Accessible Unlicensed Requires Authentication Published by De Gruyter Oldenbourg July 6, 2017

Slow Booms and Deep Busts: 160 Years of Business Cycles in Spain

Thomas Gries, Marlon Fritz and Yuanhua Feng
From the journal Review of Economics

Abstract

This paper introduces an economically important new idea for detrending macroeconomic time series and examines the Spanish business cycle pattern with respect to potential asymmetries. To address difficulties in the trend and cycle decomposition, a nonparametric trend estimation approach is introduced and exemplary applied to the Spanish GDP data for the period 1850 to 2015. The application of an iterative plug-in (IPI) algorithm for endogenous bandwidth selection solves the problem of choosing an adequate smoothing parameter for nonparametric regression. The algorithm identifies continuously Moving Trends (MT) with a time length of 34 years. After we estimate the trend nonparametrically, we fit several time series models to the residuals for further analysis. Although asymmetry during expansion and recession phases is indicated, it is not unambiguous.

JEL Classification: E32; C14; C51

Acknowledgments

We would like to thank the anonymous referee for helpful comments and suggestions that clearly improve the paper.

Appendix A

Table 3:

Estimated AR(p) models for the standardized Spanish GDP growth rates.

Series Coeff.Spain GDP
ModelAR(1)AR(2)AR(3)AR(4)AR(5)
ϕˆ00.00020.00010.00010.00000.0000
(0.0035)(0.0030)(0.0026)(0.0021)(0.0019)
ϕˆ10.00540.0059–0.0155–0.0427–0.0652
(0.0778)(0.0767)(0.0772)(0.0760)(0.0774)
ϕˆ2−0.1653**−0.1674**−0.1998***−0.2138***
(0.0766)(0.0759)(0.0754)(0.0757)
ϕˆ3−0.1275*−0.1311*−0.1518**
(0.0768)(0.0750)(0.0761)
ϕˆ4−0.2111***−0.2158***
(0.0754)(0.0751)
ϕˆ5−0.1032
(0.0768)
σ2ˆ0.00200.00200.00190.00180.0018
Log-likelihood278.08280.38281.75285.56286.46
AIC−550.17−552.76−553.49−559.13−558.92

  1. Notes: Model parameters, standard errors in parentheses and p-values, *p(z)<0.1, **p(z)<0.05, ***p(z)<0.01.

References

Beaudry, P. and G. Koop (1993): Do Recessions Permanently Change Output?, Journal of Monetary Economics 31, 149–163. Search in Google Scholar

Beran, J. and Y. Feng (2002): Local Polynomial Fitting with Long-Memory, Short Memory and Antipersistent Errors, Annals of the Institute of Statistical Mathematics 54, 291–311. Search in Google Scholar

Beveridge, S. and C. R. Nelson (1981): A New Approach to Decomposition of Economic Time Series into Permanent and Transitory Components with Particular Attention to Measurement of the Business Cycle, Journal of Monetary Economics 7, 151–174. Search in Google Scholar

Burns, A. F. and W. C. Mitchell (1946): Measuring Business Cycles. NBER Books, Cambridge, MA. Search in Google Scholar

Cochrane, J. H. (1988): How Big Is the Random Walk in GNP?, Journal of Political Economy 96, 893–920. Search in Google Scholar

Gasser, T., A. Kneip and W. Köhler (1991): A Flexible and Fast Method for Automatic Smoothing, Journal of the American Statistical Association 86, 643–652. Search in Google Scholar

Hamilton, J. D. (1989): A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle, Econometrica 57, 357–384. Search in Google Scholar

Hansen, B. E. (1996): Inference When a Nuisance Parameter Is Not Identified under the Null Hypothesis, Econometrica 64, 413–430. Search in Google Scholar

Hansen, B. E. (1997): Inference in TAR Models, Studies in Nonlinear Dynamics & Econometrics 2, 1–16. Search in Google Scholar

Hicks, J. R. (1950): A Contribution to the Theory of the Trade Cycle. At the Clarendon Press, Oxford. Search in Google Scholar

Keynes, J. M. (1936): The General Theory of Employment, Interest and Money. Macmillan, London. Search in Google Scholar

Lopes, A. S. and G. F. Zsurkis (2015): Revisiting Non-Linearities in Business Cycles around the World. Munich Personal RePEc Archive No. 65668. Search in Google Scholar

Mitchell, W. C. (1927): Business Cycles: The Problem and Its Setting. NBER, New York. Search in Google Scholar

Morley, J. C. and J. Piger (2012): The Asymmetric Business Cycle, The Review of Economics and Statistics 94, 208–221. Search in Google Scholar

Perron, P. and T. Wada (2009): Let’s Take a Break: Trends and Cycles in US Real GDP, Journal of Monetary Economics 56, 749–765. Search in Google Scholar

Potter, S. M. (1995): A Nonlinear Approach to US GNP, Journal of Applied Econometrics 10, 109–125. Search in Google Scholar

Prados De La Escosura, L. (2017): What Was Spain’s GDP Then?, Measuring Worth, (2017 accessed February 28, 2017). Search in Google Scholar

Ramsey, J. B. and P. Rothman (1996): Time Irreversibility and Business Cycle Asymmetry, Journal of Money, Credit and Banking 78, 1–21. Search in Google Scholar

Ruppert, D., S. J. Sheather and M. P. Wand (1995): An Effective Bandwidth Selector for Local Least Squares Regression, Journal of the American Statistical Association 90, 1257–1270. Search in Google Scholar

Samuelson, P. A. (1939): Interactions between the Multiplier Analysis and the Principle of Acceleration, Review of Economic Statistics 21, 75–78. Search in Google Scholar

Sichel, D. E. (1993): Business Cycle Asymmetry: A Deeper Look, Economic Inquiry 32, 224–236. Search in Google Scholar

Tong, H. (1978): On a Threshold Model, in: C. H. Chen (ed.) Pattern Recognition and Signal Processing. Sijhoff & Noordhoff, Amsterdam. Search in Google Scholar

Tong, H. (1983): Threshold Models in Nonlinear Time Series Analysis. Time Series Analysis. Springer Verlag, Berlin. Search in Google Scholar

Published Online: 2017-7-6
Published in Print: 2017-9-26

© 2017 Oldenbourg Wissenschaftsverlag GmbH, Published by De Gruyter Oldenbourg, Berlin/Boston