scholarly journals Time-Varying Mixing Weights in Mixture Autoregressive Conditional Duration Models

2005 ◽  
Author(s):  
Giampiero M. Gallo ◽  
Giovanni De Luca
Keyword(s):  
Duration Models ◽  
Time Varying ◽  
Autoregressive Conditional Duration

Time Varying Trade Intensities and the Deutsche Telekom IPO / Zeitvariable Handelsintensitaten und die Deutsche Telekom IPO

2000 ◽  
Vol 220 (6) ◽  
Author(s):  
Reinhard Hujer ◽  
Joachim Grammig ◽  
Stefan Kokot
Keyword(s):  
Initial Public Offering ◽  
Structural Breaks ◽  
Duration Model ◽  
Time Varying ◽  
Threshold Autoregressive ◽  
Public Offering ◽  
Autoregressive Conditional Duration ◽  
Autoregressive Conditional Duration Model

SummaryWe apply the Threshold Autoregressive Conditional Duration Model (TACD) as proposed by Zhang, Russell, and Tsay (1999) to model the after market trading duration process associated with the initial public offering of the Deutsche Telekom AG share in November of 1996. Special emphasis is devoted to the empirical specification of intra-day seasonality and to the detection of non-stationarity and structural breaks in the trading process.

Autoregressive conditional duration models: an application in the Brazilian stock market

2016 ◽  
Vol 10 ◽  
pp. 1573-1594
Author(s):  
Mauri Aparecido de Oliveira ◽  
Ricardo Luiz Pereira Bueno ◽  
Lais Sayuri Kotsubo ◽  
Daniel Reed Bergmann
Keyword(s):  
Stock Market ◽  
Duration Models ◽  
Autoregressive Conditional Duration

scholarly journals Proper Specification of Nonproportional Hazards Corrections in Duration Models

2017 ◽  
Vol 25 (1) ◽  
pp. 138-144 ◽  
Author(s):  
Shuai Jin ◽  
Frederick J. Boehmke
Keyword(s):  
Proportional Hazards ◽  
Model Specification ◽  
Duration Models ◽  
Time Varying ◽  
Nonproportional Hazards ◽  
Incorrect Model ◽  
Proper Estimation ◽  
Time Invariant ◽  
Time Varying Covariates ◽  
Biased Estimates

Parametric and nonparametric duration models assume proportional hazards: The effect of a covariate on the hazard rate stays constant over time. Researchers have developed techniques to test and correct nonproportional hazards, including interacting the covariates with some function of time. Including this interaction term means that the specification now involves time-varying covariates, and the model specification should reflect this feature. However, in situations with no time-varying covariates initially, researchers often continue to model the duration with only time-invariant covariates. This error results in biased estimates, particularly for the covariates interacted with time. We investigate this issue in over forty political science articles and find that of those studies that begin with time-invariant covariates and correct for nonproportional hazards the majority suffer from incorrect model specification. Proper estimation usually produces substantively or statistically different results.

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