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Erschienen in:
Data in the 21st Century Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

Does Forecasting Benefit from Mixed-Frequency Data Sampling Model: The Evidence from Forecasting GDP Growth Using Financial Factor in Thailand

verfasst von : Natthaphat Kingnetr, Tanaporn Tungtrakul, Songsak Sriboonchitta

Erschienen in: Predictive Econometrics and Big Data

Verlag: Springer International Publishing

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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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Vorheriges Kapitel The Role of Oil Price in the Forecasts of Agricultural Commodity Prices
Nächstes Kapitel A Portfolio Optimization Between US Dollar Index and Some Asian Currencies with a Copula-EGARCH Approach
Fußnoten
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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Metadaten
Titel
Does Forecasting Benefit from Mixed-Frequency Data Sampling Model: The Evidence from Forecasting GDP Growth Using Financial Factor in Thailand
verfasst von
Natthaphat Kingnetr
Tanaporn Tungtrakul
Songsak Sriboonchitta
Copyright-Jahr
2018
Buch
Predictive Econometrics and Big Data

Print ISBN: 978-3-319-70941-3

Electronic ISBN: 978-3-319-70942-0

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-70942-0_31