Forecasting Indian Core Inflation: Simple Made Simpler

Abstract

Managing rapid economic growth in India naturally brings into focus the role of inflation forecasts for monetary and fiscal policy making. We investigate whether any of a large set of forecasting models improves upon a univariate auto-regression in forecasting core consumer price inflation. Using a monthly panel of forty macroeconomic and financial indicators we estimate nine classes of models that range from an auto-regression to various factor models, quantile regressions and machine learning specifications. In doing so, we also account for inflation expectations and climate change variables. Our root mean square forecast error model comparison metric operates at horizons of one, three, six and twelve months. With respect to the conditional mean, no model produces a forecast error significantly below that of an auto-regression at any horizon over the full comparison sample. With respect to the conditional distribution, quantile regressions that include estimated factors reduce forecast loss relative to a specification without such factors at every quantile and horizon. Our approach is general enough to be applicable to forecasting core inflation in other emerging market economies.