In the context of latent factor models that are widely used in economics, a common assumption made is one of factor pervasiveness, which implies that all available predictor or informative variables in a dataset, with the possible exception of a negligible number of them, load significantly on...

Most modern supervised statistical/machine learning (ML) methods are explicitly designed to solve prediction problems very well. Achieving this goal does not imply that these methods automatically deliver good estimators of causal parameters. Examples of such parameters include individual...

We provide adaptive inference methods for linear functionals of L1-regularized linear approximations to the conditional expectation function. Examples of such functionals include average derivatives, policy effects, average treatment effects, and many others. The construction relies on building...

In this paper we discuss the current state-of-the-art in estimating, evaluating, and selecting among non-linear forecasting models for economic and financial time series. We review theoretical and empirical issues, including predictive density, interval and point evaluation and model selection,...

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Forecasters and applied econometricians are often interested in comparing the predictive accuracy of nested competing models. A leading example of nestedness is when predictive ability is equated with ?out-of-sample Granger causality?. In particular, it is often of interest to assess whether...

Various inflation forecasting models are compared using a simulated out-of-sample forecasting framework. We focus on the question of whether monetary aggregates are useful for forecasting inflation, but unlike previous work we examine a wide range of forecast horizons and allow for estimated as...