Many traditional official statistics are not suitable for measuring high-frequency developments that evolve over the course of weeks, not months. In this paper, we track the labor market effects of the COVID-19 pandemic with weekly payroll employment series based on microdata from ADP. These...

Despite the enormous reach and influence of the literature on economic and economic policy uncertainty, one surprisingly under-researched topic has been the forecasting performance of economic uncertainty measures. We evaluate the ability of seven popular measures of uncertainty to forecast...

This paper proposes a new nonparametric mixed data sampling (MIDAS) model and develops a framework to infer clusters in a panel regression with mixed frequency data. The nonparametric MIDAS estimation method is more flexible and substantially simpler to implement than competing approaches. We...

This paper describes the E-Newton and E-QNewton algorithms for solving rational expectations (RE) models. Both algorithms treat a model's RE terms as exogenous variables whose values are iteratively updated until they (hopefully) satisfy the RE requirement. In E-Newton, the updates are based on...

Employing a large number of financial indicators, we use Bayesian Model Averaging (BMA) to forecast real-time measures of economic activity. The indicators include credit spreads based on portfolios -- constructed directly from the secondary market prices of outstanding bonds -- sorted by...

In this paper we analyze the propagation of shocks originating in sectors that are not present in a baseline dynamic stochastic general equilibrium (DSGE) model. Specifically, we proxy the missing sector through a small set of factors, that feed into the structural shocks of the DSGE model to...

When stress tests for the banking sector use a macroeconomic scenario, an unstated premise is that macro variables should be useful factors in forecasting the performance of banks. We assess whether variables such as the ones included in stress tests for U.S. bank holding companies help improve...

Many important economic decisions are based on a parametric forecasting model that is known to be good but imperfect. We propose methods to improve out-of-sample forecasts from a misspecified model by estimating its parameters using a form of local M estimation (thereby nesting local OLS and...

We conduct a novel empirical analysis of the role of leverage of financial institutions for the transmission of financial shocks to the macroeconomy. For that purpose we develop an endogenous regime-switching structural vector autoregressive model with time-varying transition probabilities that...