The Impact of External Shocks on Emerging Countries, The Case of Romania

1. The Impact of External Shocks on Emerging Countries, The Case of Romania MSc Student: POPA LACRAMIOARA SUPERVISOR:Professor MOISA ALTAR

2. Topics • Motivation • Objectives • Literature review • FAVAR approach • The asymmetry of external shocks,(TVAR) • Conclusions

3. Motivation • Because of the perspective of the integration in the Europeanand Monetary Union it is important to establish a correct assessment of the impact of the euro area shocks on the Romanian economy • Moreover the financial crisis reflect the fact that the monetary authority should take into account the external disturbances when designing the monetary policy because even if Romania had important gains in which concern the reduction in inflation, reduction of budget deficit and unemployment it is still small open economy vulnerable to external disturbances originating from more developed countries

4. Objectives • This paper has to major purposes: • to establish if the external originating from the euro area are transmitted to the Romanian economy and their magnitude • I have considered three kind of shocks: monetary policy shocks, demand shocks and supply shocks • finding possible asymmetric effects of a negative and a positive shock.

5. Literature review • The study of the open economies started with the work of Mundell(1962,1963),Dornbusch(1976), Obsfeld anf Rogoff(1995). • Canova (2005) uses a Vector autoregressive model identified using sign restrictions to study the effects of US shocks on several countries from the Latin America • Persman and Smets (2001) effects of an unexpected change in monetary • policy in the euro area using an identified VAR model alternative identification • scheme • Dawit Senbet(2008) uses a Factor augmented VAR model on a wide range of • macroeconomic indicators for several industrialized countries. He findes that • model with factors eliminates the “price puzzle” response evident in the • standard VAR models for all countries

6. Econometric methodology -FAVAR • The model consists of two blocks, one for the Romanian economy which is named “domestic” and one for the Euro area, which is named ‘foreign’ . • Let Yt be an M ×1 vector of observable economic variables from the foreign block assumed to affect the dynamics of the variables from the Romanian economy • Additional economic information, not fully captured by the Yt, may be relevant to modeling the dynamics of these series. Suppose that this additional information can be summarized by an K ×1 vector of unobserved factors, Ft, where K is “small”.

7. Econometric methodology -FAVAR -NxK-matrix of factor loadings -NxM-matrix of factor loadings ν~N(0, Q) e ~ N(0, R) -vector of error terms -Kx1-vector of unobserved factors -Mx1-vector ofobservable factors -Nx1-informational time series

8. 2.1Transform the model into the following state-space form: -measurement equation -transition equation Define:

9. -where Λ is the loading matrix from the measurement equation -joint posterior density

10. 2.2 The Gibbs sampling methodology -it implies following three steps 1 First, choose a set of starting values for the parameters θ , say 2 Second, conditional on and the data draw a set of values for from the conditional density Say- 3 Third, conditional on the sampled values of and the data, draw a set of values of the parameters θ

11. 1. Choice of I used the principal components method 2 Drawing from the conditional distribution • It can be expressed as the product of conditional distributions of factors at each date t as follows • I use Kalman filter to estimate the unobservable factors

12. 3 Drawing from the conditional distribution Define: 1.Rgression equation Bayes theorem

14. 2 VAR model The Minnesota Prior – conditions They are based on an approximation which leads to great simplifcations in prior elicitation and computation , The original Minnesota prior simplifies even further by assuming Σ to be a diagonal matrix. -each equation of the VAR can be estimated one at a time and we can set i (where is the standard OLS estimate of the error variance in the ith equation and is the iith element of ) For the prior mean of , Min, the Minnesota prior involves setting most or all of its elements to zero , except for the elements corresponding to the first own lag of the dependent variable in each equation these elements are set to one.

15. -constructing diagonal elements of so that the prior variance of parameter on k lagged j'th variable in i'th equation equals

16. Data description • The analysis is based on monthly data covering the period 2000M01-2010M02. The sources of data are Eurostat, The National Bank of Romania and National Institute of Statistics. • For each of the two blocks I collected data on real activity, inflation, money and interest rates. • For real activity, I considered data on output growth, employment, imports and exports. • Inflation is measured including the consumer price index on different category of goods (alimentary, nonalimentary) services, • interest rates include ROBOR on different maturities, 3M EURIBOR and monetary policy interest rate.

