On the residual autocorrelation of the autoregressive conditional duration model

DOI10.1016/S0165-1765(02)00303-8zbMath1254.91677OpenAlexW1995090621MaRDI QIDQ1927300

Wai-Keun Li, Philip L. H. Yu

Publication date: 1 January 2013

Published in: Economics Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0165-1765(02)00303-8

zbMATH Keywords

asymptotic distributiongoodness-of-fit testresidual autocorrelationsautoregressive conditional duration models

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Asymptotic distribution theory in statistics (62E20) Economic time series analysis (91B84)

Related Items (10)

Evaluating multiplicative error models: a residual-based approach ⋮ Evaluating financial time series models for irregularly spaced data: a spectral density approach ⋮ Diagnostic checking of the vector multiplicative error model ⋮ On the asymptotic distribution of the residual autocovariance matrices in the autoregressive conditional multinomial model ⋮ A misspecification test for multiplicative error models of non-negative time series processes ⋮ Some weighted mixed portmanteau tests for diagnostic checking in linear time series models ⋮ On diagnostic checking of the autoregressive conditional intensity model ⋮ On Diagnostic Checking Autoregressive Conditional Duration Models with Wavelet-Based Spectral Density Estimators ⋮ Diagnostic Checking for Weibull Autoregressive Conditional Duration Models ⋮ Detecting misspecifications in autoregressive conditional duration models and non-negative time-series processes

Cites Work

• Significance levels of the Box-Pierce portmanteau statistic in finite samples
• ON THE SQUARED RESIDUAL AUTOCORRELATIONS IN NON-LINEAR TIME SERIES WITH CONDITIONAL HETEROSKEDASTICITY
• Autoregressive Conditional Duration: A New Model for Irregularly Spaced Transaction Data
• Distribution of Residual Autocorrelations in Autoregressive-Integrated Moving Average Time Series Models

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