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Evaluating the New Keynesian Phillips Curve under VAR-based learning Author info | Abstract | Publisher info | Download info | Related research | Statistics Fanelli, Luca
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This paper proposes the evaluation of the New Keynesian Phillips Curve (NKPC) under a new learning mechanism where VAR learning dynamics is combined with the idea of testing the validity of the forward-looking model of inflation dynamics. The key assumption is that agents’ perceived law of motion is a VAR whose parameters are updated by recursive least squares. Differently from standard adaptive learning methods, agents test sequentially the cross-equation restrictions that the NKPC imposes on the VAR as the information set increases. When the restrictions are not rejected agents learn under the restricted system and exploit the cross-equation restrictions to forecast inflation. It is thus possible to check how much and in which periods agents’ beliefs are consistent with the restrictions of the theory. The empirical analysis on quarterly data on the euro area shows that the NKPC with negligible backward-looking parameter is not rejected when the model is evaluated over the period 1984-2005 under the proposed learning mechanism. The result, however, is not fully robust to specifications based on non stationary variables and points out that learning may represent a remarkable source of euro area inflation persistence but not its only determinant.
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Paper provided by University Library of Munich, Germany in its series MPRA Paper with number
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Date of creation: Jan 2007Date of revision:
Handle: RePEc:pra:mprapa:1616Contact details of provider: Postal: Schackstr. 4, D-80539 Munich, Germany Phone: +49-(0)89-2180-2219 Fax: +49-(0)89-2180-3900 Web page: http://mpra.ub.uni-muenchen.de More information through EDIRC
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Keywords: Adaptive learning Cross-equation restrictions Forward-looking model of inflation dynamics Perceived Law of Motion Recursive Least Squares VAR. Other versions of this item:
Find related papers by JEL classification: C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation and Testing E10 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - General D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search, Learning, and Information
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