Time-Varying Parameters and the Out-of-Sample Forecasting Performance of Structural Exchange Rate Models

Bibliographic Data

ID19418246
AuthorsChristian C P Wolff (0000-0003-2706-8120, London Business School, corresponding author)
Year1987
Volume5
Issue1
Pages87-97
Publication date1987-01-01
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueJournal of Business and Economic Statistics (JOURNAL)
Journal identifiersISSN: 0735-0015 • E-ISSN: 1537-2707
PublisherInforma UK Limited (PUBLISHER • GB)
DOI10.1080/07350015.1987.10509563
OpenAlexW1975659328
LanguageEN
Citations received5
References cited18

Varying-parameter estimation techniques based on recursive application of the Kalman filter are used to improve the predictive performance of a class of monetary exchange rate models. I find that allowing estimated parameters to vary over time enhances the models' forecasting performance for the dollar–pound, dollar–mark, and dollar–yen exchange rates. Contrary to earlier results in the literature, ex-post forecasts for the dollar-mark rate compare favorably with those obtained from the naive random walk forecasting rule

Econometrics · Economics · Exchange rate · Kalman filter · Liberian dollar · Monetary economics · Pound Sterling · Random walk · Statistics · Us dollar · Computer Science · Financial Risk and Volatility Modeling · Market Dynamics and Volatility · Mathematics · Monetary Policy and Economic Impact

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