchange Rate • Useful indicator of one country’s competitiveness • The appropriate definition and calculation of REER depend upon the economic issue to be demonstrated and data availability • The “effective” aspect of REER is referring to the weights to be put upon each interacting partner country • Import-weighted indices • Exports-weighted indices • Total direct trade (export and imports) • Multilateral export-weight • Indices to be included in REER’s measurement formula • CPI • PPI • GDP deflators • ULC each having its advantages and disadvantages

  5. Theoretical models and formulas • RER = nominal exchange rate adjusted for price level differences between countries (domestic P and abroad P* ) • REER= multilateral real exchange rate REER is usually presented in several context including: 1) relating real exchange rates to productivity differencials 2) estimating the relative price responsiveness of the trade flow 3) assessing its impact on country’s competitiveness

  6. Review of the literature:Studies on EU accession countries • Barell,Dawn, Smidkova (2002)„Estimates of Fundamental real effective exchange rate for the five EU preaccession countries” • Stability of REER will not automatically be in line with economic developments • De Broeck, Slok (2001) „Interpreting real exchange rate movements in transition countries” • EU accession countries can expect to experience further productivity –driven REER appreciations • Egart, Balasz (2002) „Investigating the Balassa-Samuelson hypothesis in transition :do we understand what we see?” • Continuous capital inflows will upward pressure on nominal exchange rate and provoke exchange rate to appreciate to unsustainable levels • Egart, Balasz and Drine , Imed and Rault, Cristophe (2002), „On the Balassa-Samuleson effect in the transition countries : a panel study” • Evidence for Romania : cointegration very unstable • Stucka, Tihomi (2004) „The effect of exchange rate change in the trade balance in Croatia” • It is questionable weather permanent depreciation is desirable to improve the trade balance • Kim,Korhonen (2002),”Equilibrium exchange rates in transition countries: evidence from dynamic panel models” • Serious challenges for the exchange rates policies in EU accession countries as joining Euro at the current level of exchange rate risks undermining exports to EU countries

  7. Theoretical implications: • When REER rises (REER depreciates) -> each unit of domestic output purchases fewer units of foreign output; • Foreign consumers demand more of our products-> the volume of exports will rise • Domestic consumers purchase fewer units of expensive foreign products -> imports decreases measured in foreign output units but increases measured in domestic output units • When REER decrease (REER appreciates) -> the opposite situation • The evolution of the exports is obvious while the evolution of imports is ambiguous • All things equal, the volume effect of REER changes outweighs the value effect , and a depreciation of REER improves the trade balance and an appreciation worsens the trade balance

  8. Empirical analysis • Data series • Results

  9. Data series • Period : 1990-2003 • Frequency : quarterly data • Log of REER_CPI index calculated as a geometric average using CPI index and weights as bilateral exports of Romania with EU countries • Log of REER_PPI index calculated as a geometric average using PPI index and weights as bilateral exports of Romania with EU countries • Log of Exports and Imports series of Romania with EU countries • Log of Trade Balance of Romania with EU countries back

  10. Results • Unit root tests on series • Augmented Dickey Fuller tests: Given the I(1) nature of the series, the cointegration analysis is employed to explore the long-run relationship among the variables • Cointegration analysis • Vector Error Correction Models • To observe short-run deviations of variables from long-run equilibrium path • To see the speed of adjustment of the variables to shocks from long-run equilibrium

  11. Cointegration analysis For the obtained number of lags I found cointegration equation for Export and REER and for Import and REER both for the 5% level of significance

  12. Export and REER_CPI and REER_PPI Import and REER_CPI and REER_PPI

  13. The hypothesis that REER_CPI and REER_PPI do not Granger cause the volume of export are rejected while the hypothesis that EXPORT do not Granger cause REER_CPI and REER_PPI are not rejected

  14. The hypothesis that REER_CPI and REER_PPI do not Granger cause the volume of Import are rejected while the hypothesis that IMPORT do not Granger cause REER_CPI and REER_PPI are not rejected

