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http://ssrn.com/abstract=1781426
 
 

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Improving Forecasting Performance by Window and Model Averaging


Prasad S. Bhattacharya


Deakin University - School of Accounting, Economics and Finance; Research Associate, Centre for Applied Macroeconomic Analysis

Dimitrios D. Thomakos


University of Peloponnese - School of Management and Economics

January 12, 2010

CAMA Working Paper Series 05/2011

Abstract:     
We present results from an extensive study on the benefits of rolling window and model averaging. Building on the recent work on rolling window averaging by Pesaran et al (2010, 2009) and on exchange rate forecasting by Molodtsova and Papell (2009), we explore whether rolling window averaging can be considered beneficial on a priori grounds, that is whether researchers can use it to improve forecasting performance and to avoid ‘window mining’ in short horizons. In addition, we investigate whether rolling window averaging can improve the performance of model averaging, especially when ‘simpler’ models are being used. Our results provide strong support for rolling window averaging, outperforming the best window forecasts more than 50% of the time across all rolling windows considered – with the outperformance being statistically significant. Furthermore, rolling window averaging smooths out the forecast path and improves robustness of the forecasting model, thus minimizing the pitfalls associated with potential structural breaks. An illustrative simulation supports the proposed improvement with the double averaging approach. Afterward the technique is applied in three datasets: exchange rates for 12 OECD countries, US inflation rate and US output growth rate. For exchange rates, we use the dataset of Molodtsova and Papell (2009) and replicate their analysis by considering rolling window and model averaging. The results reveal rolling window averaging can further improve the performance of the models and, in addition, when combined with model averaging brings forth the forecasting ability of ‘simpler’ economic models of exchange rates. With respect to US inflation and output growth forecasting, we again find that rolling window averaging outperforms the best individual window forecasts by more than 50% of the time, with significant differences from the benchmarks, and helps in model averaging by bringing forth the predictive power of economic variables in parsimonious models.

Number of Pages in PDF File: 38

Keywords: Exchange Rate Forecasting, Inflation Forecasting, Output Growth Forecasting, Rolling Window, Model Averaging, Short Horizon, Robustness

JEL Classification: C22, C53, F31, F47, E31

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Date posted: March 9, 2011  

Suggested Citation

Bhattacharya, Prasad S. and Thomakos, Dimitrios D., Improving Forecasting Performance by Window and Model Averaging (January 12, 2010). CAMA Working Paper Series 05/2011. Available at SSRN: http://ssrn.com/abstract=1781426 or http://dx.doi.org/10.2139/ssrn.1781426

Contact Information

Prasad S. Bhattacharya (Contact Author)
Deakin University - School of Accounting, Economics and Finance ( email )
221 Burwood Highway
Burwood
Melbourne, Victoria 3125
Australia
61392446645 (Phone)
61392446283 (Fax)
HOME PAGE: http://www.deakin.edu.au/buslaw/aef/
Research Associate, Centre for Applied Macroeconomic Analysis ( email )
ANU College of Business and Economics
Canberra, Australian Capital Territory 0200
Australia
Dimitrios D. Thomakos
University of Peloponnese - School of Management and Economics ( email )
Department of Economics
22100 Tripolis
Greece
+30 2710 230139 (Fax)
HOME PAGE: http://econ.uop.gr/~thomakos
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