The Academy of Economic Studies Doctoral School of Finance and Banking Exchange Rate Pass-Through into Inflation in Romania MSc student Ciurilă Nicoleta Coordinator Professor Moisă Altăr Bucharest, July 2006
The importance of the exchange rate pass-through • The aims of the paper • Empirical studies concerning exchange rate pass-through • The Data • The VAR approach • The single equation approach • Conclusions • References Dissertation paper outline
The importance of exchange rate pass-through Exchange rate pass through - “the percentage change in local currency import prices resulting from a one percent change in the exchange rate between the importing and the exporting countries” (Goldberg and Knetter (1997)) – the change in import prices is passed to some extent into producer and consumer prices Taylor (2000)-importance in the conduct of monetary policy because of its impact on inflation forecasts. Countries that experience high exchange rate pass-through tend to put more emphasis on exchange rate in the conduct of their monetary policy- especially emerging and transition countries. Pass through has an important role in EU acceding countries which will face additional constraints because of ERMII criteria. Edwards(2006)-a high pass-through into nontradable goods prices reduces the effectiveness of the exchange rate, while a high pass through into tradable goods prices will enhance its effectiveness.
The aims of the paper • To quantify the the size and speed of the exchange rate pass through into inflation; • To test whether the size of the pass through is dependant on the currency chosen as the base currency; • To determine the variables which account for inflation variability; • To determine whether the size of the pass through has declined in time; • to test if exchange rate volatility influences the size of the pass through; • to check for asymmetries in the exchange rate pass-through.
Empirical studies concerning exchange rate pass-through • Single equation method: all studies before 1995, Goldberg and Knetter (1997), Campa and Minguez (2002), Campa, Goldberg and Minguez (2005), Elkayam (2004), Edwards (2006). • VAR method and cointegration analysis: Kim (1998), McCarthy (2000), Hahn(2003), Leigh and Rossi(2002), Gueorguiev(2003), Billmeier and Bonato (2002), Coricelli, Jazbec, Masten (2004), Huefner and Schroeder (2002), Arnostova and Hurnik (2004). • Structural models – usually developed by central banks – Quartely Projection Models – Gagnon Ihrig (2004).
Adjusting the empirical analysis for the characteristics of the Romanian economy • including a central bank reaction function in the model may • prove useless as NBR has only recently adopted the interest rate as operating target • import prices are only available on a quarterly basis, so an analysis using • these prices isn’t possible in a model using monthly data • itwould be more useful to replace CPI based inflation with the inflation • computed using the CORE1 price index
The Data Monthly data series for the period of 2000M1:2006M2 The Gap of the real Production Index (GAP_IPR)– obtained by deflating the PI with the PPI and then applying a Hodrick-Prescott filter - I(0) The first difference of the log RON/EUR exchange rate (DEURM) – I(0) The first difference of the log RON/USD exchange rate (DUSDM) – I(0) The first difference of a basket currency computed as 65% EUR and 35% USD (weights given by the proportion of the imports denominated in EUR, respectively in USD) (DBASKET) – I(0) The first difference of the log PPI – seasonally adjusted using the Tramo-Seats procedure in Demetra (INFL_PPI_SA)- I(0) The first difference of the log Core1 index –seasonally adjusted using the Tramo-Seats procedure in Demetra (INFL_CORE1_SA)- I(0) The first difference of the log HICP seasonally adjusted using the Tramo-Seats procedure in Demetra (DHICP)– I(0) The first difference of the log broad money aggregate (DM2)– I(0) For alternative specifications: the monetary policy interest rate, the first difference of the log of gross nominal wage
Results of VAR approach Lag length criteria suggests for each model a specification including 1 lag The VAR approach uses the impulse response functions to analyse the pass through Speed of pass through: number of periods after which the PPI based inflation and Core1 inflation revert to their long run levels Size of pass through: Where Pt is the cumulative response of the inflation to a standard deviation shock in the exchange rate innovation; Et is the cumulative response of the exchange rate to a standard deviation shock in the exchange rate innovation. Short run pass through: t=1 Long run pass through: . In practice, we stop when the ratio stabilises.