  15. Responses of Export and Import to REER_CPI and REER_PPIimpulses

  16. Results of regression for the two types of REER Newey-West HAC Standard Errors & Covariance (lag truncation=3) Export= REER_CPI*2.714627-13.4857 R-squared 0.735833 D-W=0.25 [7.37] [-7.15] Export= REER_PPI*3.058773-15.33536 R-squared 0.677775 D-W=0.24 [6.98] [-6.83] Import =REER_CPI*2.726184-13.44575 R-squared 0.863549 D-W=0.47 [10.88] [-10.57] Import =REER_PPI*3.121839-15.55607 R-squared 0.821542 D-W=0.45 [10.22] [-9.97] 1.072.714627 =1.2016 ≈20.16% and 1.043.058773 =1.1274 ≈12,74 % respectively the volume of Export 1.072.726184 =1.2025 ≈ 20.25 % and 1.043.121839 =1.13025≈13% the volume of Import 0.932.714627 =0.8211 ≈ 17% and 0.963.058773 =0.8826 ≈11% respectively the volume of Export 0.932.726184 = 0.82050≈ 18% and 0.963.121839 = 0.8803≈ 12% the volume of Import back

  17. Export and REER

  18. Import and REER

  19. Error correction equations: Estimation Method: Least Squares Sample: 1991:3 2003:4 Included observations: 50 Total system (balanced) observations 100 Equation:D(EXPORT) = C(1)*( EXPORT(-1) - 4.165968926*REER_PPI( -1) + 21.01019822 ) + C(2)*D(EXPORT(-1)) + C(3)*D(EXPORT(-2)) + C(4)*D(EXPORT(-3)) + C(5)*D(EXPORT(-4)) + C(6)*D(EXPORT(-5)) + C(7)*D(REER_PPI(-1)) + C(8)*D(REER_PPI(-2)) + C(9) *D(REER_PPI(-3)) + C(10)*D(REER_PPI(-4)) + C(11) *D(REER_PPI(-5)) + C(12) Observations: 50 C(1)=-0.028198 t-Statistic =-3.767567 Prob =0.0003 R-squared 0.979022 Mean dependent var 0.032526 Adjusted R-squared 0.972949 S.D. dependent var 0.052673 S.E. of regression 0.008663 Sum squared resid 0.002852 Durbin-Watson stat 2.047369

  20. Equation:D(IMPORT) = C(1)*( IMPORT(-1) - 1.568281763*REER_CPI(-1) + 7.625304795 ) + C(2)*D(IMPORT(-1)) + C(3)*D(IMPORT(-2)) + C(4)*D(IMPORT(-3)) + C(5)*D(IMPORT(-4)) + C(6)*D(IMPORT( -5)) + C(7)*D(REER_CPI(-1)) + C(8)*D(REER_CPI(-2)) + C(9) *D(REER_CPI(-3)) + C(10)*D(REER_CPI(-4)) + C(11) *D(REER_CPI(-5)) + C(12) Observations: 50 C(1)=-0.026887 t-Statistic =-3.289858 Prob =0.0015 R-squared 0.878063 Mean dependent var 0.035058 Adjusted R-squared 0.842766 S.D. dependent var 0.040824 S.E. of regression 0.016188 Sum squared resid 0.009958 Durbin-Watson stat 1.664252 Equation:D(IMPORT) = C(1)*( IMPORT(-1) - 1.300769017*REER_PPI(-1) + 6.323013095 ) + C(2)*D(IMPORT(-1)) + C(3)*D(IMPORT(-2)) + C(4)*D(IMPORT(-3)) + C(5)*D(IMPORT(-4)) + C(6)*D(IMPORT(-5)) + C(7)*D(REER_PPI(-1)) + C(8)*D(REER_PPI(-2)) + C(9) *D(REER_PPI(-3)) + C(10)*D(REER_PPI(-4)) + C(11) *D(REER_PPI(-5)) + C(12) Observations: 50 C(1)=-0.032755 t-Statistic =-3.185857 Prob =0.0021 R-squared 0.876274 Mean dependent var 0.035058 Adjusted R-squared 0.840458 S.D. dependent var 0.040824 S.E. of regression 0.016306 Sum squared resid 0.010104 Durbin-Watson stat 1.675513

