This paper explores ways to integrate model uncertainty into policy evaluation. We first describe a general framework for the incorporation of model uncertainty into standard econometric calculations. This framework employs Bayesian model averaging methods that have begun to appear in a range of...

This paper seeks to contribute to discussion of the reasonableness of sometimes seemingly innocent assumptions used in theoretical trade models that the direction of trade is both predetermined for each good for each country and fixed. Here, we provide computational evidence as to the...

This paper presents a way of estimating how accurate VAR models are likely to be for answering structural questions. Data are generated from a dynamic deterministic solution of a structural model; a VAR model is estimated using a subset of these data; and the properties of the VAR model are...

It is common in empirical research to use what appear to be sensible rules of thumb for cleaning data. Measurement error is often the justification for removing (trimming) or recoding (winsorizing) observations whose values lie outside a specified range. This paper considers identification in a...

The authors show that in weakly identified models (1) the posterior mode will not be a consistent estimator of the true parameter vector, (2) the posterior distribution will not be Gaussian even asymptotically, and (3) Bayesian credible sets and frequentist confidence sets will not coincide...

Computable General Equilibrium models, widely used for the analysis of Free Trade Agreements (FTAs) are often criticized for having poor econometric foundations. This paper improves the linkage between econometric estimates of key parameters and their usage in CGE analysis in order to better...

The COVID-19 pandemic has highlighted the importance of reliable statistical models which, based on the available data, can provide accurate forecasts and impact analysis of alternative policy measures. Here we propose Bayesian time dependent Poisson autoregressive models, that include time...

We develop a transparent Bayesian approach to quantify uncertainty about linear stochastic discount factor models. We show that, for a Bayesian decision-maker, the model probability decreases with historical maximum in-sample Sharpe ratios and increases with model dimensions. We apply our...

How to best utilize the wide range of estimates of elasticities that characterize econometric literature when using calibrated models is the issue we address here through a blending of econometrics and calibration into calibmetrics. Econometrically generated literature based elasticity...

The classic 'logistic' model has provided a realistic model of the behaviour of Covid-19 in China and many East Asian countries. Once these countries passed the peak, the daily case count fell back, mirroring its initial climb in a symmetric way, just as the classic model predicts. However, in...