Next: 3. Statistical and Computational
Up: csahtml
Previous: 2.4 Finite Mixture Models
-
- 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.
Subsections