Academic journal article Atlantic Economic Journal

Modeling Cyclical Asymmetries in GDP: International Evidence

Academic journal article Atlantic Economic Journal

Modeling Cyclical Asymmetries in GDP: International Evidence

Article excerpt

Introduction

There is strong support for the business cycle asymmetry hypothesis. According to this view, business cycles consist of a sequence of phases, or regimes, and macroeconomic variables display distinct characteristics within each phase. A variety of theoretical explanations have been offered in regard to the output, such as capacity constraints [Hicks, 1950; Friedman, 1993] or rational fluctuations in the confidence of investors [Potter, 2000b].

Several testing procedures have been proposed to search for asymmetries in the data. Some authors advocate for using nonparametric tests that do not assume any specific model for the series under the alternative. Within this group, one may find tests for the equality of transition probabilities in a Markov chain [Neftqi, 1984], tests for the significance of indicator variables in multiple regressions and in seemingly unrelated regression models [Kontolemis, 1997], tests against unspecified nonlinearities like the BDS test [Brock et al., 1991], etc.

Another approach is to set up tests against a particular nonlinear time series model. If the stochastic properties of the variable are phase-dependent, then the true data generating process is nonlinear, and different types of phase dependence lead to different families of nonlinear models. A number of candidates have been proposed in the literature, see for instance the review articles by Mittnik and Niu [1994] and Potter [1999]. Carrying out the test of symmetric behavior against a well-defined alternative helps to pose the analysis of asymmetries in a structured framework when the null is rejected.

Cyclical asymmetries may arise because of asymmetric disturbances, an asymmetric propagation mechanism, or both. There is not a well-established procedure to determine the actual source of asymmetry, but most empirical work that has addressed on the issue opts for assuming symmetric noise and asymmetric transmission. As a consequence, the best way to characterize output asymmetry is to model how the impulse response function varies over different stages of the cycle. For doing so, an explicit representation of the data generating process is needed, and the modeler has to choose among several types of nonlinear models.

The choice is not straightforward. Although there is a great deal of empirical evidence for cyclical asymmetries, there is not a general pattern that fits all situations. On the contrary, asymmetries seem to be both variable-specific and country-specific. Centering on GDP, first one has to determine how many phases are relevant. The classical division into two phases--expansions and recessions--is too simple, and the recent literature advocates for considering three [Sichel, 1994], four [Emery and Koenig, 1992] and even six [Kontolemis, 1997] phases. Secondly, the number of relevant phases depends heavily on whether classical (level) cycles or growth cycles are considered [Zarnowitz, 1992; Kontolemis, 1997]. In the third place, the major economies have not evolved in the same way. Until recently, business cycles were shorter and more numerous in the United States than in Europe or Japan [Zarnowitz, 1992], and the relevant states of the cycle can not be assumed to be the same anywhere.

This paper aims to model and explain asymmetric behaviour in GDP growth in the USA, Europe, and Japan. Germany and France were chosen to represent the European economies, both because of their size in absolute terms and their role in promoting the European Union. The actual relevance of cyclical asymmetries is set forth by showing how GDP reacts to an exogenous shock, and to what extent the reaction depends on the state of the cycle at the time the shock occurs [Potter, 1994]. By comparing the responses at different countries the common facts are identified and separated from features that are country-specific.

It will be assumed that GDP growth is generated by a smooth transition autoregression (STAR) model [Granger and Terasvirta, 1993]. …

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