Forecasting Consumer Price Index Inflation in India: Vector Error Correction Mechanism Vs. Dynamic Factor Model Approach for Non-Stationary Time Series
Rudrani Bhattacharya () and
Mrigankshi Kapoor ()
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Rudrani Bhattacharya: National Institute of Public Finance and Policy
Mrigankshi Kapoor: Birla Institute of Technology and Science
Working Papers from National Institute of Public Finance and Policy
Short to medium term forecasting of inflation rate is important for economic decision making by economic agents and timely implementation of monetary policy. In this study, we develop two alternative forecasting models for Year-on-Year (YOY) inflation in Consumer Price Index (CPI) in India using a large number of macroeconomic indicators. The YOY CPI inflation and its predictive indicators are found to be non-stationary and cointegrated. To address this issue, we employ Vector Error Correction Model (VECM) and Dynamic Factor Model (DFM) modified for non-stationary time series to forecast CPI inflation. We find that in terms of Root Mean Square Error (RMSE), the VECM model performs marginally better than the DFM model. However, both models are found to have the same predictive accuracy using Diebold-Mariano test.
Keywords: CPI Inflation; India; Forecasting; Vector Error Correction Model; Dynamic Factor Model (search for similar items in EconPapers)
JEL-codes: C32 C53 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ets, nep-for, nep-mon and nep-ore
Note: Working Paper 323, 2020
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Persistent link: https://EconPapers.repec.org/RePEc:npf:wpaper:20/323
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