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The Use of Encompassing Tests for Forecast Combinations

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Author Info

  • Turgut Kisinbay

Abstract

The paper proposes an algorithm that uses forecast encompassing tests for combining forecasts. The algorithm excludes a forecast from the combination if it is encompassed by another forecast. To assess the usefulness of this approach, an extensive empirical analysis is undertaken using a U.S. macroecoomic data set. The results are encouraging as the algorithm forecasts outperform benchmark model forecasts, in a mean square error (MSE) sense, in a majority of cases.

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Bibliographic Info

Paper provided by International Monetary Fund in its series IMF Working Papers with number 07/264.

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Length: 21
Date of creation: 01 Nov 2007
Date of revision:
Handle: RePEc:imf:imfwpa:07/264

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Related research

Keywords: Forecasting models; Economic forecasting; forecasting; significance level; time series; significance levels; econometrics; linear regressions; statistic; number of variables; prediction; mean square; forecast encompassing tests; surveys; arithmetic; statistics; linear regression; absolute errors; horizontal axis; logarithm; sample sizes; outliers; economic statistics; regression techniques; samples; monte carlo simulations; covariance; standard deviations; autoregression; normal distribution; forecasting method; economic modeling; number of regressors; real variables; linear prediction; sample mean; sample size; consistent estimate; forecasting model; regression coefficients; macroeconomic time series; bayesian information criterion;

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Cited by:
  1. Leonardo Morales-Arias & Alexander Dross, 2010. "Adaptive Forecasting of Exchange Rates with Panel Data," Research Paper Series, Quantitative Finance Research Centre, University of Technology, Sydney 285, Quantitative Finance Research Centre, University of Technology, Sydney.
  2. A. Girardi & R. Golinelli & C. Pappalardo, 2014. "The Role of Indicator Selection in Nowcasting Euro Area GDP in Pseudo Real Time," Working Papers wp919, Dipartimento Scienze Economiche, Universita' di Bologna.
  3. Cang, Shuang & Yu, Hongnian, 2014. "A combination selection algorithm on forecasting," European Journal of Operational Research, Elsevier, Elsevier, vol. 234(1), pages 127-139.
  4. Antonis Michis, 2012. "Monitoring Forecasting Combinations with Semiparametric Regression Models," Working Papers, Central Bank of Cyprus 2012-02, Central Bank of Cyprus.
  5. Costantini, Mauro & Pappalardo, Carmine, 2010. "A hierarchical procedure for the combination of forecasts," International Journal of Forecasting, Elsevier, Elsevier, vol. 26(4), pages 725-743, October.
  6. Morales-Arias, Leonardo & Moura, Guilherme V., 2013. "Adaptive forecasting of exchange rates with panel data," International Journal of Forecasting, Elsevier, Elsevier, vol. 29(3), pages 493-509.
  7. Leonardo Morales-Arias & Alexander Dross, 2010. "Adaptive Forecasting of Exchange Rates with Panel Data," Kiel Working Papers 1656, Kiel Institute for the World Economy.

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