LASSO estimation of threshold autoregressive models

2015 ◽  
Vol 189 (2) ◽  
pp. 285-296 ◽  
Author(s):  
Ngai Hang Chan ◽  
Chun Yip Yau ◽  
Rong-Mao Zhang
Keyword(s):  
Autoregressive Models ◽  
Threshold Autoregressive

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

Mixture models for time series

1995 ◽  
Vol 32 (01) ◽  
pp. 123-138
Author(s):  
Assad Jalali ◽  
John Pemberton
Keyword(s):  
Time Series ◽  
General Theory ◽  
Mixture Models ◽  
Time Reversal ◽  
General Class ◽  
Zero Order ◽  
Autoregressive Models ◽  
Threshold Models ◽  
Threshold Autoregressive ◽  
New Class

In this paper we extend the class of zero-order threshold autoregressive models to a much richer class of mixture models. The new class has the important property of duality which, as we show, corresponds to time reversal. We are then able to obtain the time reversals of the zero-order threshold models and to characterise the time-reversible members of this subclass. These turn out to be quite trivial. The time-reversible models of the more general class do not suffer in this way. The complete stationary distributional structure is given, as are various moments, in particular the autocovariance function. This is shown to be of ARMA type. Finally we give two examples, the second of which extends from the finite to the countable mixture case. The general theory for this extension will be given elsewhere.

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