Model1 Model1 Model2 Model2 Model3 Model3 RON/EUR exchange rate The initial shock in the exchange rate works through the system in about 12 periods – if we consider confidence intervals even less The speed of pass through RON/USD exchange rate
RON/EUR exchange rate The size of pass through
The size of pass through • the pass-through in PPI based inflation is consistently greater than in Core1 inflation. This is due to: • the size of the pass through depends on the weight of the goods and services in the price index that are affected by the exchange rate shock • the number of stages that a shock has to pass is also important because at each stage the pass-through is incomplete • adding the first difference of the broad money aggregate seriously improves the log likelihood and the Akaike Information Criteria also decreases • adding the nominal gross wage to the model or removing it has no impact on the estimation • including the monetary policy rate as endogenous variable: very weak responses of both PPI and CORE1 inflation to any shocks, high persistence of the monetary policy interest rate, all the coefficients in the monetary policy interest rate equation are highly insignificant with the sole exception of the monetary policy interest rate itself • the ordering of the variables is an issue of discussion, especially the position of DM2 in the ordering of the variables-reordering the variables proves insignificant for the speed and size of pass through but significant for variance decomposition
RON/USD exchange rate The size of pass through
RON/basket exchange rate The size of pass through
The size of pass through-Conclusions • the RON/USD exchange rate pass-through is systematically smaller than the RON/EUR exchange rate pass-through. This can be explained by the fact that the estimation sample contains a longer period of EUR reference on the FOREX market • the pass-through coefficients for the basket are somehow in between those previously obtained, but a bit biased towards the estimates obtained for the RON/EUR exchange rate. • The model that exhibits the most economically consistent impulse response functions and has the highest log likelihood and the lowest AIC is model 4, followed by model 3 – we can test alternative specifications of the model using the block-causality test
Model 1 Variance Decomposition for CORE1 inflation Model 2 Model 3 Model 4
Variance Decomposition for CORE1 inflation –Remarks • high persistence of CORE1 inflation in all models, because the most important variable in explaining its variance, even after 10 periods, is the CORE1 inflation itself • The second most important variable that explains the variance of CORE1 inflation is the movements in the exchange rate • Third most important variable is euro zone inflation • Using an alternative specification of the VAR (DM2 comes immediately after the supply and demand shocks), we obtain thatDM2 has a greater explanatory power for the inflation reaching 10% after 10 periods • the importance of the exchange rate movement is lower in case of the alternative specification
Recursive estimation Rolling window estimation The stability of pass through coefficients Recursive estimation – starting sample: 44 observations – 30 recursive estimations Rolling window – fixed sample: 54 observations – 20 windows Clear evidence for declining pass-through coefficients – Taylor(2000)- countries with decreasing rate of inflation also experience a decline in the size of the pass through
Cointegration Analysis First specification for cointegration analysis: LEURM, LPPI, LCORE1: one cointegrating equation – the statistic of LEURM in the cointegrating vector highly unsignificant
Cointegration Analysis Second specification for cointegration analysis: LCORE1, LPPI, LEURM, LHICP : one cointegrating equation – the coefficients in the cointegration equation statistically significant, but economically incorrect
Short run pass through coefficients: Long run pass through coefficients: The single equation approach In order to allow for asymmetries I added another term in each of the above equations: First specification Second specification Where app is a dummy variable that selects depreciations of the domestic currency above a certain threshold
E views specification – estimation method Seemingly Unrelated Regressions d(lipp)=c(11)+c(12)*deurm+c(13)*d(lipp(-1))+c(14)*d(lhicp) d(lcore1)=c(21)+c(22)*deurm+c(23)*d(lcore1(-1))+c(24)*d(lhicp) The single equation approach -results • The coefficients of Euro zone inflation are statistically insignificant – this may be because it influences our economy with a number of lags • The correlation between resulting residuals very low 0.1039 • No autocorrelation in the residuals
