Entropy Econometrics for combining regional economic forecasts: A Data-Weighted Prior Estimator

Esteban Fernández-Vázquez () and Blanca Moreno ()
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Esteban Fernández-Vázquez: University of Oviedo
Blanca Moreno: University of Oviedo
Authors registered in the RePEc Author Service: Esteban Fernández Vázquez ()

Journal of Geographical Systems, 2017, vol. 19, issue 4, No 4, 349-370

Abstract: Abstract Forecast combination has been studied in econometrics for a long time, and the literature has shown the superior performance of forecast combination over individual predictions. However, there is still controversy on which is the best procedure to specify the forecast weights. This paper explores the possibility of using a procedure based on Entropy Econometrics, which allows setting the weights for the individual forecasts as a mixture of different alternatives. In particular, we examine the ability of the Data-Weighted Prior Estimator proposed by Golan (J Econom 101(1):165–193, 2001) to combine forecasting models in a context of small sample sizes, a relative common scenario when dealing with time series for regional economies. We test the validity of the proposed approach using a simulation exercise and a real-world example that aims at predicting gross regional product growth rates for a regional economy. The forecasting performance of the Data-Weighted Prior Estimator proposed is compared with other combining methods. The simulation results indicate that in scenarios of heavily ill-conditioned datasets the approach suggested dominates other forecast combination strategies. The empirical results are consistent with the conclusions found in the numerical experiment.

Keywords: Forecast combination; Entropy Econometrics; Regional indicators; Simulation (search for similar items in EconPapers)
JEL-codes: C53 R12 (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/s10109-017-0259-9

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