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References

1
Allenby, G. and Rossi, P. (1999).
Marketing models of consumer heterogeneity.
Journal of Econometrics, 89:57-78.

2
Bauwens, L. and Giot, P. (2001).
Econometric Modelling of Stock Market Intraday Activity.
Kluwer.

3
Bauwens, L. and Hautsch, N. (2003).
Dynamic latent factor models for intensity processes.
CORE DP 2003/103.

4
Bauwens, L. and Rombouts, J. (2003).
Bayesian clustering of many GARCH models.
CORE DP 2003/87.

5
Bauwens, L. and Veredas, D. (2004).
The stochastic conditional duration model: a latent factor model for the analysis of financial durations.
Forthcoming in Journal of Econometrics.

6
Bhat, C. (2001).
Quasi-random maximum simulated likelihood estimation of the mixed multinomial logit model.
Transportation Research Part B, 35:677-693.

7
Bollerslev, T., Engle, R., and Nelson, D. (1994).
ARCH models.
In Engle, R. and McFadden, D., editors, Handbook of Econometrics, Chap. 4, pages 2959-3038. North Holland Press, Amsterdam.

8
Brownstone, D. and Train, K. (1999).
Forecasting new product penetration with flexible substitution patterns.
Journal of Econometrics, 89:109-129.

9
Chib, S. (1995).
Marginal likelihood from the Gibbs output.
Journal of the American Statistical Association, 90:1313-1321.

10
Chib, S. and Greenberg, E. (1998).
Analysis of multivariate probit models.
Biometrika, 85:347-361.

11
Chib, S. and Hamilton, B. (2000).
Bayesian analysis of cross-section and clustered data treatment models.
Journal of Econometrics, 97:25-50.

12
Chib, S., Nardari, F., and Shephard, N. (2002).
Markov chain Monte Carlo methods for stochastic volatility models.
Journal of Econometrics, 108:291-316.

13
Chintagunta, P. and Honore, B. (1996).
Investigating the effects of marketing variables and unobserved heterogeneity in a multinomial probit model.
International Journal of Research in Marketing, 13:1-15.

14
Cowles, M. and Carlin, B. (1996).
Markov chain Monte Carlo convergence diagnostics: A comparative review.
Journal of the American Statistical Association, 91:883-904.

15
Cribari-Neto, F. (1997).
Econometric programming environnments: Gauss, Ox and S-plus.
Journal of Applied Econometrics, 12:77-89.

16
Danielson, J. (1994).
Stochastic volatility in asset prices: estimation with simulated maximum likelihood.
Journal of Econometrics, 61:375-400.

17
de Jong, P. and Shephard, N. (1995).
The simulation smoother for time series models.
Biometrika, 82:339-350.

18
Deb, P. and Trivedi, P. (1997).
Demand for medical care by the elderly: A finite mixture approach.
Journal of Applied Econometrics, 12:313-336.

19
Diebolt, J. and Robert, C. (1994).
Estimation of finite mixture distributions through Bayesian sampling.
Journal of the Royal Statistical Society, Series B, 56:363-375.

20
Durbin, J. and Koopman, S. (1997).
Monte Carlo maximum likelihood estimation for non-Gaussian state space models.
Biometrika, 84:669-684.

21
Durbin, J. and Koopman, S. (2000).
Time series analysis of non-Gaussian observations based on state space models from both classical and Bayesian perspectives.
Journal of the Royal Statistical Society B, 62:3-56.

22
Franses, P. and Paap, R. (2001).
Quantitative Models in Marketing Research.
Cambridge University Press, Cambridge.

23
Frühwirth-Schnatter, S. (2001).
Markov chain Monte Carlo estimation of classical and dynamic switching and mixture models.
Journal of the American Statistical Association, 96:194-209.

Frühwirth-Schnatter and Kaufmann (2002)
Frühwirth-Schnatter, S. and Kaufmann, S. (2002).
Bayesian clustering of many short time series.
Working Paper, Vienna University of Economics and Business Administration.

24
Galli, F. (2003).
Econometria dei Dati Finanzari ad Alta Frequenza.
Tesi di Dottorato in Economia Politica, Bologna.

25
Geweke, J., Keane, M., and Runkle, D. (1997).
Statistical inference in the multinomial multiperiod probit model.
Journal of Econometrics, 80:125-165.

