Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/100790 
Year of Publication: 
2000
Series/Report no.: 
Working Paper No. 2000-8
Publisher: 
Federal Reserve Bank of Atlanta, Atlanta, GA
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.
Subjects: 
Time-series analysis
Supply and demand
Demand for money
Money supply
Document Type: 
Working Paper

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