Forecasting Economic Cycle with a Structural Equation Model: Evidence from Thailand
Preecha Vichitthamaros and
Yuthana Sethapramote ()
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Jeerawadee Pumjaroen: School of Applied Statistics, National Institute of Development Administration, Bangkok 10240, Thailand
Preecha Vichitthamaros: School of Applied Statistics, National Institute of Development Administration, Bangkok 10240, Thailand
International Journal of Economics and Financial Issues, 2020, vol. 10, issue 3, 47-57
The study proposes a partial least squares structural equation modeling (PLS-SEM) evaluating the relationship among composite leading indices (CLIs) to forecast the economic cycle (EC) instead of using only individual CLI. The model of quarterly data in Thailand during 2013-2018 includes five constructs representing economic sectors that have the potential to be CLIs of EC. Those are two short-term CLIs including Short-leading economic index (SLEI) and International transmission (Trade channel) (ITT). SLEI composes Narrow money, Business sentiment index (Next 3 months), and Export volume index while ITT constructs from CLI of the major export partners. The Financial cycle (FC) has the potential to be the medium-term CLI, which includes Housing price index, Household debt to GDP, and Household debt. While Monetary condition (MC) and International transmission (Monetary channel) (ITM) are the long-term CLI. MC consists of Policy interest rate and real effective exchange rate whereas ITM is represented by the global economy using CLI for OECD and non-member economies as a proxy. The evidence from the forecasting performance in the out-of-sample by PLS-SEM outperforms the alternative models for all short-term, middle-term, and long-term periods. Therefore, the study convinces to apply the PLS-SEM to forecast EC.
Keywords: PLS-SEM; leading indicator; economic cycle; forecasting (search for similar items in EconPapers)
JEL-codes: E17 E32 E37 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:eco:journ1:2020-03-7
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