Hostname: page-component-cd9895bd7-7cvxr Total loading time: 0 Render date: 2024-12-25T17:03:53.440Z Has data issue: false hasContentIssue false

A TIME-VARYING MARKOV-SWITCHING MODEL FOR ECONOMIC GROWTH

Published online by Cambridge University Press:  10 November 2015

Bruno Morier
Affiliation:
São Paulo School of Economics—FGV
Vladimir Kühl Teles*
Affiliation:
São Paulo School of Economics—FGV
*
Address correspondence to: Vladimir K. Teles, São Paulo School of Economics—FGV, Rua Itapeva 474, São Paulo, SP 01332-000, Brazil; e-mail: vladimir.teles@fgv.br.

Abstract

This paper investigates patterns of variation in economic growth across and within countries using a time-varying transition matrix Markov-switching approach. The model developed here explains the dynamics of growth based on a collection of different states that countries pass into and out of over time; in addition, these states are characterized by their own submodels and growth patterns. The transition matrix among the different states varies over time—depending on the conditioning variables of each country—with a linear dynamic for each state. We develop a generalization of Diebold's EM algorithm and estimate a sample model in a panel with a transition matrix conditioned on institutional quality and the investment level. We find three states of growth: stable growth, miraculous growth, and stagnation. The results show that institutional quality is an important determinant of long-term growth, whereas the investment level plays a variety of roles: it contributes positively in countries with high-quality institutions but is of little relevance in countries with medium- or low-quality institutions.

