A (Semi)Parametric Functional Coefficient Logarithmic Autoregressive Conditional Duration Model

REFERENCES

• Bauwens, L., Giot, P. (2000). The logarithmic ACD model: An application to the bid-ask quote process of three NYSE stocks. Annales d'Economie et de Statistique 60:117–150.
   Google Scholar
• Boussama, F. (2000). Normalité asymptotique de l'estimateur du pseudo-maximum de vraisemblance d'un modèle GARCH. Comptes Rendus de l'Academie des Sciences, Série I 331:81–84.
   Google Scholar
• Chen, R., Tsay, R. (1991). On the ergodicity of TAR(1) processes. Annals of Applied Probability 1:613–634.
   Google Scholar
• Chen, X., Shen, X. (1998). Sieve extremum estimates for weakly dependent data. Econometrica 66:289–314.
   Web of Science ®Google Scholar
• Dette, H., Spreckelsen, I. (2004). Some comments on specification tests in nonparametric absolutely regular processes. Journal of Time Series Analysis 25:159–172.
   Web of Science ®Google Scholar
• Drost, F., Werker, B. J. M. (2004). Semiparametric duration models. Journal of Business and Economic Statistics 22:40–50.
   Web of Science ®Google Scholar
• Engle, R. F., Russell, J. R. (1998). Autoregressive conditional duration: A new model for irregularly-spaced transaction data. Econometrica 66:1127–1162.
   Web of Science ®Google Scholar
• Fernandes, M., Grammig, J. (2006). A flexible family of autoregressive conditional duration models. Journal of Econometrics 130:1–23.
   Web of Science ®Google Scholar
• Grenander, U. (1981). Abstract Inference. New York: Wiley Series.
   Google Scholar
• Hornik, K., Stinchcombe, M. B., White, H. (1989). Multilayer feedforward networks are universal approximators. Neural Networks 2:359–366.
   Web of Science ®Google Scholar
• Hwang, J. T. G., Ding, A. A. (1997). Prediction intervals for artificial neural networks. Journal of the American Statistical Association 92:109–125.
   Google Scholar
• Ling, S. (1999). On the probabilistic properties of a double-threshold arma conditional heteroskedastic model. Journal of Applied Probability 36:688–705.
   Web of Science ®Google Scholar
• Ling, S., McAleer, M. (2003). Asymptotic theory for a vector ARMA-GARCH model. Econometric Theory 19:280–310.
   Web of Science ®Google Scholar
• Lumsdaine, R. L. (1996). Consistency and asymptotic normality of the quasi-maximum likelihood estimator in IGARCH(1,1) and covariance stationary GARCH(1,1) models. Econometrica 64:575–596.
   Web of Science ®Google Scholar
• Makovoz, Y. (1996). Random approximants and neural networks. Journal of Approximation Theory 85:98–109.
   Web of Science ®Google Scholar
• Medeiros, M. C., Veiga, A. (2000). A hybrid linear-neural model for time series forecasting. IEEE Transactions on Neural Networks 11:1402–1412.
   PubMed Web of Science ®Google Scholar
• Medeiros, M. C., Veiga, A. (2009). Modeling multiple regimes in financial volatility with a flexible coefficient GARCH(1,1) model. Econometric Theory 25:117–161.
   Web of Science ®Google Scholar
• Meitz, M. (2006). A necessary and sufficient condition for the strict stationarity of a family of GARCH processes. Econometric Theory 22:985–988.
   Web of Science ®Google Scholar
• Meitz, M., Saikkonen, P. (2008). Ergodicity, mixing, and existence of moments of a class of Markov models with applications to GARCH and ACD models. Econometric Theory 24:1291–1320.
   Web of Science ®Google Scholar
• Meitz, M. Teräsvirta, T. (2006). Evaluating models of autoregressive conditional duration. Journal of Business and Economic Statistics 24:104–124.
   Web of Science ®Google Scholar
• Newey, W., McFadden, D. L. (1994). Large sample estimation and hypothesis testing. In: Engle, R. F., McFadden, D. L., eds. Handbook of Econometrics. Vol. 4. Elsevier Science, pp. 2111–2245.
   Google Scholar
• Stout, W. F. (1974). Almost Sure Convergence. New York: Academic Press.
   Google Scholar
• Suarez-Fariñas, M., Pedreira, C. E., Medeiros, M. C. (2004). Local-global neural network: A new approach for nonlinear time series modeling. Journal of the American Statistical Association 99:1092–1107.
   Web of Science ®Google Scholar
• Tjøtstheim, D. (1990). Non-linear time series and markov chains. Advances in Applied Probability 22:587–611.
   Web of Science ®Google Scholar
• Tweedie, R. L. (1975). Sufficient conditions for ergodicity and recurrence of markov chains on a general state space. Stochastic Processes and Their Applications 3:385–403.
   Google Scholar
• White, H. (1996). Estimation, Inference and Specification Analysis. New York: Cambridge University Press.
   Google Scholar
• Wooldridge, J. M. (1990). A unified approach to robust, regression-based specification tests. Econometric Theory 6:17–43.
   Web of Science ®Google Scholar
• Yoshihara, K.-i. (1976). Limiting behavior of U-statistic for stationary, absolutely regular processes. Zeitschrift für Wahrscheinlichkeitstheorie und Verwandte Gebiete 35:237–252.
   Web of Science ®Google Scholar
• Zhang, M. Y., Russell, J. R., Tsay, R. S. (2001). A nonlinear autoregressive conditional duration model with applications to financial transaction data. Journal of Econometrics 104:179–207.
   Web of Science ®Google Scholar