21201408 - STATISTICAL METHODS FOR ECONOMETRICS

THE COURSE AIMS TO INTRODUCE THE MAIN TECHNIQUES OF ECONOMETRICS, THE USE OF WHICH HAS BECOME COMMON PRACTICE IN EMPIRICAL WORK IN MANY AREAS OF ECONOMIC, FINANCIAL AND BUSINESS ANALYSIS. THE FOCUS IS ON THE INTUITION BEHIND THE DIFFERENT APPROACHES AND THEIR PRACTICAL RELEVANCE. THE COURSE INTRODUCES AND DISCUSSES EMPIRICAL EXAMPLES AND APPLICATIONS FROM AREAS OF ANALYSIS SUCH AS LABOUR ECONOMICS, FINANCE, INTERNATIONAL ECONOMICS, ENVIRONMENTAL ECONOMICS, MACROECONOMICS AND MANAGEMENT. THE USE OF THE DIFFERENT PROCEDURES IS ILLUSTRATED BY PRACTICAL EXAMPLES BASED ON THE USE OF DATA TAKEN FROM REAL CASES, WITH THE USE OF A SUITABLE SOFTWARE (E-VIEWS, R).

NACCARATO ALESSIA

teacher profile | teaching materials

Mutuazione: 21201408 METODI STATISTICI PER L'ECONOMETRIA in Scienze Economiche LM-56 N0 NACCARATO ALESSIA

Programme

Some hints of statistical inference and linear algebra
The multiple linear regression model. Interpretation and comparison of regression models. Least squares and maximum likelihood estimators. Heteroschedasticity and autocorrelation, multicollinearity, non-deterministic exogenous variables and instrumental variables method, incorrect model specification, stability of the regression function and use of dichotomous variables.
Panel data models: fixed effect models and random effect models. Within and between estimators. Heteroschedasticity and autocorrelation tests. Dynamic models for panel data: Arellano-Bond estimator.
Models with lagged variables: dynamic regression models, distributed lag models.
Introduction to time series analysis: stochastic processes, stationarity and autocovariance function, moving average and integrated autoregressive processes (AR, MA, ARMA, ARIMA), the Box and Jenkins procedure.

Core Documentation

Students can refer to the following texts (the first three can be considered alternative to each other)
1) Introduzione all'Econometria, J. H. Stock, M. W. Watson, Ed. Pearson
2) Econometrica, J. Johnston, Ed. Franco Angeli
3) Econometria, M. Verbeek, Ed. Zanichelli
4) Lectures on advanced econometrics, L. Pieraccini, Ed. Aracne
5) Introduction to Time Series Analysis and Forecasting, D. C. Montgomery, C. L. Jennings, M. Kulahci, Ed. Wiley

Some of the topics discussed in the course can also be found in
Introductory Econometrics for Finance, C. Brooks, Cambridge University Press

For background references to statistical inference and linear algebra students can refer:
Fondamenti di Inferenza Statistica, L. Pieraccini, Ed. Giappichelli
Matrix Differential Calculus in Statistics and Econometrics, J. R. Magnus, H. Neudecker, Ed. Wiley Series in Probability and Statistics

For applications with R, students can refer to one of the following texts
Introductory Statistics with R, P. Dalgaard, Ed. Springer
An Introduction to Applied Multivariate Analysis with R, B. Everitt, T. Hothorn, Ed. Springer


NACCARATO ALESSIA

teacher profile | teaching materials

Mutuazione: 21201408 METODI STATISTICI PER L'ECONOMETRIA in Scienze Economiche LM-56 N0 NACCARATO ALESSIA

Programme

Some hints of statistical inference and linear algebra
The multiple linear regression model. Interpretation and comparison of regression models. Least squares and maximum likelihood estimators. Heteroschedasticity and autocorrelation, multicollinearity, non-deterministic exogenous variables and instrumental variables method, incorrect model specification, stability of the regression function and use of dichotomous variables.
Panel data models: fixed effect models and random effect models. Within and between estimators. Heteroschedasticity and autocorrelation tests. Dynamic models for panel data: Arellano-Bond estimator.
Models with lagged variables: dynamic regression models, distributed lag models.
Introduction to time series analysis: stochastic processes, stationarity and autocovariance function, moving average and integrated autoregressive processes (AR, MA, ARMA, ARIMA), the Box and Jenkins procedure.

