Abstract
It is common for macroeconomic data to be observed at different frequencies. This gives a challenge to analysts when forecasting with multivariate model is concerned. The mixed-frequency data sampling (MIDAS) model has been developed to deal with such problem. However, there are several MIDAS model specifications and they can affect forecasting outcomes. Thus, we investigate the forecasting performance of MIDAS model under different specifications. Using financial variable to forecast quarterly GDP growth in Thailand, our results suggest that U-MIDAS model significantly outperforms the traditional time-aggregate model and MIDAS models with weighting schemes. Additionally, MIDAS model with Beta weighting scheme exhibits greater forecasting precision than the time-aggregate model. This implies that MIDAS model may not be able to surpass the traditional time-aggregate model if inappropriate weighting scheme is used.
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Notes
- 1.
Also called “The principle of parsimony”, it states that the parsimonious model specification is the model that is optimally formed with the smallest numbers of parameters to be estimated [2].
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Acknowledgements
The authors would like to thank the anonymous reviewer for useful suggestions which have greatly improved the quality of this paper. This research is supported by the Puay Ungphakorn Center of Excellence in Econometrics, Chiang Mai University.
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Kingnetr, N., Tungtrakul, T., Sriboonchitta, S. (2018). Does Forecasting Benefit from Mixed-Frequency Data Sampling Model: The Evidence from Forecasting GDP Growth Using Financial Factor in Thailand. In: Kreinovich, V., Sriboonchitta, S., Chakpitak, N. (eds) Predictive Econometrics and Big Data. TES 2018. Studies in Computational Intelligence, vol 753. Springer, Cham. https://doi.org/10.1007/978-3-319-70942-0_31
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DOI: https://doi.org/10.1007/978-3-319-70942-0_31
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