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Identification of Mixed Causal-Noncausal Models in Finite Samples
- Source :
- Annals of Economics and Statistics, 123/124:11, 307-331. Groupe des Ecoles Nationales d'Economie et Statistique (GENES)
- Publication Year :
- 2016
- Publisher :
- Groupe des Ecoles Nationales d'Economie et Statistique (GENES), 2016.
-
Abstract
- Gouriéroux and Zakoïan (2013) propose to use noncausal models to parsimoniously capture nonlinear features often observed in financial time series and in particular bubble phenomena. In order to distinguish causal autoregressive processes from purely noncausal or mixed causal-noncausal ones, one has to depart from the Gaussianity assumption on the error distribution. Financial (and to a large extent macroeconomic) data are characterized by large and sudden changes that cannot be capturedby the Normal distribution, which explains why leptokurtic error distributions are often considered in empirical finance. By means of Monte Carlo simulations, this paper investigates the identication of mixed causal-noncausal models in finite samples for different values of the excess kurtosis of the error process. We compare the performance of the MLE, assuming a t-distribution, with that of theLAD estimator that we propose in this paper. Similar to Davis, Knight and Liu (1992) we find that for infinite variance autoregressive processes both the MLE and LAD estimator converge faster. We further specify the general asymptotic normality results obtained in Andrews, Breidt and Davis (2006)for the case of t-distributed and Laplacian distributed error terms. We first illustrate our analysis by estimating mixed causal-noncausal autoregressions to model the demand for solar panels in Belgium over the last decade. Then we look at the presence of potential noncausal components in daily realizedvolatility measures for 21 equity indexes. The presence of a noncausal component is confirmed in both empirical illustrations.
- Subjects :
- Statistics and Probability
Economics and Econometrics
Realized variance
Non-Gaussian distributions
Monte Carlo method
Single Equation Models
Single Variables: Time-Series Models
Dynamic Quantile Regressions
Dynamic Treatment Effect Models
Asymptotic distribution
Noncausal models
01 natural sciences
Normal distribution
Business Fluctuations
bubbles
010104 statistics & probability
0502 economics and business
Econometrics
e37 - Prices
0101 mathematics
050205 econometrics
Mathematics
and Cycles: Forecasting and Simulation: Models and Applications
e37 - Prices, Business Fluctuations, and Cycles: Forecasting and Simulation: Models and Applications
Prices
Series (mathematics)
05 social sciences
Estimator
Financial Markets and the Macroeconomy
Autoregressive model
e44 - Financial Markets and the Macroeconomy
Kurtosis
Statistics, Probability and Uncertainty
c22 - "Single Equation Models
Dynamic Treatment Effect Models"
Social Sciences (miscellaneous)
Realized volatilities
Subjects
Details
- Language :
- English
- ISSN :
- 21154430
- Database :
- OpenAIRE
- Journal :
- Annals of Economics and Statistics
- Accession number :
- edsair.doi.dedup.....6cfb093ed9bc5b06a76155e8ac5825f2