This paper studies measurement errors that subtract signal from true variables of interest, labeled lack of signal errors (LoSE). The effect on OLS regression of LoSE is opposite the conventional wisdom about classical measurement errors, with LoSE in the dependent variable, not the explanatory...

We study the behavior of the interbank market before, during and after the 2008 financial crisis. Leveraging recent advances in network analysis, we study two network structures, a correlation network based on publicly traded bank returns, and a physical network based on interbank lending...

We propose a methodology that can efficiently measure the Value-at-Risk (VaR) of large portfolios with time-varying volatilities and correlations by bringing together the established historical simulation framework and recent contributions to the Dynamic Factor Models literature. We find that...

This paper proposes a simple generalization of the classical measurement error model, introducing new measurement errors that subtract signal from the true variable of interest, in addition to the usual classical measurement errors (CME) that add noise. The effect on OLS regression of these lack...

This paper advocates chaining the decomposition of shocks into contributions from forecast errors to the shock decomposition of the latent vector to better understand model inference about latent variables. Such a double decomposition allows us to gauge the inuence of data on latent variables,...

We estimate the natural rate of interest (r*) using a semi-structural model of the U.S. economy that jointly characterizes the trend and cyclical factors of key macroeconomic variables such as output, the unemployment rate, inflation, and short- and long-term interest rates. We specify a...

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 proposes a new model for high-dimensional distributions of asset returns that utilizes mixed frequency data and copulas. The dependence between returns is decomposed into linear and nonlinear components, enabling the use of high frequency data to accurately forecast linear dependence,...

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...

We construct new estimates of potential output and the output gap using a multivariate approach that allows for an explicit role for measurement errors in the decomposition of real output. Because we include data on hours, output, employment, and the labor force, we are able to decompose our...