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Econometrics

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Handbook of Computational Statistics

Part of the book series: Springer Handbooks of Computational Statistics ((SHCS))

Abstract

Since the last decade we live in a digitalized world where many actions in human and economic life are monitored. This produces a continuous stream of new, rich and high quality data in the form of panels, repeated cross-sections and long time series. These data resources are available to many researchers at a low cost. This new era is fascinating for econometricians who can address many open economic questions. To do so, new models are developed that call for elaborate estimation techniques. Fast personal computers play an integral part in making it possible to deal with this increased complexity.

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References

  • Allenby, G., Rossi, P.: Marketing models of consumer heterogeneity. J. Econometrics 89, 57–78 (1999)

    Article  MATH  Google Scholar 

  • Bauwens, L., Galli, F.: Efficient importance sampling for ML estimation of SCD models. Comput. Stat. Data Anal. 53, 1974–1992 (2009)

    Article  MathSciNet  MATH  Google Scholar 

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

    Google Scholar 

  • Bauwens, L., Hautsch, N.: Dynamic latent factor models for intensity processes. J. Financ. Econometrics 4, 450–493 (2006)

    Article  Google Scholar 

  • Bauwens, L., Rombouts, J.: Bayesian clustering of many GARCH models. Econometrics Rev. 26, 365–386 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  • Bauwens, L., Rombouts, J.: On marginal likelihood computation in change-point models, Forthcoming in Computational Statistics & Data Analysis. CORE DP 2009/61 (2009)

    Google Scholar 

  • Bauwens, L., Veredas, D.: The stochastic conditional duration model: a latent factor model for the analysis of financial durations. J. Econometrics 119, 381–412 (2004)

    Article  MathSciNet  Google Scholar 

  • Bhat, C.: Quasi-random maximum simulated likelihood estimation of the mixed multinomial logit model. Transp. Res. Part B 35, 677–693 (2001)

    Article  Google Scholar 

  • Bollerslev, T., Engle, R., Nelson, D.: ARCH models. In: Engle, R., McFadden, D. (eds.) Handbook of Econometrics, chapter 4, pp. 2959–3038. North Holland Press, Amsterdam (1994)

    Chapter  Google Scholar 

  • Brownstone, D., Train, K.: Forecasting new product penetration with flexible substitution patterns. J. Econometrics 89, 109–129 (1999)

    Article  MATH  Google Scholar 

  • Chib, S.: Marginal likelihood from the Gibbs output. J. Am. Stat. Assoc. 90, 1313–1321 (1995)

    Article  MathSciNet  MATH  Google Scholar 

  • Chib, S.: Estimation and comparison of multiple change-point models. J. Econometrics 86, 221–241 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  • Chib, S., Greenberg, E.: Analysis of multivariate probit models. Biometrika 85, 347–361 (1998)

    MATH  Google Scholar 

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

    Article  MATH  Google Scholar 

  • Chib, S., Nardari, F., Shephard, N.: Markov chain Monte Carlo methods for stochastic volatility models. J. Econometrics 108, 291–316 (2002)

    Article  MathSciNet  Google Scholar 

  • Chintagunta, P., Honore, B.: Investigating the effects of marketing variables and unobserved heterogeneity in a multinomial probit model. Int. J. Res. Market. 13, 1–15 (1996)

    Article  Google Scholar 

  • Cowles, M., Carlin, B.: Markov chain Monte Carlo convergence diagnostics: A comparative review. J. Am. Stat. Assoc. 91, 883–904 (1996)

    Article  MathSciNet  MATH  Google Scholar 

  • Cribari-Neto, F.: Econometric programming environnments: Gauss, Ox and S-plus. J. Appl. Econometrics 12:77–89 (1997)

    Article  Google Scholar 

  • Danielson, J.: Stochastic volatility in asset prices: estimation with simulated maximum likelihood. J. Econometrics 61, 375–400 (1994)

    Article  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  • Deb, P., Trivedi, P.: Demand for medical care by the elderly: A finite mixture approach. J. Appl. Econometrics 12, 313–336 (1997)

    Article  Google Scholar 

  • Diebolt, J., Robert, C.: Estimation of finite mixture distributions through Bayesian sampling. J. Roy. Stat. Soc. B 56, 363–375 (1994)

    MathSciNet  MATH  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  • Durbin, J., Koopman, S.: Time series analysis of non-Gaussian observations based on state space models from both classical and Bayesian perspectives. J. Roy. Stat. Soc. B 62, 3–56 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  • Fan, J., Yao, Q.: Nonlinear Time Series, (2nd edn.), Springer, Berlin (2006)

    Google Scholar 

  • Feng, D., Jiang, G.J., Song, P.X.-K.: Stochastic conditional duration models with leverage effect for financial transaction data. J. Financ. Econometrics 2, 390–421 (2004)

    Article  Google Scholar 

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

    Book  Google Scholar 

  • Frühwirth-Schnatter, S.: Markov chain Monte Carlo estimation of classical and dynamic switching and mixture models. J. Am. Stat. Assoc. 96, 194–209 (2001)

    Article  MATH  Google Scholar 

  • Frühwirth-Schnatter, S., Kaufmann, S.: Model-based clustering of multiple time series. J. Bus. Econ. Stat. 26, 78–89 (2008)