17. Data description • The data were seasonally adjusted using European Union program Demetra and were normalized considering 2005=100. • Because the number of variables used in the model is very high I included their description in the Appendix A of the paper where are also specified the results of the unit root tests

18. FAVAR – Estimation Results Impulse response to a foreign monetary policy shock

19. Impulse response to a foreign monetary policy shock

20. Impulse response to a foreign supply shock

21. Impulse response to a foreign supply shock

22. Impulse response to a foreign demand shock

23. Impulse response to a foreign demand shock

24. 2.The asymmetry of external shocks,(TVAR) A general TAR model, that permits the existence of two regimes and more than one lag, may be written as : if if -where I() is an indicator function which takes the value of 1 if the logical statement is satisfied and 0 otherwise Variables : HICP,EUR/RON exchange rate, the consumer price index, 3M ROBOR, volume of loans in national currency, volume of loans in devises, the index of industrial production

25. TVAR-Empirical results Asymmetric effects of monetary policy shocks • The reduction inflation in the euro area can be explained by a reduction in the real activity • To stimulate the economy, the Central Bank reduces the monetary policy interest rate and the demand for the national currency will increases, determinating an appreciation of the national currency. • The difference between the response of the exchange rate to a positive shock and a negative shock can be explained by the opportunistic behavior of importers who try to take advantage of the decrease in European prices causing the appreciation of the national currency • The difference in response in consumer prices can be explained by the behavior of producers who try to maintain their market share while in the case of depreciation firms absorb a part of the inflationary impact. • The response of the industrial production to european monetary policy shock is much bigger in the case of negative shock. An explenation for this may be the fact that usually, negative shocks have bigger effects on emerging markets, because of their dependencies on exports in covering the current account deficits

26. TVAR, 2000 (3) - 2010 (1) L_HICP_EU DLCS_EUR 5 DL_CPI DROBOR3M DL_CRNGR DL_CRNGV 0 DL_PI -5 -10 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Probabilities of Regime 1 1.0 0.5 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Probabilities of Regime 2 1.0 0.5 2010 2001 2002 2003 2004 2005 2006 2007 2008 2009 TVAR(positive and negative monetary policy shocks- Probabilities of the two regimes 􀜛 -it presents the periods in which each of the two regimes occurred. Both regimes includes periods of small changes in HICP

27. TVAR-Empirical results Asymmetric effects of demand shocks • An increase in unemployment rate causes a reduction in the real activity of the euro • area,so the Central Bank will adopt an expansionary monetary policy reducing the • interest rates. • An increase of the unemployment rate determines a reduction in the • real activity from the euro area. The EURO/RON exchange rate increases and this • can be explained by the reduction of demand for currencies of the emerging countriesinvestors preferring to buy safer currencies. • The second regime reflects that a reduction in the unemployment rate determines a decrease of the exchange rate. This may happen because of an increase in real activity, leading to an Increse imports which results in the increase of exports for the national economy

28. TVAR, 2000 (3) - 2010 (1) DL_UNEMPL_EUR DLCS_EUR 5 DL_CPI DROBOR3M DL_CRNGR DL_CRNGV 0 DL_PI -5 -10 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Probabilities of Regime 1 1.0 0.5 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Probabilities of Regime 2 1.0 0.5 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 TVAR(positive and negative demand shocks- Probabilities of the two regimes

29. Conclusions • I showed that external innovations play an important role for the evolution of macro-economy variables • External shocks propagate quickly to our economy and their effects are felt for a long period of time, more than one year • Comparing the three shocks we see that the effect of the demand shocks is lower then that of the monetary policy shocks and is transmitted in the economy for a shorter period of time. • An explanation for this may be the fact that financial variables react more quickly to an innovation then the variables referring to real activity for example • Asymmetric effects manifests for all the shocks considered

