Bayesian Econometrics

An approach to statistical inference based on the Bayes principle. Prior and posterior distributions. Bayesian estimation. Bayesian confidence intervals and tests. Time series models. Predictive distributions. Model choice.

The course is aimed at introducing students to fundametals of Bayesian statistical analysis, to basic econometric methods and techniques derived from Bayesian statistical inference and to selected applications of the methods.

The course is an introduction to basic Bayesian econometric methods. After fundametals of Bayesian statistical inference (prior distribution choice, Bayesian estimation, confidence intervals and tests), Bayesian analysis of regression and autoregression models, time series analysis and Bayesian methods of model choice will be presented. Standard applications will be given.

Literatura podstawowa:

G. Koop "Bayesian Econometrics", J. Wiley&Sons, Chichester 2003; T.Lancaster, An Introduction to Bayesian Inference in Econometrics, Blackwell Publ., 2004; A.Zellner, An Introduction to Bayesian Inference in Econometrics, Wiley, New York 1971

Literatura uzupełniająca:

G.E.P.Box, G.C.Tiao, Bayesian Inference in Statistical Analysis, Addison-Wesley, Reading 1973; W. Kurt, Bayesian Statistics - The Fun Way, No Starch Press, S. Francisco 2019; Ch.Robert, Bayesian Choice, Springer, New York 2001; Ch.Robert, G.Casella, Statistical Monte Carlo Methods, Springer, New York 1999.

Evaluation criteria

Traditional Written Exam (Zadania lub testy do rozwiązania): 50.00%

Projects: 50.00%

The threshold percentage of absences (excluding lectures)

defined as the proportion of class hours beyond which the achievement of learning outcomes is deemed unattainable: 50%

An approach to statistical inference based on the Bayes principle. Prior and posterior distributions. Bayesian estimation. Bayesian confidence intervals and tests. Time series models. Predictive distributions. Model choice.

The course is aimed at introducing students to fundametals of Bayesian statistical analysis, to basic econometric methods and techniques derived from Bayesian statistical inference and to selected applications of the methods.

The course is an introduction to basic Bayesian econometric methods. After fundametals of Bayesian statistical inference (prior distribution choice, Bayesian estimation, confidence intervals and tests), Bayesian analysis of regression and autoregression models, time series analysis and Bayesian methods of model choice will be presented. Standard applications will be given.

Literatura podstawowa:

G. Koop "Bayesian Econometrics", J. Wiley&Sons, Chichester 2003; T.Lancaster, An Introduction to Bayesian Inference in Econometrics, Blackwell Publ., 2004; A.Zellner, An Introduction to Bayesian Inference in Econometrics, Wiley, New York 1971

Literatura uzupełniająca:

G.E.P.Box, G.C.Tiao, Bayesian Inference in Statistical Analysis, Addison-Wesley, Reading 1973; W. Kurt, Bayesian Statistics - The Fun Way, No Starch Press, S. Francisco 2019; Ch.Robert, Bayesian Choice, Springer, New York 2001; Ch.Robert, G.Casella, Statistical Monte Carlo Methods, Springer, New York 1999.

Evaluation criteria

Traditional Written Exam (Zadania lub testy do rozwiązania): 50.00%

Projects: 50.00%

The threshold percentage of absences (excluding lectures)

defined as the proportion of class hours beyond which the achievement of learning outcomes is deemed unattainable: 50%

An approach to statistical inference based on the Bayes principle. Prior and posterior distributions. Bayesian estimation. Bayesian confidence intervals and tests. Time series models. Predictive distributions. Model choice.

The course is aimed at introducing students to fundametals of Bayesian statistical analysis, to basic econometric methods and techniques derived from Bayesian statistical inference and to selected applications of the methods.

The course is an introduction to basic Bayesian econometric methods. After fundametals of Bayesian statistical inference (prior distribution choice, Bayesian estimation, confidence intervals and tests), Bayesian analysis of regression and autoregression models, time series analysis and Bayesian methods of model choice will be presented. Standard applications will be given.

Literatura podstawowa:

G. Koop "Bayesian Econometrics", J. Wiley&Sons, Chichester 2003; T.Lancaster, An Introduction to Bayesian Inference in Econometrics, Blackwell Publ., 2004; A.Zellner, An Introduction to Bayesian Inference in Econometrics, Wiley, New York 1971

Literatura uzupełniająca:

G.E.P.Box, G.C.Tiao, Bayesian Inference in Statistical Analysis, Addison-Wesley, Reading 1973; W. Kurt, Bayesian Statistics - The Fun Way, No Starch Press, S. Francisco 2019; Ch.Robert, Bayesian Choice, Springer, New York 2001; Ch.Robert, G.Casella, Statistical Monte Carlo Methods, Springer, New York 1999.

An approach to statistical inference based on the Bayes principle. Prior and posterior distributions. Bayesian estimation. Bayesian confidence intervals and tests. Time series models. Predictive distributions. Model choice.

The course is aimed at introducing students to fundametals of Bayesian statistical analysis, to basic econometric methods and techniques derived from Bayesian statistical inference and to selected applications of the methods.

The course is an introduction to basic Bayesian econometric methods. After fundametals of Bayesian statistical inference (prior distribution choice, Bayesian estimation, confidence intervals and tests), Bayesian analysis of regression and autoregression models, time series analysis and Bayesian methods of model choice will be presented. Standard applications will be given.

Literatura podstawowa:

G. Koop "Bayesian Econometrics", J. Wiley&Sons, Chichester 2003; T.Lancaster, An Introduction to Bayesian Inference in Econometrics, Blackwell Publ., 2004; A.Zellner, An Introduction to Bayesian Inference in Econometrics, Wiley, New York 1971

Literatura uzupełniająca:

G.E.P.Box, G.C.Tiao, Bayesian Inference in Statistical Analysis, Addison-Wesley, Reading 1973; W. Kurt, Bayesian Statistics - The Fun Way, No Starch Press, S. Francisco 2019; Ch.Robert, Bayesian Choice, Springer, New York 2001; Ch.Robert, G.Casella, Statistical Monte Carlo Methods, Springer, New York 1999.