Core Documentation

Students can refer to the following texts (the first three can be considered alternative to each other)
1) Introduzione all'Econometria, J. H. Stock, M. W. Watson, Ed. Pearson
2) Econometrica, J. Johnston, Ed. Franco Angeli
3) Econometria, M. Verbeek, Ed. Zanichelli
4) Lectures on advanced econometrics, L. Pieraccini, Ed. Aracne
5) Introduction to Time Series Analysis and Forecasting, D. C. Montgomery, C. L. Jennings, M. Kulahci, Ed. Wiley

Some of the topics discussed in the course can also be found in
Introductory Econometrics for Finance, C. Brooks, Cambridge University Press

For background references to statistical inference and linear algebra students can refer:
Fondamenti di Inferenza Statistica, L. Pieraccini, Ed. Giappichelli
Matrix Differential Calculus in Statistics and Econometrics, J. R. Magnus, H. Neudecker, Ed. Wiley Series in Probability and Statistics

For applications with R, students can refer to one of the following texts
Introductory Statistics with R, P. Dalgaard, Ed. Springer
An Introduction to Applied Multivariate Analysis with R, B. Everitt, T. Hothorn, Ed. Springer


NACCARATO ALESSIA

teacher profile | teaching materials

Mutuazione: 21201408 METODI STATISTICI PER L'ECONOMETRIA in Scienze Economiche LM-56 N0 NACCARATO ALESSIA

Programme

Some hints of statistical inference and linear algebra
The multiple linear regression model. Interpretation and comparison of regression models. Least squares and maximum likelihood estimators. Heteroschedasticity and autocorrelation, multicollinearity, non-deterministic exogenous variables and instrumental variables method, incorrect model specification, stability of the regression function and use of dichotomous variables.
Panel data models: fixed effect models and random effect models. Within and between estimators. Heteroschedasticity and autocorrelation tests. Dynamic models for panel data: Arellano-Bond estimator.
Models with lagged variables: dynamic regression models, distributed lag models.
Introduction to time series analysis: stochastic processes, stationarity and autocovariance function, moving average and integrated autoregressive processes (AR, MA, ARMA, ARIMA), the Box and Jenkins procedure.

Core Documentation

Students can refer to the following texts (the first three can be considered alternative to each other)
1) Introduzione all'Econometria, J. H. Stock, M. W. Watson, Ed. Pearson
2) Econometrica, J. Johnston, Ed. Franco Angeli
3) Econometria, M. Verbeek, Ed. Zanichelli
4) Lectures on advanced econometrics, L. Pieraccini, Ed. Aracne
5) Introduction to Time Series Analysis and Forecasting, D. C. Montgomery, C. L. Jennings, M. Kulahci, Ed. Wiley

Some of the topics discussed in the course can also be found in
Introductory Econometrics for Finance, C. Brooks, Cambridge University Press

For background references to statistical inference and linear algebra students can refer:
Fondamenti di Inferenza Statistica, L. Pieraccini, Ed. Giappichelli
Matrix Differential Calculus in Statistics and Econometrics, J. R. Magnus, H. Neudecker, Ed. Wiley Series in Probability and Statistics

For applications with R, students can refer to one of the following texts
Introductory Statistics with R, P. Dalgaard, Ed. Springer
An Introduction to Applied Multivariate Analysis with R, B. Everitt, T. Hothorn, Ed. Springer


NACCARATO ALESSIA

teacher profile | teaching materials

Mutuazione: 21201408 METODI STATISTICI PER L'ECONOMETRIA in Scienze Economiche LM-56 N0 NACCARATO ALESSIA

