ESDS Conference London November 2006

1. ESDS ConferenceLondonNovember 2006 A Cointegration Analysis of EMU Convergence of the CEEC5 EU Accession Countries ANDREY DAMIANOV MSc FCCA MBA Oxford Brookes University

2. The Background • In the last 20 years in Europe two major sets of events determine the future of the continent: - the rapid European unification - the “changes” in Central & Eastern Europe CEE • The unification depends on: political will and economic feasibility, as the economic feasibility matters a lot especially in times of possible decrease of political will

3. The Background (contd.) • The creation of the European Monetary Union (EMU) is a major step contributing towards the economic viability of the European unification • The EMU, however has its supporters and critics at academic and political levels • 8 CEECs joined the EU on 1st May 2004 • Two more CEECs are very close to accession. More countries to follow future EU accession

4. The 2 Main Lenses • The classic theory on Optimum Currency Areas (OCA) and its developments • The political environment: the future Euro adoption is a pre-condition for EU membership of the new EU members CEECs. Compliance to the Maastricht criteria is obligatory at the time of EMU accession

5. Convergence – real or nominal? Between which countries? • Nominal macroeconomic variables of economic activity (Maastricht criteria) • Real macroeconomic variables of economic activity • CEEC 5 – Poland, Czech Republic, Hungary, Slovenia and Estonia • Vis-à-vis Euro-zone and between themselves

6. The Current Econometric Task • Within the traditional time series econometrics methodology to investigate the long-run behaviour and by using method based on the Johansen (1988) multivariate cointegration approach (and its developments) to estimate the number of cointegrating vectors amongst the countries (within a group) for some of the nominal and real macroeconomic activity variables. To analyse the results within a specific interpretative framework.

7. Methodology • Testing for order of integration (DF and ADF tests) based on the principles in Dickey and Fuller (1979, 1981) and developments; in some cases data break tests (Perron (1989) test, specifically one of the models as listed by Enders (2004)) • For the I(1) series -> Cointegration tests (Johansen multivariate cointegration method). Estimating the number of cointegrating vectors in VECM models, which may include information (dummy variables) from the order of integration and data break tests • Interpretative framework (as adopted by Haffer and Kutan(1994), and Haug at al. (2000))

8. Data and Data Preparation • Period: 1995 – 2004/5 • Frequency: Quarterly observations • Groups of countries: Visegrad 3 (Poland, Hungary, Czech Republic), Visegrad 3 and EMU, CEEC5 (Visegrad 3 and Estonia and Slovenia), CEEC5 and EMU

9. Data and Data Preparation (contd.) • Macroeconomic Variables Included: Nominal: Inflation, Interest Rates, Nominal Exch. Rates, Real Exch. Rates Real: Business Cycle, Unemployment Rate, De-trended Unemployment Rate • Time Series Used: CPI, Nominal Exch.Rates, Interest Rates (3 months), GDP, Unemployment Rate

10. Data and Data Preparation (contd.) • Availability of data from one source: ESDS • Sources/databases used through ESDS: IMF - IFS OECD – Main Economic Indicators Eurostat New Cronos Easy to search and use !

11. Data and Data Preparation (contd.) • Some difficulties (at the time of data download spring/summer 2005): unavailability of quarterly data for some countries and variables for the period needed • For example: * Long-term interest rates not available, hence 3 monthly interest rates used * HCPI not available for the whole period, hence CPI used * Budget deficit as % of GDP, and Gov.Debt as % of GDP not available at quarterly observations

12. Data and Data Preparation (contd.) • For the same macroeconomic variable some series were not fully available for all countries within the same database, hence they had to be taken from different sources (databases) within the ESDS • In some cases seasonal adjustment and/or de-trending had to be done, as seasonally adjusted or de-trended series were not available

13. First Results (work-in-progress) • Estimations made by using Microfit 4.0 • Many of the macroeconomic variables series are ~ I(1) • Level of convergence: depending on the group of countries and variables it varies from ‘no convergence’ through ‘partial convergence’ to ‘full convergence’ in some cases • The work continues !

14. Questions and Answers

15. Thank you !

16. References • Dickey, D.A. and Fuller, W.A. (1979), ‘Distribution of the Estimators for Autoregressive Time Series with a Unit Root’, Journal of the American Statistical Association, Vol. 74, Number 366 Theory and Methods Section, pp. 427-431 • Dickey, D.A. and Fuller, W.A. (1981), ‘Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root’, Econometrica, Vol. 49, No. 4 (July, 1981), pp. 1057-1072 • Enders, W. (2004), Applied Econometric Time Series (2nd edn), John Wiley & Sons • Haug, A.A., MacKinnon J.G. and Michelis, L. (2000), ‘European Monetary Union: A Cointegration Analysis’, Journal of International Money and Finance, 19, pp. 419-432 • Haffer, R.W. and Kutan, A.M. (1994), ‘A Long-Run View of German Dominance and the Degree of Policy Convergence in the EMS’, Economic Inquiry, Vol. XXXII, (October, 1994), pp. 684-695 • Johansen S. (1988), ‘Statistical Analysis of Cointegration Vectors’, Journal of Economic Dynamics and Control, 12, pp.231-254 • Perron, P. (1989), ‘The Great Crash, the Oil Price Shock, and the Unit Root Hypothesis’, Econometrica, Vol. 57, No. 6 (November, 1989), pp. 1361-1401