Maximum simulated likelihood methods and applications /:
The economics and statistics literature using computer simulation based methods has grown enormously over the past decades. Maximum Simulated Likelihood is a statistical tool useful for incorporating individual differences (called heterogeneity in the econometrics literature) and variations into a s...
Gespeichert in:
| Weitere Verfasser: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Bingley, U.K. :
Emerald,
2010.
|
| Schriftenreihe: | Advances in econometrics ;
26. |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | The economics and statistics literature using computer simulation based methods has grown enormously over the past decades. Maximum Simulated Likelihood is a statistical tool useful for incorporating individual differences (called heterogeneity in the econometrics literature) and variations into a statistical analysis. Problems that can be intractable with traditional methods are solved using computer simulation integrated with classical methods. Instead of assuming that everyone responds to stimuli in the same way, allowances are made for the possibility that different decision makers will respond in different ways. The techniques can be applied to problems of individual choice, such as the choice of a transportation model, or choice among health care options, as well as to the problem of making financial and macroeconomic predictions. Contributors to the volume discuss alternative simulation methods that permit faster and more accurate inference, as well as applications of established methods. |
| Beschreibung: | 1 online resource (xiv, 356 pages) : illustrations |
| Bibliographie: | Includes bibliographical references. |
| ISBN: | 9780857241504 0857241508 |
| ISSN: | 0731-9053 ; |
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| 505 | 0 | |a Introduction / William Greene -- MCMC perspectives on simulated likelihood estimation / Ivan Jeliazkov and Esther Hee Lee -- The panel probit model : adaptive integration on sparse grids / Florian Heiss -- A comparison of the maximum simulated likelihood and composite marginal likelihood estimation approaches in the context of the multivariate ordered response model / Chandra R. Bhat, Cristiano Varin, Nazneen Ferdous -- Pretest estimation in the random parameters logit model / Tong Zeng and R. Carter Hill -- Simulated maximum likelihood estimation of continuous time stochastic volatility models / Tore Selland Kleppe, Jun Yu, Hans J. Skaug -- Education savings accounts, parent contributions, and education attainment / Michael D.S. Morris -- Estimating the effect of exchange rate flexibility on financial account openness / Raul Razo-Garcia -- estimating a fractional response model with a count endogenous regressor and an application to female labor supply / Hoa B. Nguyen -- Alternative random effects panel gamma SML estimation with heterogeneity in random and one-sided error / Saleem Shaik and Ashok K. Mishra -- Modelling and forecasting volatility in a Bayesian approach / Esmail Amiri. | |
| 520 | |a The economics and statistics literature using computer simulation based methods has grown enormously over the past decades. Maximum Simulated Likelihood is a statistical tool useful for incorporating individual differences (called heterogeneity in the econometrics literature) and variations into a statistical analysis. Problems that can be intractable with traditional methods are solved using computer simulation integrated with classical methods. Instead of assuming that everyone responds to stimuli in the same way, allowances are made for the possibility that different decision makers will respond in different ways. The techniques can be applied to problems of individual choice, such as the choice of a transportation model, or choice among health care options, as well as to the problem of making financial and macroeconomic predictions. Contributors to the volume discuss alternative simulation methods that permit faster and more accurate inference, as well as applications of established methods. | ||
| 504 | |a Includes bibliographical references. | ||
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| contents | Introduction / William Greene -- MCMC perspectives on simulated likelihood estimation / Ivan Jeliazkov and Esther Hee Lee -- The panel probit model : adaptive integration on sparse grids / Florian Heiss -- A comparison of the maximum simulated likelihood and composite marginal likelihood estimation approaches in the context of the multivariate ordered response model / Chandra R. Bhat, Cristiano Varin, Nazneen Ferdous -- Pretest estimation in the random parameters logit model / Tong Zeng and R. Carter Hill -- Simulated maximum likelihood estimation of continuous time stochastic volatility models / Tore Selland Kleppe, Jun Yu, Hans J. Skaug -- Education savings accounts, parent contributions, and education attainment / Michael D.S. Morris -- Estimating the effect of exchange rate flexibility on financial account openness / Raul Razo-Garcia -- estimating a fractional response model with a count endogenous regressor and an application to female labor supply / Hoa B. Nguyen -- Alternative random effects panel gamma SML estimation with heterogeneity in random and one-sided error / Saleem Shaik and Ashok K. Mishra -- Modelling and forecasting volatility in a Bayesian approach / Esmail Amiri. |
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| id | ZDB-4-EBU-ocn699812720 |
| illustrated | Illustrated |
| indexdate | 2024-11-26T14:49:02Z |
| institution | BVB |
| isbn | 9780857241504 0857241508 |
| issn | 0731-9053 ; |
| language | English |
| oclc_num | 699812720 |
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| series | Advances in econometrics ; |
| series2 | Advances in econometrics, |
