Building Confidence Intervals for the Band-Pass and Hodrick-Prescott Filters: An Application Using Bootstrapping
AbstractThis article generates innovative confidence intervals for two of the most popular de trending methods: Hodrick-Prescott and Band-Pass filters. The confidence intervals are obtained using block-bootstrapping techniques for dependent data. As an example, we present GDP trend growth and output gap intervals for the G7 economies. This new methodology will increase the usefulness of these filters by overcoming the absence of confidence intervals.
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Bibliographic InfoPaper provided by Central Bank of Chile in its series Working Papers Central Bank of Chile with number 202.
Date of creation: Feb 2003
Date of revision:
Other versions of this item:
- Christian A. Johnson & Francisco A. Gallego, 2003. "Building Confidence Intervals for the Band-Pas and Hodrick-Prescott Filters: An Application using Bootstrapping," Computing in Economics and Finance 2003 15, Society for Computational Economics.
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
- E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
This paper has been announced in the following NEP Reports:
- NEP-ALL-2003-03-25 (All new papers)
- NEP-CMP-2003-03-25 (Computational Economics)
- NEP-DGE-2003-03-25 (Dynamic General Equilibrium)
- NEP-ECM-2003-03-25 (Econometrics)
- NEP-ETS-2003-03-25 (Econometric Time Series)
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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Discussion Papers, Northwestern University, Center for Mathematical Studies in Economics and Management Science
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- Siem Jan Koopman & Kai Ming Lee, 2005. "Measuring Asymmetric Stochastic Cycle Components in U.S. Macroeconomic Time Series," Tinbergen Institute Discussion Papers 05-081/4, Tinbergen Institute.
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