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“Explaining business cycles: News versus data revisions” Levine, Pearlman and Yang Discussion Frank Smets European Central Bank MONFISPOL Final Conference Frankfurt am Main, 19-20 September 2011 Overview • This paper is part of a larger research project in which Levine and Pearlman with co-authors analyse the empirical relevance of imperfect information for business cycle analysis. • This builds on the work of Pearlman et al (1986), Svensson and Woodford (2000) and others: Rational expectations; perfect knowledge of the structure of the economy; possibly imperfect knowledge of the nature of the shock processes. • Different from other models of imperfect information: – Rational inattention (Sims, Mackowiak and Wiederholt, …); – Bayesian learning of structural parameters (Wieland, …); Overview • In this paper, Levine et al compare a simple model with and without news shocks using revised final data and a model with and without imperfect information using real-time data. – The model is the basic New Keynesian model without habit formation or inflation indexation – Only real GDP and inflation are used as observable variables. What about the short-term interest rate? • The paper mixes three elements: news shocks, imperfect information and real-time data, but does not systematically explore all. • The paper is incomplete. Need better description of what is done and more validation. Overview Overview Overview: Results • Under perfect information (AI), the specification of the model with an AR(1) process is marginally more likely than the one which also includes a news process, ARMA(1,4). • Using the real-time data set including two revisions, the imperfect information case without news outperforms the other specifications. However, it is not clear what it means to use the real-time data set under the assumption of AI. Questions • How well are the various shocks and parameters identified? – E.g. Is it really possible to distinguish the AR(1) and news shocks and test their relative importance? It would be good to show the variance decomposition and the impulse responses to analyse the differences between both shocks. – Compare the prior and posterior likelihood function around the estimated parameters. – Apply the method of Pesaran et al (2011) to test identification. – How does one decide on the form of the news process? Questions • Which information set should one use? Which set of shocks should this be compared with? – E.g. the model also contains hours worked. Observing this variable would be very helpful in pinning down the productivity process. – How robust are the results to alternative informational assumptions? – One drawback of the Bayesian approach is that it is not that easy to compare results using different data sets. The marginal likelihood is conditional on the observed data. Questions • The most original part of the paper is the use of real-time data to identify expected future innovations. – Question: Can the inference of all shocks change as measurement error hits? – It would be good to show how much of the actual movements in output and inflation are due to measurement error; – What are the impulse responses to measurement error shocks? How do the impulse responses differ across the AI and II case? – Show how some key unobservable concepts like the output gap change. Questions • The most original part of the paper is the use of real-time data to identify expected future innovations. – How do you deal with the later revisions? – Describe the size of the measurement errors in the data section. – The paper need to better explain why with the realtime data set the II case does so much better. – Does the same hold true if only the revised data set is used? Why wasn’t the II case considered in the case without measurement error.