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2021•RePEc: Research Papers in EconomicsRequires access

Bayesian econometrics in Stata 17

David Schenck

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Abstract

Stata 17 introduced Bayesian support for many time-series and panel-data commands. In this talk, I will discuss Bayesian vector autoregression models, Bayesian DSGE models, and Bayesian panel-data models. Bayesian estimation is well suited to these models because economic considerations often impose structure that is captured well by informative priors. I will describe the main features of these commands as well as Bayesian diagnostics, posterior hypothesis tests, predictions, impulse–response functions, and forecasts.

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Stata 17 introduced Bayesian support for many time-series and panel-data commands. In this talk, I will discuss Bayesian vector autoregression models, Bayesian DSGE models, and Bayesian panel-data models. Bayesian estimation is well suited to these models because economic considerations often impose structure that is captured well by informative priors. I will describe the main features of these commands as well as Bayesian diagnostics, posterior hypothesis tests, predictions, impulse–response functions, and forecasts.

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Available abstract

Stata 17 introduced Bayesian support for many time-series and panel-data commands. In this talk, I will discuss Bayesian vector autoregression models, Bayesian DSGE models, and Bayesian panel-data models. Bayesian estimation is well suited to these models because economic considerations often impose structure that is captured well by informative priors. I will describe the main features of these commands as well as Bayesian diagnostics, posterior hypothesis tests, predictions, impulse–response functions, and forecasts.

Key concepts: Bayesian probability, Bayesian econometrics, Variable-order Bayesian network, Econometrics, Prior probability, Bayesian vector autoregression, Bayesian average, Impulse response

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