Programme

Some hints of statistical inference and linear algebra
The multiple linear regression model. Interpretation and comparison of regression models. Least squares and maximum likelihood estimators. Heteroschedasticity and autocorrelation, multicollinearity, non-deterministic exogenous variables and instrumental variables method, incorrect model specification, stability of the regression function and use of dichotomous variables.
Panel data models: fixed effect models and random effect models. Within and between estimators. Heteroschedasticity and autocorrelation tests. Dynamic models for panel data: Arellano-Bond estimator.
Models with lagged variables: dynamic regression models, distributed lag models.
Introduction to time series analysis: stochastic processes, stationarity and autocovariance function, moving average and integrated autoregressive processes (AR, MA, ARMA, ARIMA), the Box and Jenkins procedure.

Core Documentation

Students can refer to the following texts (the first three can be considered alternative to each other)
1) Introduzione all'Econometria, J. H. Stock, M. W. Watson, Ed. Pearson
2) Econometrica, J. Johnston, Ed. Franco Angeli
3) Econometria, M. Verbeek, Ed. Zanichelli
4) Lectures on advanced econometrics, L. Pieraccini, Ed. Aracne
5) Introduction to Time Series Analysis and Forecasting, D. C. Montgomery, C. L. Jennings, M. Kulahci, Ed. Wiley

Some of the topics discussed in the course can also be found in
Introductory Econometrics for Finance, C. Brooks, Cambridge University Press

For background references to statistical inference and linear algebra students can refer:
Fondamenti di Inferenza Statistica, L. Pieraccini, Ed. Giappichelli
Matrix Differential Calculus in Statistics and Econometrics, J. R. Magnus, H. Neudecker, Ed. Wiley Series in Probability and Statistics

For applications with R, students can refer to one of the following texts
Introductory Statistics with R, P. Dalgaard, Ed. Springer
An Introduction to Applied Multivariate Analysis with R, B. Everitt, T. Hothorn, Ed. Springer


NACCARATO ALESSIA

teacher profile | teaching materials

Mutuazione: 21201408 METODI STATISTICI PER L'ECONOMETRIA in Scienze Economiche LM-56 N0 NACCARATO ALESSIA

Programme

Some hints of statistical inference and linear algebra
The multiple linear regression model. Interpretation and comparison of regression models. Least squares and maximum likelihood estimators. Heteroschedasticity and autocorrelation, multicollinearity, non-deterministic exogenous variables and instrumental variables method, incorrect model specification, stability of the regression function and use of dichotomous variables.
Panel data models: fixed effect models and random effect models. Within and between estimators. Heteroschedasticity and autocorrelation tests. Dynamic models for panel data: Arellano-Bond estimator.
Models with lagged variables: dynamic regression models, distributed lag models.
Introduction to time series analysis: stochastic processes, stationarity and autocovariance function, moving average and integrated autoregressive processes (AR, MA, ARMA, ARIMA), the Box and Jenkins procedure.

Core Documentation

Students can refer to the following texts (the first three can be considered alternative to each other)
1) Introduzione all'Econometria, J. H. Stock, M. W. Watson, Ed. Pearson
2) Econometrica, J. Johnston, Ed. Franco Angeli
3) Econometria, M. Verbeek, Ed. Zanichelli
4) Lectures on advanced econometrics, L. Pieraccini, Ed. Aracne
5) Introduction to Time Series Analysis and Forecasting, D. C. Montgomery, C. L. Jennings, M. Kulahci, Ed. Wiley

Some of the topics discussed in the course can also be found in
Introductory Econometrics for Finance, C. Brooks, Cambridge University Press

For background references to statistical inference and linear algebra students can refer:
Fondamenti di Inferenza Statistica, L. Pieraccini, Ed. Giappichelli
Matrix Differential Calculus in Statistics and Econometrics, J. R. Magnus, H. Neudecker, Ed. Wiley Series in Probability and Statistics

For applications with R, students can refer to one of the following texts
Introductory Statistics with R, P. Dalgaard, Ed. Springer
An Introduction to Applied Multivariate Analysis with R, B. Everitt, T. Hothorn, Ed. Springer