Growth and Employment in Europe: Sustainability and Competitiveness, Department of Economics, WU (Wirtschaftsuniversität Wien)
Random Walks and Non-Linear Paths in Macroeconomic Time Series: Some Evidence and Implications
() and Adriaan van Zon
Abstract: This paper investigates whether the inherent
non-stationarity of macroeconomic time series is entirely due to a random
walk or also to non-linear components. Applying the numerical tools of the
analysis of dynamical systems to long time series for the US, we reject the
hypothesis that these series are generated solely by a linear stochastic
process. Contrary to the Real Business Cycle theory that attributes the
irregular behavior of the system to exogenous random factors, we maintain
that the fluctuations in the time series we examined cannot be explained
only by means of external shocks plugged into linear autoregressive models.
A dynamical and non-linear explanation may be useful for the double aim of
describing and forecasting more accurately the evolution of the system.
Linear growth models that find empirical verification on linear econometric
analysis, are therefore seriously called in question. Conversely non-linear
dynamical models may enable us to achieve a more complete information about
economic phenomena from the same data sets used in the empirical analysis
which are in support of Real Business Cycle Theory. We conclude that Real
Business Cycle theory and more in general the unit root autoregressive
models are an inadequate device for a satisfactory understanding of
economic time series. A theoretical approach grounded on non-linear metric
methods, may however allow to identify non-linear structures that
endogenously generate fluctuations in macroeconomic time series.
Keywords: Random Walks, Real Business Cycle Theory, Chaos; (follow links to similar papers)
JEL-Codes: C22; E32; (follow links to similar papers)
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