In this paper, we establish the consistency of the model selection criterion based on the quasi-marginal likelihood (QML) obtained from Laplace-type estimators. We consider cases in which parameters are strongly identified, weakly identified and partially identified. Our Monte Carlo results...

We propose a new approach to analyze economic shocks. Our new procedure identifies economic shocks as exogenous shifts in a function; hence, we call them "functional shocks." We show how to identify such shocks and how to trace their effects in the economy via VARs using "VARs with functional...

We propose a new approach to analyze economic shocks. Our new procedure identifies economic shocks as exogenous shifts in a function; hence, we call them “ functional shocks.” We show how to identify such shocks and how to trace their effects in the economy via VARs using “ ...

It is standard in applied work to select forecasting models by ranking candidate models by their prediction mean squared error (PMSE) in simulated out-of-sample (SOOS) forecasts. Alternatively, forecast models may be selected using information criteria (IC). We compare the asymptotic and...

While forecasting is a common practice in academia, government and business alike, practitioners are often left wondering how to choose the sample for estimating forecasting models. When we forecast inflation in 2014, for example, should we use the last 30 years of data or the last 10 years of...

In this paper we propose empirical methods for detecting and identifying misspecifications in DSGE models. We introduce wedges in a DSGE model and identify potential misspecification via forecast error variance decomposition (FEVD) and marginal likelihood analyses. Our simulation results based...

This paper studies stylized empirical facts regarding the effects of unexpected changes in aggregate macroeconomic fiscal policies on consumers that are allowed to differ depending on their individual characteristics. We use data from the Consumption Expenditure Survey (CEX) to estimate...

Many users of structural VAR models are primarily interested in learning about the shape of structural impulse response functions. This requires joint inference about sets of structural impulse responses, allowing for dependencies across time as well as across response functions. Such joint...