Estimating and forecasting Bahrain quarterly GDP growth using simple regression and factor-based methods

Hanan Naser ()

Empirical Economics, 2015, vol. 49, issue 2, 449-479

Abstract: In this paper, simple regression estimates and factor-based models are utilised to produce forecasts for Bahrain quarterly gross domestic product growth. Using simulated out-of-sample experiments, we assess and compare the performance of the simple regression estimates, which exploit the available information on selected indicator variables, with factor-based estimates. These estimates use up to 65 variables to obtain new factors that embody most of the potential information and handle it in a systematic way following the Stock–Watson approach. Additionally, we compare the performance of the nowcast factor MIDAS with the quarterly factor (static-SW) models based on time-aggregated data, which neglect the most recent information. Our empirical findings can be summarised as follows. First, using more information does not help to produce more accurate results. Preselected indicator variables can clearly improve the forecast performance in comparison with the use of large dataset. Second, quarterly factor models are in general outperformed by the nowcast factor models that directly relate low-frequency data to those of high frequency. Third, the best forecasting performance can be reached using simple regression estimates with a handful of variables. However, it fails in density forecast evaluation tests. Thus, the alternative factor-MIDAS model is considered as an optimal model that passes all the performance evaluation tests. Fourth, concerning the difference between MIDAS projection methods, the results indicate that MIDAS with exponential distributed lag functions outperforms the MIDAS with unrestricted lag polynomials. The best performing projection based on the number of factors is the model with three factors. They can pick up the rapid switch in the utility of the indicators automatically. Finally, although the most accurate Flash estimates are obtained at 84 days, nowcasting using industrial production into a bridge equation witnessed insignificant loss in accuracy at 54 days only. Copyright Springer-Verlag Berlin Heidelberg 2015

Keywords: Forecasting; Quarterly GDP; Simple regression; Principal components; Factor models (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s00181-014-0892-9

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