Forecasting Singapore's economy using statistical learning and factor models

Forecasting Singapore's economy using statistical learning and factor models

We evaluate the performance of the penalized vector autoregression (VAR), diffusion index (DI), and regression tree-based ensemble learning models to forecast Singapore's macroeconomy using high-dimensional data. Our dataset consists of 220 monthly time series that capture the economy of Singap...

Full description

Saved in:
Bibliographic Details
Main Authors: Foo, Benedict, Koh, Deng Yao, Tan, Juan Pang, Wang, Wenjie
Other Authors: School of Social Sciences
Format: Article
Language:English
Published: 2023
Subjects:
Social sciences::Economic theory
Diffusion Index
Forecasting
Online Access:https://hdl.handle.net/10356/170350
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
Description
Summary:We evaluate the performance of the penalized vector autoregression (VAR), diffusion index (DI), and regression tree-based ensemble learning models to forecast Singapore's macroeconomy using high-dimensional data. Our dataset consists of 220 monthly time series that capture the economy of Singapore and 20 monthly times series that capture the global economic environment. We find that the penalized VAR model and the ensemble learning model give an outstanding performance in both short and long horizons. On the other hand, the performance of the DI model depends crucially on the methods to select the number of factors. In particular, a conventional selection method may overestimate the true number of factors and thus deteriorate the forecasting performance of the DI model. Additionally, the VAR and DI models may utilize different information in forecasting.

Similar Items