This paper examines the suitability of Google Trends data for the modeling and forecasting of interregional migration in Russia. Monthly migration data, search volume data, and macro variables are used with a set of univariate and multivariate models to study the migration data of the two...

This paper aims to assess whether Google search data is useful when predicting the US unemployment rate among other more traditional predictor variables. A weekly Google index is derived from the keyword “unemployment” and is used in diffusion index variants along with the weekly number of...

We use a machine learning approach to forecast the US GDP value of the current quarter and several quarters ahead. Within each quarter, the contemporaneous value of GDP growth is unavailable but can be estimated using higher-frequency variables that are published in a more timely manner. Using...

This paper introduces the Super Learner to nowcast and forecast the probability of a US economy recession in the current quarter and future quarters. The Super Learner is an algorithm that selects an optimal weighted average from several machine learning algorithms. In this paper, elastic net,...

Textual analysis of the NBER Working Papers published during 1999–2016 is done to assess the effects of the 2007–2009 crisis on the academic literature. The volume of crisis-related WPs is counter-cyclical, lagging the financial-instability-index. WPs by the Monetary-Economics,...

Textual analysis of 14,270 NBER Working Papers published during 1999–2016 is done to assess the effects of the 2008 crisis on the economics literature. The volume of crisis-related WPs is counter-cyclical, lagging the financial-instability-index. WPs by the Monetary-Economics, Asset-Pricing,...