Backcasting world trade growth using data reduction methods
Bibliographic Data
| ID | 21543394 |
|---|---|
| Authors | Amélie Charles (Audencia Business School Nantes Cedex 3 France), Olivier Darné (0000-0003-2796-4873, LEMNA University of Nantes Nantes France, corresponding author) |
| Year | 2022 |
| Volume | 45 |
| Issue | 10 |
| Pages | 3169-3191 |
| Publication date | 2022-10-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | World Economy (JOURNAL) |
| Journal identifiers | ISSN: 0378-5920 • E-ISSN: 1467-9701 |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1111/twec.13274 |
| OpenAlex | W4224241116 |
| Language | EN |
| References cited | 31 |
This paper compares backcasting performance of models based on variable selection to dynamic factor model for backcasting the world trade growth rate with two months ahead. The variable selection models are specified by applying penalised regressions and an automatic general‐to‐specific procedure, using a large data set. A recursive forecast study is carried out to assess the backcasting performance by distinguishing crisis and non‐crisis periods. The results show that, some selection‐based models exhibit a good backcasting performance during both periods. The more accurate backcasts seem to be SCAD, adaptive Elastic‐Net and adaptive SCAD during the global financial crisis (GFC) and COVID‐19 crisis, whereas it seems rather Lasso, Elastic‐Net, adaptive Lasso and DFM during the non‐crisis period. Amongst the predictors for backcasting world trade growth, it appears that the index of global economic conditions proposed by Baumeister et al. ( The Review of Economics and Statistics , 2020), the PMI indicator on new export orders in manufacturing sector and the MSCI world index are relevant
Backcasting · Econometrics · Economics · Elastic net regularization · Feature selection · Financial crisis · Macroeconomics · Panel data · Computer Science · Energy, Environment, Economic Growth · Market Dynamics and Volatility · Mathematics · Monetary Policy and Economic Impact · Artificial Intelligence
Regularization and Variable Selection Via the Elastic Net
The Adaptive Lasso and Its Oracle Properties
Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
Measuring Economic Policy Uncertainty
Regression Shrinkage and Selection Via the Lasso
Machine Learning Time Series Regressions With an Application to Nowcasting
A Test for Superior Predictive Ability
Macroeconomic Forecasting Using Diffusion Indexes
Energy Markets and Global Economic Conditions
A Quasi–Maximum Likelihood Approach for Large, Approximate Dynamic Factor Models
| Highly cited | No |
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