Survey Based Forecasting: To Average or Not to Average

Abstract:

Forecasting inflation rate is of tremendous importance for firms, consumers, as well as monetary policy makers. Besides macroeconomic indicators, professional surveys deliver experts’ expectation and perception of the future movements of the price level. This research studies survey-based inflation forecast in an extended recent sample covering the Great Recession and its aftermath. Traditional methods extract the central tendency in mean or median and use it as a predictor in a simple linear model. Among the three widely cited surveys, we confirm the superior forecasting capability of the Survey of Professional Forecasters (SPF). While each survey consists of many individual experts, we utilize machine learning methods to aggregate the individual information. In addition to the off-the-shelf machine leaning algorithms such as the Lasso, the random forest and the gradient boosting machine (GBM), we tailor the standard Lasso by differentiating the penalty level according to an expert’s experience, in order to handle for participants’ frequent entries and exits in surveys. The tailored Lasso delivers strong empirical results in the SPF and beats all other methods except for the overall best performer, GBM. Combining forecasts of the tailored Lasso model and GBM further achieves the most accurate inflation forecast in both the SPF and the Livingston Survey, which beyonds the reach of a single machine learning algorithm. We conclude that combination of machine learning forecasts is a useful technique to predict inflation, and averaging should be exercised in a new generation of algorithms capable of digesting disaggregated information.