Simulation of time series using periodic gamma autoregressive models

2018 ◽  
Vol 26 (4) ◽  
pp. 1315-1338 ◽  
Author(s):  
Victor Eduardo Leite de Almeida Duca ◽  
Reinaldo Castro Souza ◽  
Pedro Guilherme Costa Ferreira ◽  
Fernando Luiz Cyrino Oliveira
Keyword(s):  
Time Series ◽  
Autoregressive Models

scholarly journals Wasserstein Autoregressive Models for Density Time Series

2021 ◽  
Author(s):  
Chao Zhang ◽  
Piotr Kokoszka ◽  
Alexander Petersen
Keyword(s):  
Time Series ◽  
Autoregressive Models

scholarly journals Change-point detection, segmentation, and related topics

2020 ◽  
Vol 68 ◽  
pp. 97-122
Author(s):  
Jean-Marc Bardet ◽  
Vincent Brault ◽  
Serguei Dachian ◽  
Farida Enikeeva ◽  
Bruno Saussereau
Keyword(s):  
Time Series ◽  
Change Point ◽  
Point Estimation ◽  
Change Point Detection ◽  
Autoregressive Models ◽  
Change Point Estimation ◽  
Adaptive Penalty ◽  
Singularity Point ◽  
Dependency Structures ◽  
Point Detection

Recent contributions to change-point detection, segmentation and inference for non-regular models are presented. Various problems are considered including the multiple change-point estimation with adaptive penalty for time series with different dependency structures, estimation of the singularity point in cusp-type models, inference for thresholded autoregressive models, and cross-segmentation of matrices.

scholarly journals What Proportion of Time is a Particular Market Inefficient? … A Method for Analysing the Frequency of Market Efficiency when Equity Prices Follow Threshold Autoregressions

2018 ◽  
Vol 10 (2) ◽  
Author(s):  
Muhammad Farid Ahmed ◽  
Stephen Satchell
Keyword(s):  
Time Series ◽  
Market Efficiency ◽  
Stationary Process ◽  
Equity Returns ◽  
Autoregressive Models ◽  
Asset Markets ◽  
Equity Prices ◽  
Threshold Autoregressive ◽  
Time Series Econometrics ◽  
The Impact

Abstract We assume that equity returns follow multi-state threshold autoregressions and generalize existing results for threshold autoregressive models presented in Knight and Satchell 2011. “Some new results for threshold AR(1) models,” Journal of Time Series Econometrics 3(2011):1–42 and Knight, Satchell, and Srivastava (2014) for the existence of a stationary process and the conditions necessary for the existence of a mean and a variance; we also present formulae for these moments. Using a simulation study, we explore what these results entail with respect to the impact they can have on tests for detecting bubbles or market efficiency. We find that bubbles are easier to detect in processes where a stationary distribution does not exist. Furthermore, we explore how threshold autoregressive models with i.i.d trigger variables may enable us to identify how often asset markets are inefficient. We find, unsurprisingly, that the fraction of time spent in an efficient state depends upon the full specification of the model; the notion of how efficient a market is, in this context at least, a model-dependent concept. However, our methodology allows us to compare efficiency across different asset markets.

Threshold Autoregressive Models for Directional Time Series

2016 ◽  
pp. 13-25 ◽  
Author(s):  
Mahayaudin M. Mansor ◽  
Max E. Glonek ◽  
David A. Green ◽  
Andrew V. Metcalfe
Keyword(s):  
Time Series ◽  
Autoregressive Models ◽  
Threshold Autoregressive

Maximum likelihood estimates of incorrect Markov models for time series and the derivation of AIC

1980 ◽  
Vol 17 (01) ◽  
pp. 59-72 ◽  
Author(s):  
Yosihiko Ogata
Keyword(s):  
Time Series ◽  
Asymptotic Behavior ◽  
Maximum Likelihood ◽  
Markov Models ◽  
Maximum Likelihood Estimators ◽  
Information Criterion ◽  
Parametric Family ◽  
Autoregressive Models ◽  
Maximum Likelihood Estimates ◽  
True Distribution

The asymptotic behavior of the maximum likelihood estimators of Markov models or autoregressive models are given when the true distribution is not a member of the assumed parametric family. The derivation of Akaike's Information Criterion is reviewed for this case.

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