Modelling Economic Time Series in the Presence of Variance Non-Stationarity: A Practical Approach
AbstractAlthough non-stationarity in the level of a time series is always tested (and there is a variety of tests for this purpose), non-stationarity in the variance is sometimes neglected in applied research. In this work, the consequences of neglecting variance non-stationarity in economic time series, and the conceptual difference between variance non-stationarity and conditional variance are discussed. An ad hoc method for testing and correcting for variance non-stationarity is suggested. It is shown that the presence of variance non-stationarity leads to misspecified univariate ARIMA models and correcting for it, the number of model parameters is vastly reduced. The implications of the tests for the hypothesis of weak form market efficiency (WFME) are discussed. More specifically it is argued that the usual autocorrelation tests are inappropriate when based on the differences of asset prices. Finally, it is shown how the analysis of outliers is affected by the presence of variance non-stationarity.
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Bibliographic InfoPaper provided by Bank of Greece in its series Working Papers with number 07.
Length: 30 pages
Date of creation: Nov 2003
Date of revision:
Publication status: Published in Applied Financial Economics, 2004, 14 (4), pp. 265-278. Titled "The Importance of Variance Stationarity in Economic Time Series Modelling. A Practical Approach".
Applied time series analysis; economic time series; Box-Jenkins modelling; variance non-stationarity; conditional variance; outlier analysis; efficient market hypothesis.;
Find related papers by JEL classification:
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
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