Learning and forecasting
The traditional reaction to forecasting anomalies in macroeconomics is that they imply that professional forecasters are irrational, i.e., are not making efficient use of the information available to them.
Forecasts of professional forecasters are anomalous: they are biased, forecast errors are autocorrelated, and forecast revisions predict forecast errors. Sticky or noisy information models seem like unlikely explanations for these anomalies: professional forecasters pay attention constantly and have precise knowledge of the data in question. We propose that these anomalies arise because professional forecasters don’t know the model that generates the data. We show that Bayesian agents learning about hard-to-learn features of the data generating process (low frequency behavior) can generate all the prominent aggregate anomalies emphasized in the literature. We show this for two applications: professional forecasts of nominal interest rates for the sample period 1980-2019 and CBO forecasts of GDP growth for the sample period 1976-2019. Our learning model for interest rates also provides an explanation for deviations from the expectations hypothesis of the term structure that does not rely on time-variation in risk premia.
Learning About the Long Run
Authors: Leland Farmer, Emi Nakamura, Jon Steinsson
From: University of Virginia, UC Berkeley
Machine learning and forecasting
Popular econometric models, with well defined long-run solutions can lead to forecasting failure when economic indicators suffer from structural breaks. To solve this problem, Pinto and Castle use a clustering function to detect possible abnormalities in the data, which could be stated as a structural break. When this happens, they have an algorithm that switches between the set of robust and non-robust models, which are combined with LASSO.
They perform a rigorous empirical evaluation with 400 simulated series with an artificial structural break and with real data economic series: Industrial Production and Consumer Prices for all Western European countries available from the OECD database. Their results show that the proposed method statistically outperforms benchmarks in forecast accuracy for most case scenarios, particularly at short horizons.
A Machine Learning Dynamic Switching Approach to Forecasting when there are Structural Breaks
Authors: Jeronymo Marcondes Pinto, Jennifer L. Castle
From: Sao Paulo School of Economics, University of Oxford
