We propose Hybrid Shrink (HS), a novel model for combining forecasts that merges forecast selection, weighting, and shrinkage concepts by shrinking forecasters’ weights to average weights, i.e., toward subset selection, or to zero. HS learns the forecast shrinkage direction by minimizing the...

The expectations-augmented Phillips curve (PC) is a cornerstone of many macroeconomic models. We consider the extent to which professional forecasters' inflation and unemployment rate forecasts are `theory consistent', and find much heterogeneity. Perceptions about the responsiveness of...

Many large and small companies in the tech and startup sector have been laying off an unusually high number of workers in 2022 and 2023. We are interested in predicting when this period of layoffs might end, without resorting to economic forecasts. We observe that a sample of layoffs up to March...

Futures trading is one of the oldest trading methods and investing in commodities. History of commodities futures trading in India is interrupted, flabbergasted and disrupted, unlike commodities future trading in Chicago Mercantile Exchange (CME), Chicago Board of Trade (CBOT), or London Metal...

AbstractForecast combination—the aggregation of individual forecasts from multiple experts or models—is a proven approach to economic forecasting. To date, research on economic forecasting has concentrated on local combination methods, which handle separate but related forecasting tasks in...

During the last decade a lot of academic papers consider the possibility of predicting the economic fluctuations and macroeconomic variables volatility with the use of news data. The reason for this is the development of new machine learning techniques and enhancement of the existed methods. The...

This article assesses the relative effects on household recycling across characteristics of individuals and their households, their counties, and the states in which they live. A representative United States sample assesses household recycling between 2005 and 2014, making possible a pooled...

In this paper, we evaluate the marginal predictive content of a variety of new business conditions and economic uncertainty indexes. Our indexes are defined as latent factors extracted from a high dimensional macroeconomic dataset (business conditions indexes) and as functions of predictive...

We develop methodology and theory for a general Bayesian approach towards dynamic variable selection in high-dimensional regression models with time-varying parameters. Specifically, we propose a variational inference scheme which features dynamic sparsity-inducing properties so that different...

We introduce OFTER, a time series forecasting pipeline tailored for mid-sized multivariate time series. OFTER utilizes the non-parametric models of k-nearest neighbors and Generalized Regression Neural Networks, integrated with a dimensionality reduction component. To circumvent the curse of...