How Synchronized is the Mena Region with Advanced Economies? Evidence from an Autoregressive Distributed Lag Models

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Abstract

This paper explores the patterns of aggregate cyclical behavior within the Middle East and North Africa region and among this region and the major industrialized economies. We seek to determine how the volatility and the symmetry of MENA countries have been modified following their recent liberalization initiatives. In particular, we ask the broad question of how far the MENA zone can “couple” with the most developed economies and maintain a relative synchronicity with the world business cycle over 1970-2010. The Hodrick-Prescott filter is applied to decompose the real GDP of these countries and obtain the resulting series of cyclical components. These are compared at different time horizons: the contemporary, short-term, and long-term. Two approaches are used: a static one, based on properties of variability, co-variation and correlation, and a dynamic one, based on long-term relationships using an autoregressive distributed lag models and short-term dynamics using an error correction models. A long-term convergence between the MENA, the G7, the European, and the Anglo-Saxon cycles is confirmed particularly during 1989-2010, period under which the MENA countries have engaged an important economic integration process. This could denote a coupling of the region with the industrialized nations. The idiosyncratic cycles of the MENA countries are closely associated with the G7 experience, especially, in long term. While the European cycle has an important effect on the North African countries, the Middle East region is rather more dominated by the Anglo-Saxon zone.

[1] Duarte, A. Holden, K, 2003, The business cycle in the G-7 economies. International Journal ofForecasting, 19, 685-700.

[2] Artis, M.J, Zenon, G.K., Denise, R.O., 1997, Business Cycles for G7 and European Countries. Journal of Business, 70, 249-279.

[3] Chan, Tze-Haw, Lau, Evan, 2007, Business cycles and the synchronization process: a bounds testing approach. Munich Personal RePEc Archive, n° 2053, November.

[4] Engle, Granger, 1987, Co-integration and Error Correction: Representation, Estimation, and Testing. Econometrica, vol. 55, issue 2, 251-76.

[5] Hassler, U., Wolters, J.A., 2006, Autoregressive Distributed Lag Models and Cointegration. Allgemeines Statistisches Archiv, Volume 90, N°1 / March.

[6] Inklaara, R., Jong-A-Pina, R., De Haan, J., 2008, Trade and business cycle synchronization in OECD countries-A re-examination. European Economic Review, 52, 646-666

[7] Johansen, S., Juselius, K., 1990, Maximum likelihood estimation and inference on cointegration with application to the demand for money. Oxford Bulletin of Economics and Statistics, 52.

[8] Keele, L., De Boef, S., 2004, Not Just for Cointegration: Error Correction Models with Stationary Data. Department of Politics and International Relations, Nuffield College and OxfordUniversity

[9] Monfort, A., Renne, J.P., Ruëffer, R., Vitale, G., 2004, Is economic activity in the G7 synchronised? Common shocks vs. spillover effects. CEPR Discussion Paper, n° 4119

[10] Pesaran, M.H., Shin, Y., 1996, An autoregressive distributed lag modelling approach to cointegration analysis. Cambridge University: Department of Applied Economics.

[11] Pesaran, M.H., Shin, Y., Smith, R., 2000, Structural analysis of vector error correction models with exogenous I(1) variables. Journal of Econometrics, 97, 293-343

[12] Pesaran, M.H., Shin, Y., Smith, R., 2001, Bounds Testing Approaches to the Analysis of Level Relationships. Journal of Applied Econometrics, 16(3), 289-326.

[13] Pesaran, M.H., Shin, Y., Smith, R.J., 1996, Testing for the Existence of a Long Run Relationship. DAE Working Paper, N° 9622, Cambridge University.

Annals of the Alexandru Ioan Cuza University - Economics

The Journal of "Alexandru Ioan Cuza" University from Iasi

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