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Working Paper
IDENTIFICATION THROUGH HETEROGENEITY
We analyze set identification in Bayesian vector autoregressions (VARs). Because set identification can be challenging, we propose to include micro data on heterogeneous entities to sharpen inference. First, we provide conditions when imposing a simple ranking of impulse-responses sharpens inference in bivariate and trivariate VARs. Importantly; we show that this set reduction also applies to variables not subject to ranking restrictions. Second, we develop two types of inference to address recent criticism: (1) an efficient fully Bayesian algorithm based on an agnostic prior that directly ...
Working Paper
Refining Set-Identification in VARs through Independence
Identification in VARs has traditionally mainly relied on second moments. Some researchers have considered using higher moments as well, but there are concerns about the strength of the identification obtained in this way. In this paper, we propose refining existing identification schemes by augmenting sign restrictions with a requirement that rules out shocks whose higher moments significantly depart from independence. This approach does not assume that higher moments help with identification; it is robust to weak identification. In simulations we show that it controls coverage well, in ...
Working Paper
When Is the Use of Gaussian-inverse Wishart-Haar Priors Appropriate?
Several recent studies have expressed concern that the Haar prior typically employed in estimating sign-identified VAR models is driving the prior about the structural impulse responses and hence their posterior. In this paper, we provide evidence that the quantitative importance of the Haar prior for posterior inference has been overstated. How sensitive posterior inference is to the Haar prior depends on the width of the identified set of a given impulse response. We demonstrate that this width depends not only on how much the identified set is narrowed by the identifying restrictions ...