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Working Paper
Posterior simulators in econometrics
Working Paper
Simulation-based Bayesian inference for economic time series
This paper surveys recently developed methods for Bayesian inference and their use in economic time series models. It begins by reviewing aspects of Bayesian inference essential to understanding the implications of the Bayesian paradigm for time series analysis. It next describes the use of posterior simulators to solve otherwise intractable analytical problems. The theory and the computational advances are brought together in setting forth a practical framework for decision-making and forecasting. These developments are illustrated in the context of the vector autoregressions, stochastic ...
Working Paper
Bayesian comparison of econometric models
Working Paper
Variable selection and model comparison in regression
In the specification of linear regression models it is common to indicate a list of candidate variables from which a subset enters the model with nonzero coefficients. This paper interprets this specification as a mixed continuous-discrete prior distribution for coefficient values. It then utilizes a Gibbs sampler to construct posterior moments. It is shown how this method can incorporate sign constraints and provide posterior probabilities for all possible subsets of regressors. The methods are illustrated using some standard data sets.
Working Paper
Bayesian inference for hospital quality in a selection model
This paper develops new econometric methods to infer hospital quality in a model with discrete dependent variables and non-random selection. Mortality rates in patient discharge records are widely used to infer hospital quality. However, hospital admission is not random and some hospitals may attract patients with greater unobserved severity of illness than others. In this situation the assumption of random admission leads to spurious inference about hospital quality. This study controls for hospital selection using a model in which distance between the patient?s residence and alternative ...
Journal Article
A fine time for monetary policy?
Recent research in evaluating the effects of monetary policy is potentially tainted by the problem of time aggregation: that is, effects may be incorrectly estimated using quarterly data if the effects of policy occur rapidly. This study evaluates whether time aggregation is a serious problem in a simple vector autoregression. It shows time aggregation has little impact on evaluating the effect of monetary policy in a simple vector autoregression including total reserves, nonborrowed reserves, and the federal funds rate. This finding suggests that time aggregation is unlikely to be important ...
Discussion Paper
Priors for macroeconomic time series and their application
This paper takes up Bayesian inference in a general trend stationary model for macroeconomic time series with independent Student-t disturbances. The model is linear in the data, but nonlinear in parameters. An informative but nonconjugate family of prior distributions for the parameters is introduced, indexed by a single parameter which can be readily elicited. The main technical contribution is the construction of posterior moments, densities, and odds ratios using a six-step Gibbs sampler. Mappings from the index parameter of the family of prior distribution to posterior moments, ...
Working Paper
Bayesian inference for linear models subject to linear inequality constraints
The normal linear model, with sign or other linear inequality constraints on its coefficients, arises very commonly in many scientific applications. Given inequality constraints Bayesian inference is much simpler than classical inference, but standard Bayesian computational methods become impractical when the posterior probability of the inequality constraints (under a diffuse prior) is small. This paper shows how the Gibbs sampling algorithm can provide an alternative, attractive approach to inference subject to linear inequality constraints in this situation, and how the GHK probability ...
Working Paper
Bayesian reduced rank regression in econometrics
The reduced rank regression model arises repeatedly in theoretical and applied econometrics. To date the only general treatments of this model have been frequentist. This paper develops general methods for Bayesian inference with noninformative reference priors in this model, based on a Markov chain sampling algorithm, and procedures for obtaining predictive odds ratios for regression models with different ranks. These methods are used to obtain evidence on the number of factors in a capital asset pricing model.