Modeling Expectations with Noncausal Autoregressions
AbstractThis paper is concerned with univariate noncausal autoregressive models and their potential usefulness in economic applications. We argue that noncausal autoregressive models are especially well suited for modeling expectations. Unlike conventional causal autoregressive models, they explicitly show how the considered economic variable is affected by expectations and how expectations are formed. Noncausal autoregressive models can also be used to examine the related issue of backward-looking or forward-looking dynamics of an economic variable. We show in the paper how the parameters of a noncausal autoregressive model can be estimated by the method of maximum likelihood and how related test procedures can be obtained. Because noncausal autoregressive models cannot be distinguished from conventional causal autoregressive models by second order properties or Gaussian likelihood, a detailed discussion on their specification is provided. Motivated by economic applications we explicitly use a forward-looking autoregressive polynomial in the formulation of the model. This is different from the practice used in previous statistics literature on noncausal autoregressions and, in addition to its economic motivation, it is also convenient from a statistical point of view. In particular, it facilitates obtaining likelihood based diagnostic tests for the specified orders of the backward-looking and forward-looking autoregressive polynomials. Such test procedures are not only useful in the specification of the model but also in testing economically interesting hypotheses such as whether the considered variable only exhibits forward-looking behavior. As an empirical application, we consider modeling the U.S. inflation dynamics which, according to our results, is purely forward-looking.
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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 8411.
Date of creation: 2008
Date of revision:
Noncausal autoregression; expectations; inflation persistence;
Other versions of this item:
- Markku Lanne & Pentti Saikkonen, 2008. "Modeling Expectations with Noncausal Autoregressions," Economics Working Papers ECO2008/20, European University Institute.
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
- E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
This paper has been announced in the following NEP Reports:
- NEP-ALL-2008-04-29 (All new papers)
- NEP-CBA-2008-04-29 (Central Banking)
- NEP-ECM-2008-04-29 (Econometrics)
- NEP-ETS-2008-04-29 (Econometric Time Series)
- NEP-MAC-2008-04-29 (Macroeconomics)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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Research Discussion Papers
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"Bayesian Model Selection and Forecasting in Noncausal Autoregressive Models,"
23646, University Library of Munich, Germany.
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"Has U.S. Inflation Really Become Harder to Forecast?,"
29992, University Library of Munich, Germany.
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- Lanne, Markku & Luoto, Jani & Saikkonen, Pentti, 2010.
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23648, University Library of Munich, Germany.
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- Lanne, Markku & Saikkonen, Pentti, 2009.
"GMM Estimation with Noncausal Instruments,"
23649, University Library of Munich, Germany.
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