Long run and cyclical strong dependence in macroeconomic time series: Nelson and Plosser revisited
AbstractThis paper deals with the presence of long range dependence at the long run and the cyclical frequencies in macroeconomic time series. We use a procedure that allows us to test unit roots with fractional orders of integration in raw time series. The tests are applied to an extended version of Nelson and Plosserâs (1982) dataset, and the results show that, though the classic unit root hypothesis cannot be rejected in most of the series, fractional degrees of integration at both the zero and the cyclical frequencies are plausible alternatives in some cases. Additionally, the root at the zero frequency seems to be more important than the cyclical one for all series, implying that shocks affecting the long run are more persistent than those affecting the cyclical part. The results are consistent with the empirical fact observed in many macroeconomic series that the long-term evolution is nonstationary, while the cyclical component is stationary.
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Bibliographic InfoArticle provided by Springer in its journal Empirica.
Volume (Year): 34 (2007)
Issue (Month): 2 (April)
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Web page: http://www.springerlink.com/link.asp?id=100261
Fractional integration; Cyclical behaviour; Long memory; C22;
Other versions of this item:
- Luis A. Gil-Alana, . "Long run and cyclical strong dependence in macroeconomic time series. Nelson and Plosser revisited," Faculty Working Papers 17/06, School of Economics and Business Administration, University of Navarra.
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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- Guglielmo Maria Caporale & Juncal Cunado & Luis A. Gil-Alana, 2008. "Modelling Long-Run Trends and Cycles in Financial Time Series Data," CESifo Working Paper Series 2330, CESifo Group Munich.
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