  21. Results of regressions: EXPORT =REER_CPI *0.565837+GDP_EU*0.390866 -0.971709 R-squared 0.691239 , D-W=0.54 [3.57] [2.71] [- 1.26] EXPORT =REER_PPI *0.441380+GDP_EU*0.507131 -0.887198 R-squared 0.608194 , D-W=0.38 [3.16] [3.26] [-1.11] IMPORT=REER_CPI*-0.095769+EXPORT*0.802969+AGR_DEMAND*0.048147+ 1.078879 R-squared 0.961766 , D-W=0.28 [-1.59] [16.92] [1.33] [3.66] IMPORT=REER_PPI*-0.007240+EXPORT*0.793771+AGR_DEMAND*0.023037+ 1.078879 R-squared 0.969995 , D-W=0.25 [-0.137] [15.98] [0.48] [2.42]

  22. REER influence on Trade Balance Romania has negative Trade Balance (TB) with EU countries VAR lag length criteria : 7 lags for both REER_CPI and REER_PPI relationship with TB

  23. REER influence on Trade Balance • cointegration equation for 5% level of significance for the two cases TB and REER_CPI and TB and REER_PPI Lags interval (in first differences): 1 to 7 Unrestricted Cointegration Rank Test

  24. Pairwise Granger Causality Tests: Sample: 1990:1 2003:4 Lags: 1 Null Hypothesis: Obs F-Statistic Probability REER_CPI does not Granger Cause TB 55 9.52595 0.00324 TB does not Granger Cause REER_CPI 0.02620 0.87203 Lags: 2 Null Hypothesis: Obs F-Statistic Probability REER_CPI does not Granger Cause TB 54 2.32283 0.10869 TB does not Granger Cause REER_CPI 0.02812 0.97229 Lags: 1 Null Hypothesis: Obs F-Statistic Probability REER_PPI does not Granger Cause TB 55 9.19004 0.00379 TB does not Granger Cause REER_PPI 0.01979 0.88866 Lags: 2 Null Hypothesis: Obs F-Statistic Probability REER_PPI does not Granger Cause TB 54 2.31398 0.10958 TB does not Granger Cause REER_PPI 0.02818 0.97223

  25. Results of regressions for the two types of REER TB=REER_CPI*1.65779 -8.692956 R-squared 0.441621 , D-W=0.79 [3.6841] [-3.8573] TB=REER_PPI*1.92424 -9.293298 R-squared 0.431312 , D-W=0.78 [3.6981] [-3.8518] • 1.071.65 =1.118 ≈11.8 % and 1.041.92 =1.078 ≈7.8 % • 0.931.65 =0.887 ≈ 12 % and 0.961.92 =0.92 ≈8 % • TB does not have the expected sign and consequently it initially worsens at REER depreciations and then it improves (starting with lag 4 it has the expected negative sign)

