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Understanding DSGE Filters in Forecasting and Policy Analysis, Michal Andrle

This paper introduces methods that allow analysts to (i) decompose the estimates of unobserved quantities into observed data, (ii) to better understand revision properties of the model, and (iii) to impose subjective prior constraints on path estimates of unobserved shocks in structural economic models. For instance, a decomposition of the flexible-price output gap, or a technology shock, into contributions of output, inflation, interest rates, and other observed variables' contribution is feasible. The intuitive nature and analytical clarity of the suggested procedures are appealing for policy-related and forecasting models
Table Of Contents
Cover; Contents; I. Introduction; II. Theoretical background; A. Filter representation of a state-space model; B. Weights and Practical Implementation of the Method; C. Effects of Data Revisions; D. Effects of New Data Releases; III. Missing Observations and Imposing Judgement on the Filter; A. Missing observations and time-varying parameters; B. Imposing Judgement; IV. Examples and Applications; A. Flexible price output gap and real marginal costs in Smets and Wouters (2007); Figures; 1. Flexible-price output gap in SW07; 2. Real marginal costs in SW07; 3. Filter weights and gain SW07
Literary Form
non fiction
Description based upon print version of record
Physical Description
1 online resource (24 p.)
Specific Material Designation
Form Of Item

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