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Summary

This paper evaluates three approaches to address parameter proliferation issue in nowcasting: (i) variable selection using adjusted stepwise autoregressive integrated moving average with exogenous variables (AS-ARIMAX); (ii) regularization in machine learning (ML); and (iii) dimensionality reduction via principal component analysis (PCA). Utilizing 166 variables, we estimate our models from 2007Q2 to 2019Q4 using rolling-window regression, while applying these three approaches. We then conduct a pseudo out-of-sample performance comparison of various nowcasting models—including Bridge, MIDAS, U-MIDAS, dynamic factor model (DFM), and machine learning techniques including Ridge Regression, LASSO, and Elastic Net to predict China’s annualized real GDP growth rate from 2020Q1 to 2023Q1. Our findings suggest that the LASSO method outperform all other models, but only when guided by economic judgment and sign restrictions in variable selection. Notably, simpler models like Bridge with AS-ARIMAX variable selection yield reliable estimates nearly comparable to those from LASSO, underscoring the importance of effective variable selection in capturing strong signals.

Summary

This paper presents the Mauritius Quarterly Projection Model (QPM), the semi-structural analytical tool that underpins the modernized Forecasting and Policy Analysis System of the Bank of Mauritius (BOM). The model is designed to capture the salient features of the domestic economy, including key monetary policy transmission channels and the recently introduced flexible inflation targeting framework. Relative to canonical QPM structures, it also incorporates a parsimonious fiscal block and a labor market block, providing key insights on broader macroeconomic dynamics and enriching the policy advice. The model optimally balances theoretical consistency—evident in coherent shock propagation and policy responses—and empirical reliability, as reflected in its strong in-sample forecasting performance. The practical use of the Mauritius QPM in the context of the BOM’s regular forecasting cycles for the production of baseline projections, counterfactual simulations and alternative scenarios, together with the corresponding model-based economic narratives, make it a critical component of the BOM’s forward-looking monetary policy formulation.

Subject:

Exchange rates,
Foreign exchange,
Inflation,
Labor,
Labor markets,
Output gap,
Prices,
Production,
Real effective exchange rates,
Real wages,
Wages

Keywords:

Exchange rates,
Forecasting and Policy Analysis,
Global,
Inflation,
Labor markets,
Mauritius,
Monetary Policy,
Output gap,
Quarterly Projection Model,
Real effective exchange rates,
Real wages,
Transmission Mechanism,
Wages

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