A popular approach to perform inference on a target parameter in the presence of nuisance parameters is to construct estimating equations that are orthogonal to the nuisance parameters, in the sense that their expected first derivative is zero. Such first-order orthogonalization may, however,...

A popular approach to perform inference on a target parameter in the presence of nuisance parameters is to construct estimating equations that are orthogonal to the nuisance parameters, in the sense that their expected first derivative is zero. Such first-order orthogonalization may, however,...

We develop methodology for estimation and inference using machine learning to enrich economic models. Our framework takes a standard economic model and recasts the parameters as fully flexible nonparametric functions, to capture the rich heterogeneity based on potentially high dimensional or...

We develop methodology for estimation and inference using machine learning to enrich economic models. Our framework takes a standard economic model and recasts the parameters as fully flexible nonparametric functions, to capture the rich heterogeneity based on potentially high dimensional or...

This study focuses on the consensus forecasts from the Survey of Professional Forecasters (SPF) for 1993-2017. These include the SPF forecasts of US 10-year Treasury rate (TBR), Moody's Aaa corporate bond rate (Aaa), CPI inflation, and real GDP growth. We show that both SPF and random walk...

This study focuses on the consensus forecasts from the Survey of Professional Forecasters (SPF) for 1993-2017. These include the SPF forecasts of US 10-year Treasury rate (TBR), Moody's Aaa corporate bond rate (Aaa), CPI inflation, and real GDP growth. We show that both SPF and random walk...

The paper explores probability theory foundations behind evaluation of probabilistic forecasts. The emphasis is on a situation when the forecast examiner possesses only partially the information which was available and was used to produce a forecast. We argue that in such a situation forecasts...