| spelling | Maximum simulated likelihood methods and applications / edited by William Greene, R. Carter Hill. Bingley, U.K. : Emerald, 2010. 1 online resource (xiv, 356 pages) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier polychrome. rdacc http://rdaregistry.info/termList/RDAColourContent/1003 text file rdaft http://rdaregistry.info/termList/fileType/1002 Advances in econometrics, 0731-9053 ; 26 Introduction / William Greene -- MCMC perspectives on simulated likelihood estimation / Ivan Jeliazkov and Esther Hee Lee -- The panel probit model : adaptive integration on sparse grids / Florian Heiss -- A comparison of the maximum simulated likelihood and composite marginal likelihood estimation approaches in the context of the multivariate ordered response model / Chandra R. Bhat, Cristiano Varin, Nazneen Ferdous -- Pretest estimation in the random parameters logit model / Tong Zeng and R. Carter Hill -- Simulated maximum likelihood estimation of continuous time stochastic volatility models / Tore Selland Kleppe, Jun Yu, Hans J. Skaug -- Education savings accounts, parent contributions, and education attainment / Michael D.S. Morris -- Estimating the effect of exchange rate flexibility on financial account openness / Raul Razo-Garcia -- estimating a fractional response model with a count endogenous regressor and an application to female labor supply / Hoa B. Nguyen -- Alternative random effects panel gamma SML estimation with heterogeneity in random and one-sided error / Saleem Shaik and Ashok K. Mishra -- Modelling and forecasting volatility in a Bayesian approach / Esmail Amiri. The economics and statistics literature using computer simulation based methods has grown enormously over the past decades. Maximum Simulated Likelihood is a statistical tool useful for incorporating individual differences (called heterogeneity in the econometrics literature) and variations into a statistical analysis. Problems that can be intractable with traditional methods are solved using computer simulation integrated with classical methods. Instead of assuming that everyone responds to stimuli in the same way, allowances are made for the possibility that different decision makers will respond in different ways. The techniques can be applied to problems of individual choice, such as the choice of a transportation model, or choice among health care options, as well as to the problem of making financial and macroeconomic predictions. Contributors to the volume discuss alternative simulation methods that permit faster and more accurate inference, as well as applications of established methods. Includes bibliographical references. Econometrics. http://id.loc.gov/authorities/subjects/sh85040763 Économétrie. Economics. bicssc Econometrics. bicssc Business & Economics Econometrics. bisacsh Gestion d'entreprises. eclas Econometrics fast Greene, William. Hill, R. Carter. http://id.loc.gov/authorities/names/n83189179 Print version: Maximum simulated likelihood methods and applications. Bingley : Emerald, ©2010 9780857241498 (OCoLC)655662134 Advances in econometrics ; 26. http://id.loc.gov/authorities/names/no98010995 FWS01 ZDB-4-EBU FWS_PDA_EBU https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=351734 Volltext |
| spellingShingle | Maximum simulated likelihood methods and applications / Advances in econometrics ; Introduction / William Greene -- MCMC perspectives on simulated likelihood estimation / Ivan Jeliazkov and Esther Hee Lee -- The panel probit model : adaptive integration on sparse grids / Florian Heiss -- A comparison of the maximum simulated likelihood and composite marginal likelihood estimation approaches in the context of the multivariate ordered response model / Chandra R. Bhat, Cristiano Varin, Nazneen Ferdous -- Pretest estimation in the random parameters logit model / Tong Zeng and R. Carter Hill -- Simulated maximum likelihood estimation of continuous time stochastic volatility models / Tore Selland Kleppe, Jun Yu, Hans J. Skaug -- Education savings accounts, parent contributions, and education attainment / Michael D.S. Morris -- Estimating the effect of exchange rate flexibility on financial account openness / Raul Razo-Garcia -- estimating a fractional response model with a count endogenous regressor and an application to female labor supply / Hoa B. Nguyen -- Alternative random effects panel gamma SML estimation with heterogeneity in random and one-sided error / Saleem Shaik and Ashok K. Mishra -- Modelling and forecasting volatility in a Bayesian approach / Esmail Amiri. Econometrics. http://id.loc.gov/authorities/subjects/sh85040763 Économétrie. Economics. bicssc Econometrics. bicssc Business & Economics Econometrics. bisacsh Gestion d'entreprises. eclas Econometrics fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85040763 |
| title | Maximum simulated likelihood methods and applications / |
| title_auth | Maximum simulated likelihood methods and applications / |
| title_exact_search | Maximum simulated likelihood methods and applications / |
| title_full | Maximum simulated likelihood methods and applications / edited by William Greene, R. Carter Hill. |
| title_fullStr | Maximum simulated likelihood methods and applications / edited by William Greene, R. Carter Hill. |
| title_full_unstemmed | Maximum simulated likelihood methods and applications / edited by William Greene, R. Carter Hill. |
| title_short | Maximum simulated likelihood methods and applications / |
| title_sort | maximum simulated likelihood methods and applications |
| topic | Econometrics. http://id.loc.gov/authorities/subjects/sh85040763 Économétrie. Economics. bicssc Econometrics. bicssc Business & Economics Econometrics. bisacsh Gestion d'entreprises. eclas Econometrics fast |
| topic_facet | Econometrics. Économétrie. Economics. Business & Economics Econometrics. Gestion d'entreprises. Econometrics |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=351734 |
| work_keys_str_mv | AT greenewilliam maximumsimulatedlikelihoodmethodsandapplications AT hillrcarter maximumsimulatedlikelihoodmethodsandapplications |