Short run and long run pass through coefficients The single equation approach -results -The results are very similar to those obtained through the VAR estimation in case of model 4. -lower pass through into CORE1 inflation than in PPI based inflation
Short run pass though Long run pass though The single equation approach –the stability of the coefficients Using the rolling window technique the following results are obtained: -The short run pass through coefficients fluctuate across the sample with the pass through into PPI based inflation ranging between 0.22 and 0.25 and with the pass through into CORE1 inflation ranging between 0.11 and 0.07 -The long run pass through coefficients are clearly far from stable and seem to be systematically decreasing. The pass through into PPI based inflation ranges between 0.41and 0.24 , while the pass through into CORE1 inflation ranges between 0.33 and 0.17
E views specification The single equation approach –Including the volatility d(lipp)=c(11)+c(12)*deurm+c(13)*d(lipp(-1))+c(14)*d(lhicp)+c(15)*volatility d(lcore1)=c(21)+c(22)*deurm+c(23)*d(lcore1(-1)) +c(24)*d(lhicp) +c(25)*volatility The series of monthly exchange rate volatility starting from daily appreciation/ depreciation of the RON with respect to EUR; a rolling GARCH(2,1) model was fitted on the daily data. Monthly variance was retrieved by adding daily variances as the covariance term isn’t statistically significant. Garch estimation for the whole sample
Estimation results The single equation approach –Including the volatility Pass through coefficients
E views specification d(lipp)=c(11)+c(12)*deurm+c(13)*d(lipp(-1))+c(14)*d(lhicp)+c(15)*abs(deurm) d(lcore1)=c(21)+c(22)*deurm+c(23)*d(lcore1(-1))+ c(24)*d(lhicp) + c(25)*abs(deurm) The single equation approach –testing for appreciation asymmetry The two coefficients allowing for appreciation asymmetry aren’t statistically significant.
E views specification d(lipp)=c(11)+c(12)*deurm+c(13)*app*deurm+c(14)*d(lipp(-1)) +c(15)*d(lhicp) d(lcore1)=c(21)+c(22)*deurm+c(23)*app*deurm+c(24)*d(lcore1(-1)) +c(25)*d(lhicp) Where app is a dummy variable taking the value of 1 for RON appreciation and 0 for RON depreciation. The single equation approach –testing for asymmetry • The result of the above estimation is also inconclusive. In order to examine whether there is another threshold for the movement of the exchange rate, the following estimation was performed: • The interval between the maximum appreciation and the maximum depreciation of the RON was split into equal intervals of 0.001 • A dummy variable d1 was constructed for deurm>a, where a represents each value of the interval • The above specification was estimated each time replacing app with d1 • The coefficients and the corresponding t-statistics were saved in a matrix
The single equation approach –testing for asymmetry The two t statistics have the biggest absolute value (that means that they are the most significant) at point 0.022287, so one could assume that this is the threshold value for the exchange rate change. However, further investigations must be performed using the methodology of Tsay(1998), Hansen(2000), Alessandrini(2003) and Arbatli (2005).
The speed of pass-through: an initial shock in the exchange rate movement completely works through the economy and is passed into producer and consumer prices in 12 months • The size of the pass through varies across the models and across the exchange rates used. The RON/EUR exchange rate pass through into producer prices varies between 0.37 and 0.45 depending on the VAR model, while the pass through into consumer prices varies between 0.30 and 0.37. • The RON/USD exchange rate pass-through is consistently lower than the RON/EUR, ranging between 0.28 and 0.35 for producer prices, and between 0.21 and 0.28 for consumer prices • The basket exchange rate pass-through coefficients are for each model found to be between the coefficient of the RON/EUR pass-through and the RON/USD pass-through • Variance decomposition: The percent of the variance explained by different variables varies across models and for model 4 it also varies across alternative orderings of the variables. It is clear however, that the determinants of inflation are the following: inflation itself (78%-90%), the exchange rate movements(21%-27%), HICP inflation (19%) and the variation of the broad monetary aggregate (2%-10%). Conclusions
