Federal Reserve Bank of Dallas

About: Federal Reserve Bank of Dallas is a other organization based out in Dallas, Texas, United States. It is known for research contribution in the topics: Monetary policy & Inflation. The organization has 196 authors who have published 994 publications receiving 35508 citations.

A Multi-Country Approach to Forecasting Output Growth Using PMIs

Abstract: This paper derives new theoretical results for forecasting with Global VAR (GVAR) models. It is shown that the presence of a strong unobserved common factor can lead to an undeter-mined GVAR model. To solve this problem, we propose augmenting the GVAR with additional proxy equations for the strong factors and establish conditions under which forecasts from the augmented GVAR model (AugGVAR) uniformly converge in probability to the infeasible optimal forecasts obtained from a factor-augmented high-dimensional VAR model. The small sample properties of the proposed solution are investigated by Monte Carlo experiments as well as empirically. In the empirical part, we investigate the value of the information content of Purchasing Managers Indices (PMIs) for forecasting global (48 countries) growth, and compare forecasts from AugGVAR models with a number of data-rich forecasting methods, including Lasso, Ridge, partial least squares and factor-based methods. It is found that (a) regardless of the forecasting methods considered, PMIs are useful for nowcasting, but their value added diminishes quite rapidly with the forecast horizon, and (b) AugGVAR forecasts do as well as other data-rich forecasting techniques for short horizons, and tend to do better for longer forecast horizons.

Mobility and Engagement Following the SARS-Cov-2 Outbreak

Abstract: We develop a Social Distancing Index (SDI) based on a range of mobility metrics from Safegraph geolocation data, and validate the index with mobility data from Google and Unacast. We construct SDIs at the county, MSA, state and nationwide level, and link these measures to indicators of economic activity. According to our measures, the bulk of social distancing occurred during the week of March 15 and simultaneously across the U.S. At the national peak of social distancing in early April, localities that engaged in 10% more social distancing than average saw an additional 0.6% of their populations claiming unemployment insurance, an additional 2.8 pp reduction in small businesses employment, an additional 2.6 pp increase in small business closures, and an additional 3.2 pp reduction in new-business applications. A gradual and broad-based reduction in social distancing started in the third week of April.

Nonparametric conditional density estimation of labour force participation

Abstract: Labour force participation decision has been studied primarily in a parametric framework. The weaknesses of the parametric estimators to misspecification of the error distribution and to functional form assumptions are well known. This paper compares the predictive performance of widely used parametric and semiparametric estimators with results obtained from nonparametric kernel conditional density estimation with likelihood cross-validated bandwidth selection and mixed data type. The results are striking. The predictive performance of the nonparametric estimator is 95% against 71% to 77% of the parametric and semiparametric estimators. The nonparametric estimator is able to correctly predict the outcome for 83% of non-participants in the labour force as against 15% by probit and logit models. This underscores the need to use nonparametric estimators in studying labour market behaviour.