GENERALIZED AUTOREGRESSIVE SCORE MODELS WITH APPLICATIONS

Journal of Applied Econometrics - Tập 28 Số 5 - Trang 777-795 - 2013
Drew Creal1, Siem Jan Koopman2,3, André Lucas4,3
1University of Chicago Booth School of Business, Chicago, IL, USA#TAB#
2Department of Econometrics, VU University Amsterdam, Netherlands
3Tinbergen Institute, Amsterdam, Netherlands
4Department of Finance VU University Amsterdam, and Duisenberg School of Finance Amsterdam Netherlands

Tóm tắt

SUMMARYWe propose a class of observation‐driven time series models referred to as generalized autoregressive score (GAS) models. The mechanism to update the parameters over time is the scaled score of the likelihood function. This new approach provides a unified and consistent framework for introducing time‐varying parameters in a wide class of nonlinear models. The GAS model encompasses other well‐known models such as the generalized autoregressive conditional heteroskedasticity, autoregressive conditional duration, autoregressive conditional intensity, and Poisson count models with time‐varying mean. In addition, our approach can lead to new formulations of observation‐driven models. We illustrate our framework by introducing new model specifications for time‐varying copula functions and for multivariate point processes with time‐varying parameters. We study the models in detail and provide simulation and empirical evidence. Copyright © 2012 John Wiley & Sons, Ltd.

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Tài liệu tham khảo

10.1016/S0304-4076(95)01749-6

10.1093/jjfinec/nbj013

10.1198/016214503388619238

10.1016/0304-4076(86)90063-1

10.2307/1925546

CipolliniF EngleRF GalloGM.2006.Vector multiplicative error models: representation and inference. NBER Working Paper.

Cox DR, 1981, Statistical analysis of time series: some recent developments, Scandinavian Journal of tatistics, 8, 93

10.1080/07474938.2011.607333

10.1198/jbes.2011.10070

10.1093/biomet/90.4.777

10.1007/s11009-005-1480-4

DiasA EmbrechtsP.2004.Dynamic copula models for multivariate high‐frequency data in finance. ETH Zurich.

10.1111/j.1540-6261.2009.01495.x

10.2307/1912773

10.1198/073500102288618487

10.1002/jae.683

10.1080/07474938608800095

10.1016/j.jeconom.2005.01.018

10.1198/073500104000000370

10.2307/2999632

10.1002/(SICI)1099-1255(199607)11:4<399::AID-JAE401>3.0.CO;2-R

10.1198/jbes.2009.0016

10.1002/jae.1197

10.2307/1912559

Harvey AC, 1989, Forecasting, Structural Time Series Models and the Kalman Filter

HarveyAC ChakravartyT.2008.Beta–t–(E)GARCH. Working Papers in Economics Faculty of Economics Cambridge University UK.

10.1093/biomet/68.1.165

10.1016/j.jeconom.2007.07.001

10.1016/j.jeconom.2011.02.003

10.1016/j.jeconom.2008.12.008

10.2307/2938260

10.1111/j.1468-2354.2006.00387.x

10.1007/978-3-540-71297-8_34

RussellJR.2001.Econometric modeling of multivariate irregularly‐spaced high‐frequency data. University of Chicago Graduate School of Business.

10.1198/073500104000000541

ShephardN.1995.Generalized linear autoregressions. Nuffield College University of Oxford.

Shephard N, 2005, Stochastic Volatility: Selected Readings, 10.1093/oso/9780199257195.001.0001

10.1093/biomet/84.3.653

SklarA.1959.Fonctions de répartition à n dimensions et leurs marges. Publications de l'Institut de Statistique de L'Université de Paris 8; 229–231.

10.1214/009053606000000803

10.1016/j.insmatheco.2005.01.008

10.1017/CCOL0521252806

Wooldridge JM, 1994, Handbook of Econometrics, 2639, 10.1016/S1573-4412(05)80014-5

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Journal of Applied Econometrics

Tập 28 Số 5

777-795

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