30. Bernanke, Ben and Jean Boivin, (April 2003) ,“Monetary Policy in a Data-Rich Environment”, Journalof Monetary Economics 50:3, 525-546. • Bernanke, B. S., J. Boivin, and P. Eliasz, ”(versions 2003,2004,2005) “Measuring the effects of monetary policy: A factor-augmented vector autoregressive (FAVAR) approach , Quarterly Journal of Economics 120, 387-422. • Brooks Chris, (2002), “Introductoryeconometrics for finance “,Cambridge University Press, ch 9. • Boivin J.,Serena Ng,(2003)“Are more Data Always Better for Factor Analysis?”, NBER Working Paper 9829. • Belviso F, Milani F,(2003),“Structural Factor-Augmented VAR(SFAVAR)”, Princeton University. • Blaes B.,(2009),“Money and monetary policy transmission in the euro area:evidence from FAVAR and VAR approaches”,Deutsche Bundesbank, Research Centre Discussion PaperSeries 1: Economic Studies No 18 • Canova Fabio,(2005), “ The Transmission of US Shocks to Latin America”, Jurnal of applied econometrics 20: 229–251 • Christiano, Laurence, Martin Eichenbaum, and Charles Evans,(2000), “Monetary Policy Shocks:What Have We Learned and to What End?”, in J. Taylor and M.Woodford, eds.,Handbook of Macroeconomics, Amsterdam: North-Holland • Debortoli D.,Nunes R.,(2008) “The macroeconomic efects of external pressures on monetary policy”, International Finance Discussion Papers No.244 • George Casella; Edward I. George,( Aug., 1992) “Explaining the Gibbs Sampler”, The American Statistician, Vol. 46, No. 3. pp. 167-174 • Gelfand Alan E.; Adrian F. M. Smith “Sampling-Based Approaches to Calculating Marginal Densities”, Jun., 1990,Journal of the American Statistical Association, Vol. 85, No. 410 pp. 398-409 • Hansen Bruce E.,(1996), “Inference in TAR Models”, Boston College Economics Department • HamiltonJD,(1994), ”Time Series Analysis “,Princeton University Press ,ch13

31. Kim, Chang-Jin and Charles R. Nelson,1999, State-Space Models with Regime Switching,Cambridge MA: MIT Press • Korobilis Dimitris “ Assessing the Transmission of Monetary Policy Shocks Using Dynamic Factor Models , Department of Economics, University of Strathclyde; The Rimini Center for Economic Analysis • Koop, G. ,2003, ” Bayesian Econometrics”, Wiley, Chichester ,ch10 • Mumtaz Haroon, Paolo Surico,2007, “The Transmission of International Shocks:A Factor Augmented VAR Approach”, Bank of England • Martin Eichenbaum ,Charles L. Evans,1995, “Some empirical evidence on the effects of shocks to monetary policy of the exchange rates”, Quarterly Journal of Economics No.110 • McCoy D., McMahon M. “Differences in The transmission of Monetary Policy in the Euro-Area:an empirical approach” ,2000,Central Bank & Financial Services Authority of Ireland (CBFSAI) in its series Research Technical Papers No.5/RT/00 • Obstfeld Maurice “International Macroeconomics:Beyond the Mundell-Fleming Model”, July 2001, Department of Economics, University of California, Berkeley • Peersman G.Smets F. ,“The Monetary transmission Mechanism in the Euro area:more evidence from VAR analysis” ,2001,European central bank WP No. 91 • Peek J. and Eric S. Rosengren “The International Transmission of Financial Shocks:The Case of Japan”,1996, Federal Reserve Bank of Boston Working Paper No 96-1 • Ramaswamy R.,Sloek T.“The real effects of monetary policy in The European Union:What are the differences”,1997,IMF Research Department WP No. 160 • Raftery Adrian E.,Lewis S.“How Many Iterations in The Gibbs Sampler”,1991,University of Washington • Senbet D. “Measuring the Impact and International Transmission of Monetary Policy: A Factor-Augmented VectorAutoregressive (FAVAR) Approach”,2008, European Journal of Economics, Finance and Administrative Sciences Issue 13

32. Kalman filter for Prediction Updating is the Kalman gain

33. Thank You!