<--- Back to Details
First PageDocument Content
Time series analysis / Statistics / Time series models / Autoregressive conditional heteroskedasticity / Noise / CUSUM / Time series / Autoregressive conditional duration / Economic model / Autoregressive model / Parameter / ACD
Date: 2016-06-28 03:34:41
Time series analysis
Statistics
Time series models
Autoregressive conditional heteroskedasticity
Noise
CUSUM
Time series
Autoregressive conditional duration
Economic model
Autoregressive model
Parameter
ACD

Ann Inst Stat Math:621–637 DOIs10463x Parameter change test for autoregressive conditional duration models Sangyeol Lee1 · Haejune Oh1

Add to Reading List

Source URL: www.ism.ac.jp

Download Document from Source Website

File Size: 277,35 KB

Share Document on Facebook

Similar Documents

Advanced time-series analysis (University of Lund, Economic History Department) 30 Jan-3 February andMarch 2012 Lecture 4 Econometric techniques for stationary series 1: Univariate stochastic models with BoxJenkin

Advanced time-series analysis (University of Lund, Economic History Department) 30 Jan-3 February andMarch 2012 Lecture 4 Econometric techniques for stationary series 1: Univariate stochastic models with BoxJenkin

DocID: 1veTy - View Document

Advanced time-series analysis (University of Lund, Economic History Department) 30 Jan-3 February andMarch 2012 Lecture 5 Econometric techniques for stationary series 2: Distributed lag models, ARX type models, Ko

Advanced time-series analysis (University of Lund, Economic History Department) 30 Jan-3 February andMarch 2012 Lecture 5 Econometric techniques for stationary series 2: Distributed lag models, ARX type models, Ko

DocID: 1v1s9 - View Document

Advanced time-series analysis (University of Lund, Economic History Department) 30 Jan-3 February andMarch 2012 Lecture 7 Conditional heteroscedasticity models: ARCH and GARCH techniques and their applications.  7

Advanced time-series analysis (University of Lund, Economic History Department) 30 Jan-3 February andMarch 2012 Lecture 7 Conditional heteroscedasticity models: ARCH and GARCH techniques and their applications. 7

DocID: 1u4tE - View Document

BoydstunMaking the News: Politics, the Media and Agenda Setting Table 5.4. Results from pooled cross-sectional time series models of Times front-page attention All Policy Topics Coefficients

BoydstunMaking the News: Politics, the Media and Agenda Setting Table 5.4. Results from pooled cross-sectional time series models of Times front-page attention All Policy Topics Coefficients

DocID: 1tFJm - View Document

Simultaneous Learning of Nonlinear Manifold and Dynamical Models for High-dimensional Time Series Rui Li, Tai-Peng Tian and Stan Sclaroff ∗ Computer Science Department, Boston University {lir, tiantp, sclaroff}@cs.bu.e

Simultaneous Learning of Nonlinear Manifold and Dynamical Models for High-dimensional Time Series Rui Li, Tai-Peng Tian and Stan Sclaroff ∗ Computer Science Department, Boston University {lir, tiantp, sclaroff}@cs.bu.e

DocID: 1tEFE - View Document