Lanne, Markku and Saikkonen, Pentti (2008): Modeling Expectations with Noncausal Autoregressions.
Download (359Kb) | Preview
This 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.
|Item Type:||MPRA Paper|
|Original Title:||Modeling Expectations with Noncausal Autoregressions|
|Keywords:||Noncausal autoregression; expectations; inflation persistence|
|Subjects:||C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C52 - Model Evaluation, Validation, and Selection
E - Macroeconomics and Monetary Economics > E3 - Prices, Business Fluctuations, and Cycles > E31 - Price Level; Inflation; Deflation
C - Mathematical and Quantitative Methods > C2 - Single Equation Models; Single Variables > C22 - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
|Depositing User:||Markku Lanne|
|Date Deposited:||23. Apr 2008 14:53|
|Last Modified:||28. Feb 2013 22:13|
Andrews, B., R.A. Davis, and F.J. Breidt (2006). Maximum likelihood estimation for all-pass time series models. Journal of Multivariate Analysis 97, 1638-1659.
Andrews, D. and W. Chen (1994). Approximately median-unbiased estimation of autoregressive models. Journal of Business and Economic Statistics 12, 187�-204.
Breidt, J., R.A. Davis, K.S. Lii, and M. Rosenblatt (1991). Maximum likelihood estimation for noncausal autoregressive processes. Journal of Multivariate Analysis 36, 175-198.
Breidt, J., R.A. Davis, and A.A. Trindade (2001). Least absolute deviation estimation for all-pass time series models. The Annals of Statistics 29, 919-946.
Brockwell, P.J. and R.A. Davis (1987). Time Series: Theory and Methods. Springer-Verlag. New York.
Campbell, J.Y., A.W. Lo, and A.C. MacKinlay (1997). Econometrics of Financial Markets. Princeton University Press. Princeton.
Canova, F. (2007). Methods for Applied Macroeconomic Research. Princeton University Press. Princeton.
Cecchetti, S.G. and G. Debelle (2006). Has the inflation process changed? Economic Policy, April 2006, 311-�352.
Huang, J. and Y. Pawitan (2000). Quasi-likelihood estimation of noninvertible moving average processes. Scandinavian Journal of Statistics 27, 689-710.
Lii, K.-S. and M. Rosenblatt (1996). Maximum likelihood estimation for non-Gaussian nonminimum phase ARMA sequences. Statistica Sinica 6, 1-22.
Rosenblatt, M. (2000). Gaussian and Non-Gaussian Linear Time Series and Random Fields. Springer-Verlag, New York.
White, H. (1994). Estimation, Inference and Specification Analysis. Cambridge University Press. New York.
Available Versions of this Item
- Modeling Expectations with Noncausal Autoregressions. (deposited 23. Apr 2008 14:53) [Currently Displayed]