Title: Estimating New-Keynesian Phillips Curves: A Full Information Maximum Likelihood Approach
Abstract: The New-Keynesian Phillips curve has recently become an important ingredient in monetary policy models. However, using limited information methods, the empirical support for the New-Keynesian Phillips curve appear to be mixed. This paper argues, by means of Monte Carlo simulations with a simple New-Keynesian sticky price model, that single equations methods, e.g. GMM, are likely to produce imprecise and biased estimates. Then, it is argued that estimating the model with full information maximum likelihood (FIML) is a useful way of obtaining better estimates. Finally, a version of the model used in the Monte Carlo simulations is estimated on U.S. data with FIML and although the pure forward-looking New-Keynesian Phillips curve is rejected, a version with both forward- and backward-looking components provides a reasonable approximation of U.S. inflation dynamics.
Publication Year: 2005
Publication Date: 2005-03-01
Language: en
Type: preprint
Access and Citation
AI Researcher Chatbot
Get quick answers to your questions about the article from our AI researcher chatbot