  26. TB and REER_CPI (7 lags): Error Correction Model D(TB) = 0.1370008082*( TB(-1) + 0.01767896916*REER_CPI_LOG(-1) ) + 0.7865814588*D(TB(-1)) - 0.3968784339*D(TB(-2)) + 0.03360259529*D(TB(-3)) - 0.2447805494*D(TB(-4)) -0.04381380141*D(TB(-5)) - 0.04652583436*D(TB(-6)) - 0.1803369447*D(TB(-7)) +3.425845879*D(REER_CPI (-1)) – 0.8003956003*D(REER_CPI (-2)) +1.207371803*D(REER_CPI (-3)) +1.756795848*D(REER_CPI (-4)) - 3.157573105*D(REER_CPI (-5)) + 2.403071583*D(REER_CPI (-6)) - 0.01985208971*D(REER_CPI (-7)) D(REER_CPI) = - 0.06954231854*( TB(-1) + 0.01767896916*REER_CPI(-1) ) –0.07424238375*D(TB(-1)) + 0.1036032247*D(TB(-2)) +0.005701677302*D(TB(-3)) +0.03426812401*D(TB(-4)) + 0.01956357912*D(TB(-5)) + 0.05240118994*D(TB(-6)) +0.04620054128*D(TB(-7)) - 0.5301569997*D(REER_CPI(-1)) + 0.02040601877*D(REER_CPI(-2)) –0.4077126554*D(REER_CPI(-3)) + 0.3907634519*D(REER_CPI(-4)) +0.1055090966*D(REER_CPI(-5)) - 0.5415890667*D(REER_CPI(-6)) +0.03241797129*D(REER_CPI(-7)) TB and REER_PPI (7 lags): D(TB) = 0.1453655075*( TB(-1) + 0.2806808741*REER_PPI(-1) - 1.039744678 ) + 0.7616830328*D(TB(-1)) - 0.5830842106*D(TB(-2)) + 0.1383830284*D(TB(-3)) - 0.2598415963*D(TB(-4)) - 0.006246235075*D(TB(-5)) - 0.08143625724*D(TB(-6)) - 0.1648990411*D(TB(-7)) + 3.078886096*D(REER_PPI(-1)) - 1.223630924*D(REER_PPI(-2)) + 1.706903517*D(REER_PPI(-3)) + 1.682785129*D(REER_PPI(-4)) - 2.927038695*D(REER_PPI(-5)) + 2.73469155*D(REER_PPI(-6)) - 0.8225060185*D(REER_PPI(-7)) - 0.00491755967 D(REER_PPI) = - 0.07763000086*( TB(-1) + 0.2806808741*REER_PPI(-1) - 1.039744678 ) - 0.04419584655*D(TB(-1)) + 0.1514702121*D(TB(-2)) - 0.01653847494*D(TB(-3)) + 0.03061622078*D(TB(-4)) + 0.01204721762*D(TB(-5)) + 0.05570758654*D(TB(-6)) + 0.04820989632*D(TB(-7)) - 0.4477123584*D(REER_PPI(-1)) + 0.03722262183*D(REER_PPI(-2)) - 0.5756806286*D(REER_PPI(-3)) + 0.2824755348*D(REER_PPI(-4)) - 0.0615261533*D(REER_PPI(-5)) - 0.6419286947*D(REER_PPI(-6)) + 0.161200314*D(REER_PPI(-7)) + 0.01206296165

  27. Dependent Variable: D(TB) Method: Least Squares Sample(adjusted): 1992:1 2003:4 Included observations: 48 after adjusting endpoints D(TB) = C(1)*( TB(-1) + 0.01069773101*REER_CPI(-1) + 0.2191682532 ) + C(2)*D(TB(-1)) + C(3)*D(TB(-2)) + C(4)*D(TB(-3)) + C(5)*D(TB( -4)) + C(6)*D(TB(-5)) + C(7)*D(TB(-6)) + C(8)*D(TB(-7)) + C(9) *D(REER_CPI(-1)) + C(10)*D(REER_CPI(-2)) + C(11)*D(REER_CPI(-3)) + C(12)*D(REER_CPI(-4)) + C(13) *D(REER_CPI(-5)) + C(14)*D(REER_CPI(-6)) + C(15) *D(REER_CPI(-7)) + C(16) Coefficient Std. Error t-Statistic Prob. C(1) 0.154977 0.087303 1.775161 0.0854 C(2) 0.772608 0.225150 3.431524 0.0017 C(3) -0.430887 0.253643 -1.698796 0.0991 C(4) 0.042718 0.140261 0.304563 0.7627 C(5) -0.252806 0.076088 -3.322545 0.0022 C(6) -0.052952 0.085477 -0.619482 0.5400 C(7) -0.063642 0.080023 -0.795298 0.4323 C(8) -0.191026 0.071940 -2.655359 0.0122 C(9) 3.421184 0.646882 5.288729 0.0000 C(10) -0.841963 0.769861 -1.093657 0.2823 C(11) 1.268492 0.664479 1.909003 0.0653 C(12) 1.716229 0.449494 3.818133 0.0006 C(13) -3.198078 0.704544 -4.539218 0.0001 C(14) 2.399025 0.873477 2.746522 0.0098 C(15) -0.156624 0.839850 -0.186491 0.8532 C(16) -0.016135 0.026257 -0.614526 0.5432 R-squared 0.705076 Mean dependent var 0.022440 Adjusted R-squared 0.566830 S.D. dependent var 0.169311 S.E. of regression 0.111433 Akaike info criterion -1.289581 Sum squared resid 0.397356 Schwarz criterion -0.665848 Log likelihood 46.94995 Durbin-Watson stat 2.087974 White Heteroskedasticity Test: F-statistic 1.788021 Probability 0.116205 Jarque-Bera normality Test: Statistic 2.391790 Probability 0.302433

