Research

This paper considers inflation forecasting for a vast panel of countries. We combine the information from common factors driving global and country-specific inflation to build different models. We also rely on new advances in the Machine Learning literature. We show that random forests and neural networks are very competitive models, and their superiority, although stable across most of the time period considered, increases during recessions. We also show that it is easier to forecast countries with more developed economies. The forecasting gains seem to be partially explained by the degree of trade openness and inflation volatility within a year. Our results have two significant implications for monetary policy. First, our forecasts can serve as inflation expectations for countries where survey data are unavailable. Second, we shed some light on the links between inflation from different countries, facilitating the study of the transmission of monetary shocks.

The anatomy of out of sample forecast accuracy (with Daniel Borup, David E. Rapach, Philippe G. Coulombe, and Sander Schwenk-Nebbe)

We develop metrics based on Shapley values for interpreting time-series forecasting models, including “black-box” models from machine learning. Our metrics are model agnostic, so that they are applicable to any model (linear or nonlinear, parametric or nonparametric). Two of the metrics, iShapley-VI and oShapley-VI, measure the importance of individual predictors in fitted models for explaining the in-sample and out-of-sample predicted target values, respectively. The third metric is the performance-based Shapley value (PBSV), our main methodological contribution. PBSV measures the contributions of individual predictors in fitted models to the out-of-sample loss and thereby anatomizes out-of-sample forecasting accuracy. In an empirical application forecasting US inflation, we find important discrepancies between individual predictor relevance according to the in-sample iShapley-VI and out-of-sample PBSV. We use simulations to analyze potential sources of the discrepancies, including overfitting, structural breaks, and evolving predictor volatilities.

Python Package to implement the performance-based Shapley value (PBSV)

The anatomy of machine learning-based portfolio performance (with David E. Rapach, Philippe G. Coulombe, and Sander Schwenk-Nebbe)

The relevance of asset return predictability is routinely assessed by the economic value that it produces in asset allocation exercises. Specifically, out-of-sample return forecasts are generated based on a set of predictors, increasingly via “black box” machine learning models. The return forecasts then serve as inputs for constructing a portfolio, and portfolio performance metrics are computed over the forecast evaluation period. To shed light on the sources of the economic value generated by return predictability in fitted machine learning models, we develop a methodology based on Shapley values—the Shapley-based portfolio performance contribution (SPPC)—to directly estimate the contributions of individual or groups of predictors to portfolio performance. We illustrate the use of the SPPC in an empirical application measuring the economic value of cross-sectional stock return predictability based on a large number of firm characteristics and machine learning.

Risk aversion and asset market participation: Evidence from micro-level register data (with Stig Vinther Møller and Tobias Skipper Soussi)

We use high-quality register data to analyze the relation between participation rates in asset markets and implied risk aversion coefficients within the framework of the consumption-based asset pricing model. We show that the higher the participation rate in asset markets, the higher the consumption risk and the lower the implied risk aversion. As a result, we observe a considerable reduction of the equity premium puzzle.

Contributions to crowd-sourced research projects:

Reproducibility in Management Science

Miloš Fišar, Ben Greiner, Christoph Huber, Elena Katok, Ali Ozkes, and Management Science Reproducibility Collaboration

Work in progress:

Forecasting commodity returns: the role of sentiment (with Christoffer Thimsen and Oguzhan Cepni)

Money illusion versus inflation non-neutrality: An indirect inference approach (with Tom Engsted and Thomas Q. Pedersen)

Seasonal Fluctuations in House Prices (with Stig V. Møller, Thomas Q. Pedersen, and Allan Timmermann)