26
Ghysels, E., Harvey, A., and Renault, E. (1996).
Stochastic volatility.
In Maddala, G. and Rao, C., editors, Handbook of Statistics, pages 119-191. Elsevier Science, Amsterdam.

27
Gourieroux, C. and Monfort, A. (1997).
Simulation-based Econometric Methods.
Oxford University Press, Oxford.

28
Hajivassiliou, V. and Mc Fadden, D. (1998).
The method of simulated scores for the estimation of LDV models.
Econometrica, 66:863-896.

29
Hajivassiliou, V. and Ruud, P. (1994).
Classical estimation methods for LDV models using simulation.
In: Handbook of Econometrics, Vol. 4, Chap. 40, North Holland, Amsterdam.

30
Hamilton, J. (1989).
A new approach to the economic analysis of nonstationary time series and the business cycle.
Econometrica, 57:357-384.

31
Hawkes, A. G. (1971).
Spectra of some self-exciting and mutually exciting point processes.
Biometrika, 58:83-90.

32
Horowitz, J. (1998).
Semiparametric Methods in Econometrics.
Springer Verlag, Berlin.

33
Jacquier, E., Polson, N., and Rossi, P. (1994).
Bayesian analysis of stochastic volatility models (with discussion).
Journal of Business and Economic Statistics, 12:371-417.

34
Jacquier, E., Polson, N., and Rossi, P. (2004).
Bayesian analysis of stochastic volatility models with fat-tails and correlated errors.
Forthcoming in Journal of Econometrics.

35
Kim, S., Shephard, N., and Chib, S. (1998).
Stochastic volatility: likelihood inference and comparison with ARCH models.
Review of Economic Studies, 65:361-393.

36
Liesenfeld, R. and Richard, J.-F. (2003).
Univariate and multivariate stochastic volatility models: estimation and diagnostics.
Journal of Empirical Finance, 10:505-531.

37
Maddala, G. (1983).
Limited-dependent and Qualitative Variables in Econometrics.
Cambridge University Press, Cambridge.

38
Mariano, R., Schuermann, T., and Weeks, M. (2000).
Simulation-based Inference in Econometrics.
Cambridge University Press, Cambridge.

39
McCulloch, R., Polson, N., and Rossi, P. (2000).
A Bayesian analysis of the multinomial probit model with fully identified parameters.
Journal of Econometrics, 99:173-193.

40
McFadden, D. and Train, K. (2000).
Mixed MNL models for discrete response.
Journal of Applied Econometrics, 15:447-470.

41
Paap, R. and Franses, P. (2000).
A dynamic multinomial probit model for brand choice with different long-run and short-run effects of marketing-mix variables.
Journal of Applied Econometrics, 15:717-744.

42
Pagan, A. and Ullah, A. (1999).
Nonparametric Econometrics.
Cambridge University Press, Cambridge.

43
Richard, J.-F. (1998).
Efficient high-dimensional Monte Carlo importance sampling.
Manuscript, University of Pittsburgh.

44
Richardson, S. and Green, P. (1997).
On Bayesian analysis of mixtures with an unknown number of components.
Journal of the Royal Statistical Society, Series B, 59:731-792.

45
Russell, J. (1999).
Econometric modeling of multivariate irregularly-spaced high-frequency data.
Manuscript, University of Chicago.

46
Sandman, G. and Koopman, S. (1998).
Estimation of stochastic volatility models via Monte Carlo maximum likelihood.
Journal of Econometrics, 67:271-301.

47
Shephard, N. (1996).
Statistical aspects of ARCH and stochastic volatility, Chap. 1, pages 1-67.
Time Series Models: In Econometrics, Finance and Other Fields. Chapman & Hall, in D. R. Cox, D. V. Hinkley, and O. E. Barndorff-Nielsen (eds.), London.

48
Strickland, C., Forbes, C., and Martin, G. (2003).
Bayesian analysis of the stochastic conditional duration model.
Manuscript, Monash University.

49
Tanner, M. and Wong, W. (1987).
The calculation of posterior distributions by data augmentation.
Journal of the American Statistical Association, 82:528-540.

50
Train, K. (2003).
Discrete Choice Methods with Simulation.
Cambridge University Press, Cambridge.



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