Chennai Mathematical Institute


2 -3 pm, NKN Hall,CMI New Building
Inflation Forecasting in Emerging Markets: A Machine Learning Approach

Kriti Mahajan
Center for Advanced Financial Research and Learning (CAFRAL – promoted by the RBI), Mumbai, India.


In developing and emerging economies, the accuracy of macroeconomic forecasts is often constrained by the limited availability of data both in time series and in cross-section. Given this constraint, this paper uses a suite of machine learning methods to explore if they can offer any improvements in forecast accuracy for headline CPI inflation (y-o-y) in 3 emerging market economies: India, China, and South Africa. For each forecast horizon for each country, we use a host of machine learning models and compare the accuracy of each method to 2 benchmark models (namely, a moving average forecast and SARIMA). For India, we find that the deep neural networks outperform the benchmark forecast for all horizons except the 1 month ahead forecast. The reduction in forecasting error ranges from 44% to 63%. For South Africa, the neural network model provides a reduction in forecasting error between 42% and 57% for the 1 year forecast. For China, the reduction in forecasting error is much more modest ranging from 5% to 33%. An average forecast using different neural net methods performs much better than any individual forecast.


Short Biography:

Ms. Kriti Mahajan is a Research Associate at the Center for Advanced Financial Research and Learning (CAFRAL – promoted by the RBI), Mumbai, India. Her research focuses on the application of econometric and machine learning methods to inform policy decisions in emerging and developing market economies.

She attended the Masters in Economics program at the Sciences Po Doctoral School (Paris, France) and was a research assistant for Dr. Emeric Henry (Head of Doctoral studies in Economy, Associate Professor, Department of Economics, Sciences Po). She has a Bachelors in Economics from St. Xavier’s College (Mumbai, India).