Conclusions • Using both recursive and the rolling window estimation sufficiently clear evidence was found that the pass through has declined gradually • The results of the single equation approach are consistent with the ones obtained through the VAR method: the long run pass through into producer prices is equal to 0.37 while the long run pass through into consumer prices is equal to 0.30 • The pass through coefficients computed using the single equation approach were also checked for stability using the rolling window approach – the conclusion is the same: pass through in Romania seems to have decreased in the last period. • Taking into account exchange rate volatility proved in statistically significant in both equations and changes the pass through coefficients: The long run pass through into producer prices becomes equal to 0.29 while the long run pass through into consumer prices changes to 0.24. • Including the appreciation of the exchange rate as a distinct explanatory variable in both equations made no difference - the coefficients are not significant - there probably is no asymmetry around the zero value of the exchange rate change. • The investigation for a non-zero threshold of the exchange rate change showed as marginally significant a 0.022287 depreciation as a threshold value
Amato, J., Filardo, A., Galati,G., von Peter,G., Zhu, F. (2005): “Research on exchange rates and monetary policy: an overview”, BIS Working paper no.179 Arbatli, E. (2003): “Exchange Rate Pass Through in Turkey: Looking for Asymmetries”, Central Bank Review, vol. 3, issue 2, pages 85-124 Brooks, C. (2002) : Introductory Econometrics for Finance , Cambridge University Press Billmeier, A., Bonato, L. (2002): “Exchange Rate Pass-Through and Monetary Policy in Croatia”, IMF Working Paper, 109/2002 Caner, M., Hansen, B. (2004): “Instrumental Variable Estimation of a Threshold Model”, Econometric Theory, 20, 813–843 Coricelli, F., Jazbec B., Masten I. (2004) „Exchange Rate Pass-Through in Acceding Countries”, European University Institute Working Paper no. 2004/16 Choudhri E. U., Faruqee H., and Hakura D. (2002): “Explaining the Exchange Rate Pass-Through in Different Prices”, IMF Working Paper, 224/2002 Campa, J.M, Gonzalez Minguez, J.M. (2002): “Differences in Exchange Rate Pass-Through in the Euro Area”, IESE Working Paper No. D/479 Christiano, L., Eichenbaum, M., Evans, C. (1996) „Sticky Price and Limited Participation Models of Money: A Comparison”, NBER Working Paper no. 5804 Campa, J.M, Goldberg, L., Gonzalez Minguez , J.M. (2005): „Exchange-Rate Pass-Through To Import Prices In The Euro Area”, NBER Working Paper no. 11632 Edwards, S. (2006): “The Relationship Between Exchange Rates And Inflation Targeting Revisited”, NBER Working Paper no. 12163 Enders, W.(2004): „Applied Time Series Econometrics”, John Wiley & Sons References
Engle, C., Devereux M. (2002):”Exchange Rate Pass-Through, Exchange Rate Volatility, and Exchange Rate Disconnect”, NBER working Paper no. 9568 • Gagnon, J., Ihring, G. (2002): “Monetary Policy and Exchange Rate Pass-Through”, Board of Governors of the Federal Reserve System, International Finance Discussion Paper No. 2001/704. • Goldberg, P., Knetter, M (1997): “Goods Prices and Exchange Rates: What have we learned?”, NBER Working Paper no. 5862 • Gueorguiev, N. (2003): “Exchange rate pass through in Romania”, IMF Working Paper, 130/2003 • Hahn, E. (2003): “Pass Through of External Shocks to Euro Area Inflation”, ECB Working Paper, no.243 • Hufner, F.P, Schroder, M. (2002): “Exchange Rate Pass Through to Consumer Prices: A European Perspective”, Centre for European Economic Research Discussion Paper no. 02-20 • Leigh, D., Rossi M.(2002): “Exchange Rate Pass-Through in Turkey”, IMF Working Paper no. 2002/204 • McCarthy, J. (2000):”Pass-Through of Exchange Rates and Import Prices to Domestic Inflation in Some Industrialized Economies”, Federal Reserve Bank of New York Working Paper • Rowland, P. (2003): “Exchange Rate Pass-Through to Domestic Prices: The Case of Colombia”Banco de la Republica de Colombia • Sarno, L., Taylor, M. (2002): “The Economics of Exchange Rates”, Cambridge University Press • Sekine, T. (2006): “Time-varying exchange rate pass-through: experiences of some industrial countries”, BIS Working Paper no 202
Taylor,J.B. (2000): “Low Inflation, Pass-Through, and the Pricing Power of Firms” European Economic Review vol. 44, issue 7, pages 1389-1408 • Tsay, R (1998): “Testing and Modeling Multivariate Threshold Models”, Journal of the American Statistical Association , 94, 1188-1202. • Warne, A (1993) “A Common Trends Model: Identification, Estimation and Inference”, Seminar Paper No. 555, IIES, Stockholm University