Nowcasting and Forecasting Russian Regional CPI: Sparse Models and the Time-Varying Value of Online Data

Dean Fantazzini and Alexey Kurbatskii

MPRA Paper from University Library of Munich, Germany

Abstract: This paper investigates the utility of Google Trends data for nowcasting and forecasting regional Consumer Price Indices (CPIs) in Russia. For nowcasting, we compare random walk, ARIMA, and Autoregressive Distributed Lag (ARDL) models, with and without search data. For forecasting, we evaluate ten approaches, including Vector Autoregression (VAR) with Hierarchical Lasso (HLag), dynamic factor models, and shrinkage methods. Results show that for nowcasting, multivariate ARDL models with macroeconomic data consistently outperform simpler ones, while Google Trends adds positive but limited value. In forecasting, search data offers negligible average improvement due to a structural break in early 2022: its predictive power was significant before the geopolitical shift but degraded sharply afterward. Instead, the VAR model with HLag sparsity and comprehensive macroeconomic data consistently proves superior. A robustness check with random forests confirms the advantage of the sparse structured approach. The study highlights the nuanced role of online data and the importance of sparse models for robust forecasting in Russian regions.

Keywords: Nowcasting and Forecasting; Google Trends; Russian Regions; ARDL; VAR; Hierarchical Lasso; Random Forests; Regional CPI; Nonparametric Shrinkage (search for similar items in EconPapers)
JEL-codes: C14 C32 C53 C55 E31 E37 R11 (search for similar items in EconPapers)
Date: 2026
New Economics Papers: this item is included in nep-cis, nep-ets and nep-for
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