Transcript ESDS Conference London November 2006 A Cointegration Analysis of EMU Convergence of the CEEC5 EU Accession Countries ANDREY DAMIANOV MSc FCCA MBA Oxford Brookes University The Background • In.

ESDS Conference
London
November 2006
A Cointegration Analysis of EMU
Convergence of the CEEC5 EU
Accession Countries
ANDREY DAMIANOV MSc FCCA MBA
Oxford Brookes University
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
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
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
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
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.
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))
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
Data and Data Preparation (contd.)
• Macroeconomic Variables Included:
Nominal:
Inflation,
Nominal Exch. Rates,
Interest Rates,
Real Exch. Rates
Real:
Business Cycle,
Unemployment Rate,
De-trended Unemployment Rate
• Time Series Used:
CPI,
Interest Rates (3 months),
Unemployment Rate
Nominal Exch.Rates,
GDP,
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 !
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
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 detrending had to be done, as seasonally adjusted or
de-trended series were not available
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 !
Questions and Answers
• Thank you !
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