Ajuste Estacional e Integración en Variables Macroeconómicas
AbstractThe importance of separating secular from seasonal movements in macroeconomic data cannot be understated. For policy purposes, filtering the data is of paramount importance both to analyse macroeconomic fluctuations and to model and quantify the responses of the economy to policy shocks. Despite its importance, seasonality is usually considered at best a nuisance that must be removed from the data before its use. Removing seasonal components is, however, not a trivial task. This paper presents evidence that popular methods to remove seasonality are not harmless procedures and that important information is lost in the filtering of the series. Modern techniques suggest, moreover, that these methods alter our understanding of the relationship among macroeconomic variables and in response to policy shocks. In particular, econometric results suggest that most variables present unit roots not only in their long-run component but also at semi-annual and seasonal frequencies. Consequently, the analysis and simulation of policy models undertaken with seasonally adjusted data should be carefully complemented with the analysis of non-filtered data.
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Bibliographic InfoPaper provided by Central Bank of Chile in its series Working Papers Central Bank of Chile with number 73.
Date of creation: Jun 2000
Date of revision:
Other versions of this item:
- Raimundo Soto, 2002. "Ajuste Estacional e Integración en Variables Macroeconómicas," Latin American Journal of Economics-formerly Cuadernos de Economía, Instituto de Economía. Pontificia Universidad Católica de Chile., vol. 39(116), pages 135-155.
- C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- 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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