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© 2023 International Monetary Fund
WP/23/45
IMF Working Paper
Institute for Capacity Development
Identifying Optimal Indicators and Lag Terms for Nowcasting Models
Prepared by Jing Xie
Authorized for distribution by Paul Cashin
March 2023
IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.
ABSTRACT: Many central banks and government agencies use nowcasting techniques to obtain policy relevant information about the business cycle. Existing nowcasting methods, however, have two critical shortcomings for this purpose. First, in contrast to machine-learning models, they do not provide much if any guidance on selecting the best explantory variables (both high- and low-frequency indicators) from the (typically) larger set of variables available to the nowcaster. Second, in addition to the selection of explanatory variables, the order of the autoregression and moving average terms to use in the baseline nowcasting regression is often set arbitrarily. This paper proposes a simple procedure that simultaneously selects the optimal indicators and ARIMA(p,q) terms for the baseline nowcasting regression. The proposed AS-ARIMAX (Adjusted Stepwise Autoregressive Moving Average methods with exogenous variables) approach significantly reduces out-of-sample root mean square error for nowcasts of real GDP of six countries, including India, Argentina, Australia, South Africa, the United Kingdom, and the United States.
| JEL Classification Numbers: | C32, C53, E37, E52, 011 |
| Keywords: | Nowcasting; Mixed Frequency; Forecasting; Business Cycles |
| Author’s E-Mail Address: | jxie2@imf.org |
| JEL Classification Numbers: | C32, C53, E37, E52, 011 |
| Keywords: | Nowcasting; Mixed Frequency; Forecasting; Business Cycles |
| Author’s E-Mail Address: | jxie2@imf.org |
Title Page
WORKING PAPERS
Identifying Optimal Indicators and Lag Terms for Nowcasting Models
Prepared by Jing Xie
Contents
Section 1. Introduction
Section 2. Automatic ARIMA Selection Procedure
Section 3. Adjusted Stepwise ARIMAX Variable Selection Procedure and a Simple Example
Section 4. Benchmark Models
Section 5. Nowcasting Methodology
Section 6. Nowcasting Indian Real GDP
Section 7. Empirical Results
Section 8. Other Country Examples
Section 9. Conclusion
Annex I. AS-ARIMAX Procedure Charts
Annex II. Candidate Indicators and Three Main Attributes
Annex III. Other Country Examples
Bibliography
FIGURES
Figure 1 Realistic Forecast Evaluation: Three Benchmark Models
Figure 2 Realistic Forecast Evaluation: Bridge and U-MIDAS Models
TABLES
Table 1: Pre-Selected data to Nowcast India’s Real GDP
Table 2 Automatic ARIMA Stepwise Variable Selection – Steps 1 and 2 Result
Table 3 Automatic ARIMA Stepwise Variable Selection – Step 3 Result
Table 4 Automatic ARIMA Stepwise Variable Selection – Selected Baseline Model
Table 5 Automatic ARIMA Stepwise Variable Selection – Adjusted Baseline Model
Table 6 Selected Baseline Model
Table 7 Testing for Serial Correlation: Q Statistics
Table 8 Testing for Normality: Jarque-Bera Test
Table 9 Testing for Heteroskedasticity: Breusch-Pagan-Godfrey test
Table 10 Forecast Evaluation Comparison: RMSE
Table 11 Forecast Evaluation Comparison: Theil U2
Table 12 Forecast Evaluation Comparison for Other Countries: RMSE
Acknowledgements:
I would like to thank Mr. Sam Ouliaris for providing instrumental guidance throughout this research, and am grateful to Mr. Paul Cashin, Mr. Fei Han, Ms. Ivy Sabuga, Mr. Alexander Borodin, and Institute for Capacity Development colleagues for their helpful comments. I am also grateful to Ms. Elisa Manarinjara for her support along the publication procedure.