Likelihood-preserving normalization in multiple equation models

Daniel Waggoner and Tao Zha

No 2000-8, FRB Atlanta Working Paper from Federal Reserve Bank of Atlanta

Abstract: Causal analysis in multiple equation models often revolves around the evaluation of the effects of an exogenous shift in a structural equation. When taking into account the uncertainty implied by the shape of the likelihood, we argue that how normalization is implemented matters for inferential conclusions around the maximum likelihood (ML) estimates of such effects. We develop a general method that eliminates the distortion of finite-sample inferences about these ML estimates after normalization. We show that our likelihood-preserving normalization always maintains coherent economic interpretations while an arbitrary implementation of normalization can lead to ill-determined inferential results.

Keywords: time series analysis; Supply and demand; Demand for money; Money supply (search for similar items in EconPapers)
Date: 2000
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (13)

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Journal Article: Likelihood preserving normalization in multiple equation models (2003)
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