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

Fantazzini, Dean and Kurbatskii, Alexey (2026): Nowcasting and Forecasting Russian Regional CPI: Sparse Models and the Time-Varying Value of Online Data. Forthcoming in: Journal of the New Economic Association No. 4

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.

• Item Type: MPRA Paper
• Language: English
• Keywords: Nowcasting and Forecasting; Google Trends; Russian Regions; ARDL; VAR; Hierarchical Lasso; Random Forests; Regional CPI; Nonparametric Shrinkage
• Subjects: C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C14 - Semiparametric and Nonparametric Methods: General; C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C32 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes ; State Space Models; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C55 - Large Data Sets: Modeling and Analysis; E - Macroeconomics and Monetary Economics > E3 - Prices, Business Fluctuations, and Cycles > E31 - Price Level ; Inflation ; Deflation; E - Macroeconomics and Monetary Economics > E3 - Prices, Business Fluctuations, and Cycles > E37 - Forecasting and Simulation: Models and Applications; R - Urban, Rural, Regional, Real Estate, and Transportation Economics > R1 - General Regional Economics > R11 - Regional Economic Activity: Growth, Development, Environmental Issues, and Changes

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