    Article  Google Scholar 

  • Geweke, J., Keane, M., Runkle, D.: Statistical inference in the multinomial multiperiod probit model. J. Econometrics 80, 125–165 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  • Ghysels, E., Harvey, A., Renault, E.: Stochastic volatility. In: Maddala, G., Rao, C. (eds.) Handbook of Statistics, pp. 119–191. Elsevier Science, Amsterdam (1996)

    Google Scholar 

  • Giordani, P., Kohn, R.: Efficient bayesian inference for multiple change-point and mixture innovation models. J. Bus. Econ. Stat. 26, 66–77 (2008)

    Article  MathSciNet  Google Scholar 

  • Gouriéroux, C., Monfort, A.: Simulation-based Econometric Methods. Oxford University Press, Oxford (1997)

    Book  Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  • Horowitz, J.: Semiparametric Methods in Econometrics. Springer, Berlin (1998)

    MATH  Google Scholar 

  • Jacquier, E., Polson, N., Rossi, P.: Bayesian analysis of stochastic volatility models (with discussion). J. Bus. Econ. Stat. 12, 371–417 (1994)

    Google Scholar 

  • Jacquier, E., Polson, N., Rossi, P.: Bayesian analysis of stochastic volatility models with fat-tails and correlated errors. J. Econometrics 122, 185–212 (2004)

    Article  MathSciNet  Google Scholar 

  • Kim, C.-J., Nelson, C.R.: Has the U.S. economy become more stable? a Bayesian approach based on a Markov-switching model of the business cycle. Rev. Econ. Stat. 81, 608–616 (1999)

    Google Scholar 

  • Kim, S., Shephard, N., Chib, S.: Stochastic volatility: likelihood inference and comparison with ARCH models. Rev. Econ. Stud. 65, 361–393 (1998)

    Article  MATH  Google Scholar 

  • Koop, G., Potter, S.: Estimation and forecasting with multiple breaks. Rev. Econ. Stud. 74, 763–789 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  • Li, Q., Racine, J.S.: Nonparametric Econometrics: Theory and Practice. Princeton University Press, USA (2006)

    Google Scholar 

  • Liesenfeld, R., Richard, J.-F.: Univariate and multivariate stochastic volatility models: estimation and diagnostics. J. Empir. Finance 10, 505–531 (2003)

    Article  Google Scholar 

  • Liu, C., Maheu, J.: Are there structural breaks in realized volatility? J. Financ. Econometrics 6, 326–360 (2008)

    Article  Google Scholar 

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

    MATH  Google Scholar 

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

    Book  MATH  Google Scholar 

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

    Article  MATH  Google Scholar 

  • McFadden, D., Train, K.: Mixed MNL models for discrete response. J. Appl. Econometrics 15, 447–470 (2000)

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Pagan, A., Ullah, A.: Nonparametric Econometrics. Cambridge University Press, Cambridge (1999)

    Google Scholar 

  • Pastor, L., Stambaugh, R.F.: The equity premium and structural breaks. J. Finan. 56, 1207–1239 (2001)

    Article  Google Scholar 

  • Pesaran, M.H., Pettenuzzo, D., Timmermann, A.: Forecasting time series subject to multiple structural breaks. Rev. Econ. Stud. 73, 1057–1084 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  • Richard, J.-F., Zhang, W.: Efficient high-dimensional Monte Carlo importance sampling. J. Econometrics 141, 1385–1411 (2007)

    Article  MathSciNet  Google Scholar 

  • Richardson, S., Green, P.: On Bayesian analysis of mixtures with an unknown number of components. J. Roy. Stat. Soc. B 59, 731–792 (1997)

    Article  MathSciNet  MATH  Google Scholar 

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

    Google Scholar 

  • Sandman, G., Koopman, S.: Estimation of stochastic volatility models via Monte Carlo maximum likelihood. J. Econometrics 67, 271–301 (1998)

    Article  Google Scholar 

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

    Google Scholar 

  • Strickland, C., Forbes, C., Martin, G.: Bayesian analysis of the stochastic conditional duration model. Comput. Stat. Data Anal. 50, 2247–2267 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  • Tanner, M., Wong, W.: The calculation of posterior distributions by data augmentation. J. Am. Stat. Assoc. 82, 528–540 (1987)

    Article  MathSciNet  MATH  Google Scholar 

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

    Book  MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. Université Catholique de Louvain, Louvain-la-Neuve, Belgium

    Luc Bauwens

  2. HEC Montréal, CIRANO, CIRPEE, CORE, Montreal, Canada

    Jeroen V. K. Rombouts

Authors
  1. Luc Bauwens
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  2. Jeroen V. K. Rombouts
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Corresponding author

Correspondence to Luc Bauwens .

Editor information

Editors and Affiliations

  1. Dept. Computational & Data, Sciences, George Mason University, University Drive 4400, Fairfax, 22030-4444, Virginia, USA

    James E. Gentle

  2. L.v.Bortkiewicz Chair of Statistics, C.A.S.E. Centre f. Appl. Stat. & Econ., Humboldt-Universität zu Berlin, Unter den Linden 6, Berlin, 10099, Germany

    Wolfgang Karl Härdle

  3. Dept. Socioinformation, Okayama University, Ridai-cho 1-1, Okayama, 700-0005, Japan

    Yuichi Mori

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Bauwens, L., Rombouts, J.V.K. (2012). Econometrics. In: Gentle, J., Härdle, W., Mori, Y. (eds) Handbook of Computational Statistics. Springer Handbooks of Computational Statistics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21551-3_35

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