Early warning system for real economy : a case study of Thailand
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2019
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2562
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eng
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105 leaves
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b210881
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
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National Institute of Development Administration. Library and Information Center
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Jeerawadee Pumjaroen (2019). Early warning system for real economy : a case study of Thailand. Retrieved from: https://repository.nida.ac.th/handle/662723737/5206.
Title
Early warning system for real economy : a case study of Thailand
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Abstract
The research aims to identify Composite Leading Indexes (CLIs) and develops the Early Warning System (EWS) by Partial Least Squares Structural Equation Modeling (PLS-SEM). The objective of EWS is to forecast the Economic Cycle (EC) in short-term, medium-term, and long-term periods.
Data from Thailand during Q1/2003-Q4/2008 are applied to pursue the purposes. The research uses Real Gross Domestic Product (GDP) as a proxy for the Thai economy, which is the target variable that EWS aims to early signal. The indicators from various economic sectors are gathered to construct CLIs. Before starting the estimation process, the data are filtered out unnecessary components and standardized so that the data will contain only cyclical patterns and not have the unit effect in the analysis. The research builds up the CLIs from the formative measurement models: Short-Leading Economic Index (SLEI), Financial Cycle (FC), Monetary Condition (MC), and International Transmission by Trade Channel (ITT), whereas the research sets International Transmission by International Monetary Policy Channel (ITM) as a single-item construct. The CLIs are separated into a short-term, medium-term, and long-term leading period. The short-term CLIs include SLEI and ITT, whereas FC is the medium-term CLI, and the long-term CLIs consist of MC and ITM. Regarding results, SLEI and ITT can signal EC at one-quarter ahead, FC leads EC at seven-quarter in advance, and MC and ITM advance signal EC eleven-quarter.
To confirm that EWS by PLS-SEM is outstanding to forecast EC, the research compares the forecasting performance of EWS by PLS-SEM with the CLI by equal weight and the ARIMA model. The evidence is explicit that EWS by PLS-SEM outperforms the benchmark models for all short-term, medium-term, and long-term leading periods.
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Thesis (Ph.D. (Applied Statistics))--National Institute of Development Administration, 2019