We present a sharp test for the efficiency of job separations. First, we document a dramatic increase in the separation rate – 11.2ppt (28%) over five years – in response to a quasi-experimental extension of UI benefit duration for older workers. Second, after the abolition of the policy,...

This paper proposes two distinct contributions to econometric analysis of large information sets and structural instabilities. First, it treats a regression model with time-varying coefficients, stochastic volatility and exogenous predictors, as an equivalent high-dimensional static regression...

Social scientists have long been interested in the relationship between parental factors and later child income. Finding the best characterization of this relationship for the question at hand is however fraught with choices. In this paper we use machine learning methods to assess the...

We examine the relationship between exchange rates and macroeconomic fundamentals using a two-step maximum likelihood estimator through which we compute time-varying factor loadings. Factors are obtained as principal components, extracted from vintage macro-datasets that combine FRED-MD and OECD...

This tutorial gives an overview of SHAP (SHapley Additive exPlanation), one of the most commonly used techniques for examining a black-box machine learning (ML) model. Besides providing the necessary game theoretic background, we show how typical SHAP analyses are performed and used to gain...

The out-of-sample R2 is designed to measure forecasting performance without look-ahead bias. However, researchers can hack this performance metric even without multiple tests by constructing a prediction model using the intuition derived from empirical properties that appear only in the test...

We propose a novel and easy-to-implement framework for forecasting correlation risks based on a large set of salient realized correlation features and the sparsity-encouraging LASSO technique. Considering the universe of S&P 500 stocks, we find that the new approach manifests in statistically...

We introduce an ensemble learning method based on Gaussian Process Regression (GPR) for predicting conditional expected stock returns given stock-level and macro-economic information. Our ensemble learning approach significantly reduces the computational complexity inherent in GPR inference and...

Out-of-sample R2-hacking problems can arise even without multiple testing if a researcher constructs a prediction model using the intuition derived from empirical properties that appear only in the test sample. We provide a machine-learning solution for this problem in the context of robust...