Monitoring Forecasting Combinations with Semiparametric Regression Models
AbstractIn this study, a modelling framework is proposed for evaluating the accuracy of forecasting combinations when the number of available forecasts is large and changes in time. Squared forecast errors are modelled with a semiparametric additive regression model where the linear part involves indicator variables reflecting the time period when the forecast is performed and the nonparametric part involves a smooth function of the number of individual forecasts entering the combinations. The partial regression estimates permit two-dimensional plots of the relationship between squared forecast errors and the number of forecasts entering the combinations and can be used to assess the contribution of additional forecasts in reducing the forecast errors. The method is demonstrated with six empirical applications using macroeconomic forecasts published by Her Majesty’s Treasury.
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Bibliographic InfoPaper provided by Central Bank of Cyprus in its series Working Papers with number 2012-02.
Length: 21 pages
Date of creation: May 2012
Date of revision:
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Web page: http://www.centralbank.gov.cy/nqcontent.cfm?a_id=1
More information through EDIRC
Forecasting combinations; semiparametric models; forecast errors.;
Find related papers by JEL classification:
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
- E3 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles
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