Type
Articles
Copyright
Copyright © Cambridge University Press 2015 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Aizenman, J. and Spiegel, M. (2007) Takeoffs. Working paper 13084, National Bureau of Economic Research, Inc.CrossRefGoogle Scholar
Alfo, M., Trovato, G., and Waldmann, R.J. (2008) Testing for country heterogeneity in growth models using a finite mixture approach. Journal of Applied Econometrics 23, 487514.CrossRefGoogle Scholar
Ardic, O.P. (2006) The gap between the rich and the poor: Patterns of heterogeneity in the cross-country data. Economic Modelling 23, 538555.CrossRefGoogle Scholar
Bai, J. and Perron, P. (2003) Computation and analysis of multiple structural change models. Journal of Applied Econometrics 18, 122.CrossRefGoogle Scholar
Basturk, N., Paap, R., and van Dijk, D. (2008) Structural Differences in Economic Growth. Discussion paper 08-085/4, Tinbergen Institute.CrossRefGoogle Scholar
Berg, A., Ostry, J.D., and Zettelmeyer, J. (2012) What makes growth sustained? Journal of Development Economics 98, 149166.CrossRefGoogle Scholar
Bloom, D.E., Canning, D., and Sevilla, J. (2003) Geography and poverty traps. Journal of Economic Growth 8, 355378.CrossRefGoogle Scholar
Breiman, L., Friedman, J., Olshen, R., and Stone, C. (1984) Classification and Regression Trees. Monterey, CA: Wadsworth and Brooks.Google Scholar
Canova, F. (2004) Testing for convergence clubs in income per capita: A predictive density approach. International Economic Review 45, 4977.CrossRefGoogle Scholar
Cuberes, D. and Jerzmanowski, M. (2012) Medium-term growth: the role of policies and institutions. In de La Grandville, Olivier (ed.), Frontiers of Economics and Globalization, Vol. 11. Emerald Group Publishing Limited.Google Scholar
Dempster, A.P., Laird, N.M., and Rubin, D.B. (1977) Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society. Series B (Methodological) 39, 138.CrossRefGoogle Scholar
Desdoigts, A. (1999) Patterns of economic development and the formation of clubs. Journal of Economic Growth 4, 305330.CrossRefGoogle Scholar
Diebold, F.X., Lee, J.-H., and Weinbach, G.C. (1993) Regime Switching with Time-Varying Transition Probabilities. Working paper 93-12, Federal Reserve Bank of Philadelphia.Google Scholar
Durland, J.M. and McCurdy, T. (1994) Duration-dependent transitions in a Markov model of U.S. GNP growth. Journal of Business and Economic Statistics 12, 279288.Google Scholar
Durlauf, S.N. and Johnson, P.A. (1995) Multiple regimes and cross-country growth behaviour. Journal of Applied Econometrics 10, 365384.CrossRefGoogle Scholar
Easterly, W., Kremer, M., Pritchett, L., and Summers, L.H. (1993) Good policy or good luck? Country growth performance and temporary shocks. Journal of Monetary Economics 32, 459483.CrossRefGoogle Scholar
Ehrlich, I. and Lui, F.T. (1999) Bureaucratic corruption and endogenous economic growth. Journal of Political Economy 107, S270S293.CrossRefGoogle Scholar
Filardo, A.J. (1994) Business-cycle phases and their transitional dynamics. Journal of Business and Economic Statistics 12, 299308.Google Scholar
Hall, R.E. and Jones, C.I. (1999) Why do some countries produce so much more output per worker than others? Quarterly Journal of Economics 114, 83116.CrossRefGoogle Scholar
Hamilton, J.D. (1990) Analysis of time series subject to changes in regime. Journal of Econometrics 45, 3970.CrossRefGoogle Scholar
Hamilton, J.D. (1994) Time Series Analysis, 1st ed. Princeton, NJ: Princeton University Press.CrossRefGoogle Scholar
Hansen, B.E. (2000) Sample splitting and threshold estimation. Econometrica 68, 575604.CrossRefGoogle Scholar
Hausmann, R., Pritchett, L., and Rodrik, D. (2004) Growth Accelerations. Working paper, John F. Kennedy School of Government, Harvard University.CrossRefGoogle Scholar
Hsieh, C.-T. and Klenow, P.J. (2009) Misallocation and manufacturing TFP in China and India. Quarterly Journal of Economics 124, 14031448.CrossRefGoogle Scholar
Jerzmanowski, M. (2006) Empirics of hills, plateaus, mountains and plains: A Markov-switching approach to growth. Journal of Development Economics 81, 357385.CrossRefGoogle Scholar
Jones, B.F. and Olken, B.A. (2008) The anatomy of start–stop growth. Review of Economics and Statistics 90, 582587.CrossRefGoogle Scholar
Jones, C.I. (2011) Intermediate goods and weak links in the theory of economic development. American Economic Journal: Macroeconomics 3, 128.Google Scholar
Jong-A-Pin, R. and de Haan, J. (2007) Political Regime Change, Economic Reform and Growth Accelerations. CESifo Working Paper Series 1905, CESifo Group Munich.CrossRefGoogle Scholar
Kerekes, M. (2009) Growth Miracles and Failures in a Markov Switching Classification Model of Growth. Discussion Paper 11, Freie Universität Berlin.Google Scholar
Knack, S. and Keefer, P. (1995) Institutions and economic performance: Cross-country tests using alternative institutional measures. Economics and Politics 7, 207227.CrossRefGoogle Scholar
Kourtellos, A. (2002) A Projection Pursuit Approach to Cross Country Growth Data. Working papers in economics 0213, University of Cyprus Department of Economics.Google Scholar
Loh, W.-Y. (2002) Regression Trees with Unbiased Variable Selection and Interaction Detection. Technical report, Statistica Sinica.Google Scholar
Masanjala, W.H. and Papageorgiou, C. (2004) The Solow model with CES technology: Nonlinearities and parameter heterogeneity. Journal of Applied Econometrics 19, 171201.CrossRefGoogle Scholar
Paap, R., Franses, P.H., and van Dijk, D. (2005) Does Africa grow slower than Asia, Latin America and the Middle East? Evidence from a new data-based classification method. Journal of Development Economics 77, 553570.CrossRefGoogle Scholar
Pritchett, L. (2000) Understanding patterns of economic growth: Searching for hills among plateaus, mountains, and plains. World Bank Economic Review 14, 221250.CrossRefGoogle Scholar
Pritchett, L. (2003) A toy collection, a socialist star and a democratic dud: Growth theory, Vietnam, and the Philippines. In Rodrik, Dani (ed.), In Search of Prosperity: Analytical Narratives on Economic Growth. Princeton, NJ: Princeton University Press.Google Scholar
Ruud, P.A. (1991) Extensions of estimation methods using the EM algorithm. Journal of Econometrics 49, 305341.CrossRefGoogle Scholar
Tan, C.M. (2010) No one true path: Uncovering the interplay between geography, institutions, and fractionalization in economic development. Journal of Applied Econometrics 25, 11001127.CrossRefGoogle Scholar
Wacziarg, R. and Welch, K.H. (2003) Trade Liberalization and Growth: New Evidence. Research paper, Graduate School of Business, Stanford University.CrossRefGoogle Scholar
Watson, M. (1983) Alternative algorithms for the estimation of dynamic factor, mimic and varying coefficient regression models. Journal of Econometrics 23, 385400.CrossRefGoogle Scholar