  28. Dependent Variable: D(TB) Method: Least Squares Sample(adjusted): 1992:1 2003:4 Included observations: 48 after adjusting endpoints D(TB) = C(1)*( TB(-1) + 0.3370609842*REER_PPI(-1) - 1.444550447 ) + C(2)*D(TB(-1)) + C(3)*D(TB(-2)) + C(4)*D(TB(-3)) + C(5)*D(TB(-4)+ C(6)*D(TB(-5)) + C(7)*D(TB(-6)) + C(8)*D(TB(-7)) + C(9) *D(REER_PPI(-1)) + C(10)*D(REER_PPI(-2)) + C(11 ) *D(REER_PPI(-3)) + C(12)*D(REER_PPI(-4)) + C(13) *D(REER_PPI(-5)) + C(14)*D(REER_PPI(-6)) + C(15) *D(REER_PPI(-7)) + C(16) Coefficient Std. Error t-Statistic Prob. C(1) 0.146502 0.074730 1.960429 0.0587 C(2) 0.645211 0.208309 3.097381 0.0040 C(3) -0.472682 0.226722 -2.084853 0.0451 C(4) 0.098739 0.133076 0.741979 0.4635 C(5) -0.265859 0.073777 -3.603558 0.0011 C(6) -0.022232 0.084235 -0.263930 0.7935 C(7) -0.076169 0.077014 -0.989016 0.3301 C(8) -0.156350 0.068061 -2.297198 0.0283 C(9) 2.884267 0.560499 5.145893 0.0000 C(10) -0.906927 0.690607 -1.313232 0.1984 C(11) 1.541538 0.586483 2.628445 0.0131 C(12) 1.775626 0.459297 3.865960 0.0005 C(13) -2.656261 0.619329 -4.288936 0.0002 C(14) 2.369020 0.781475 3.031472 0.0048 C(15) -0.509762 0.766876 -0.664725 0.5110 C(16) -0.006860 0.024101 -0.284647 0.7777 R-squared 0.704920 Mean dependent var 0.022440 Adjusted R-squared 0.566601 S.D. dependent var 0.169311 S.E. of regression 0.111463 Akaike info criterion -1.289052 Sum squared resid 0.397566 Schwarz criterion -0.665318 Log likelihood 46.93725 Durbin-Watson stat 2.215315 White Heteroskedasticity Test : F-statistic 1.687595 Probability 0.141207 Jarque-Bera normality Test: Statistic 6.482801 Probability 0.039109

  29. Conclusions • Results show that is possible to start building a quantitative background for discussion about REER in Romania during the accession process • REER is a useful summary indicator of essential economic information • REER can be a good indicator for monetary and exchange rate policies in order to forecast trade balance in a country (R-squared ≈ 70%) • Exports and Imports have the expected reaction to REER movements • Trade Balance initially worsens after a REER depreciation and then it improves • It is questionable whether permanent depreciation is desirable to improve trade balance

  30. Romanian Trade volumes Romanian “ Trade Openness” to GDP ratio mil USD 86.0% 14000 84.0% 12000 82.0% 10000 80.0% 8000 78.0% Weight in GDP export with EU 6000 76.0% export with Europe 4000 74.0% 2000 72.0% Total export 70.0% 0 68.0% 1990 1992 1994 1996 1998 2000 2002 2001 2002 2003 period period Source: Romanian External Trade Department back

More Related