Forecasting deflation probability in the EA : a combinatoric approach

Abstract

I assess and forecast the probability of deflation in the EA at different horizons using a binomial probit model. I select the best predictors among more than one-hundred variables adopting a two-step combinatoric approach and exploiting parallel computation in Julia language. I show that the best-selected variables coincide to those standardly included in a small New Keynesian model. Also, I assess the goodness of the models using three different loss functions: the Mean Absolute Error (MAE), the Root Mean Squared Error (RMSE) and the Area Under the Receiver Operating Characteristics (AUROC). The results are reasonably consistent among the three criteria. Finally, I compute an index averaging the forecasts to assess the probability of being in a deflation state in the next two years. The index shows that having inflation above the 2% level before March 2019 is extremely unlikely.