Econometric Time Series
http://lists.repec.org/mailman/listinfo/nep-ets
Econometric Time Series
2024-03-11
The Anatomy of Out-of-Sample Forecasting Accuracy
http://d.repec.org/n?u=RePEc:fip:fedawp:97785&r=ets
We introduce the performance-based Shapley value (PBSV) to measure the contributions of individual predictors to the out-of-sample loss for time-series forecasting models. Our new metric allows a researcher to anatomize out-of-sample forecasting accuracy, thereby providing valuable information for interpreting time-series forecasting models. The PBSV is model agnostic—so it can be applied to any forecasting model, including "black box" models in machine learning, and it can be used for any loss function. We also develop the TS-Shapley-VI, a version of the conventional Shapley value that gauges the importance of predictors for explaining the in-sample predictions in the entire sequence of fitted models that generates the time series of out-of-sample forecasts. We then propose the model accordance score to compare predictor ranks based on the TS-Shapley-VI and PBSV, thereby linking the predictors' in-sample importance to their contributions to out-of-sample forecasting accuracy. We illustrate our metrics in an application forecasting US inflation.
Daniel Borup
Philippe Goulet Coulombe
Erik Christian Montes Schütte
David E. Rapach
Sander Schwenk-Nebbe
model interpretation; Shapley value; predictor importance; loss function; machine learning; inflation
2024-02-21
Nowcasting with mixed frequency data using Gaussian processes
http://d.repec.org/n?u=RePEc:arx:papers:2402.10574&r=ets
We propose and discuss Bayesian machine learning methods for mixed data sampling (MIDAS) regressions. This involves handling frequency mismatches with restricted and unrestricted MIDAS variants and specifying functional relationships between many predictors and the dependent variable. We use Gaussian processes (GP) and Bayesian additive regression trees (BART) as flexible extensions to linear penalized estimation. In a nowcasting and forecasting exercise we focus on quarterly US output growth and inflation in the GDP deflator. The new models leverage macroeconomic Big Data in a computationally efficient way and offer gains in predictive accuracy along several dimensions.
Niko Hauzenberger
Massimiliano Marcellino
Michael Pfarrhofer
Anna Stelzer
2024-02
Principal Component Analysis for Nonstationary Series
http://d.repec.org/n?u=RePEc:nbr:nberwo:32068&r=ets
This paper develops a procedure for uncovering the common cyclical factors that drive a mix of stationary and nonstationary variables. The method does not require knowing which variables are nonstationary or the nature of the nonstationarity. An application to the FRED-MD macroeconomic dataset demonstrates that the approach offers similar benefits to those of traditional principal component analysis with some added advantages.
James D. Hamilton
Jin Xi
2024-01