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On diagnostic checking in ARMA models with conditionally heteroscedastic martingale difference using wavelet methods
- Source :
- Econometrics and Statistics.
- Publication Year :
- 2021
- Publisher :
- Elsevier BV, 2021.
-
Abstract
- Wavelet-based tests for lack-of-fit in semi-strong autoregressive moving average models with conditional heteroscedastic martingale difference innovations are investigated. The chi-square distributions of the Box-Pierce-Ljung methods are not necessarily adequate in this context and adjustments appear necessary. Seasonal irregularities in the spectral density of the innovations can affect the power of the classical tests, providing motivations for studying wavelets. Using the Franklin wavelet, the asymptotic distributions of the empirical wavelet coefficients are derived, and the asymptotic chi-square distributions of the wavelet-based tests are established. Monte Carlo simulations are conducted to study the performance of the methodology under the null and alternative hypotheses, including seasonal alternatives.
- Subjects :
- Statistics and Probability
Economics and Econometrics
Heteroscedasticity
05 social sciences
Monte Carlo method
Null (mathematics)
Context (language use)
01 natural sciences
010104 statistics & probability
Wavelet
Goodness of fit
0502 economics and business
Applied mathematics
Autoregressive–moving-average model
Martingale difference sequence
0101 mathematics
Statistics, Probability and Uncertainty
050205 econometrics
Mathematics
Subjects
Details
- ISSN :
- 24523062
- Database :
- OpenAIRE
- Journal :
- Econometrics and Statistics
- Accession number :
- edsair.doi...........210553a1040d9c47b69268f17affd14b