Programme

Review of point estimation, interval estimation, hypothesis testing, and simple regression.
The nature of econometric and economic data; Further analysis of the simple regression model; Introduction to the multiple regression model.
Inference in the multiple regression model; The normality assumption; Hypothesis testing for a single parameter; Calculation and interpretation of the p-value; Confidence intervals.
Asymptotic least squares and further analysis of the multiple regression model; Interaction models; Goodness of fit and adjusted (R^2).
Multiple regression models with qualitative information; Dummy variables; Restricted and unrestricted models; Introduction to heteroskedasticity.
Basic panel data; Difference-in-differences; Two-period panel data; Panel data estimators; Control groups.
Advanced panel data methods; Fixed effects; Fixed effects estimator; Random effects; Random effects estimators.
Time series data; stationarity.
Regression with time series data: distributed lag models.
Autocorrelation, correlogram and autoregressive models.
Regression with serially correlated errors.

Prerequisiti

Basic notions of probability and statistical inference

Core Documentation

J. Wooldridge, Introductory Econometrics: A Modern Approach, Cengage International Edition, 8th Edition (2025). Chapters: 1-14.

Attendance

Highly recommended

Type of evaluation

The examination consists of a written test with a number of multiple-choice questions and some open questions (one of which will require the interpretation of R output), followed by an oral discussion of the student’s written test.

Programme

Review of point estimation, interval estimation, hypothesis testing, and simple regression.
The nature of econometric and economic data; Further analysis of the simple regression model; Introduction to the multiple regression model.
Inference in the multiple regression model; The normality assumption; Hypothesis testing for a single parameter; Calculation and interpretation of the p-value; Confidence intervals.
Asymptotic least squares and further analysis of the multiple regression model; Interaction models; Goodness of fit and adjusted (R^2).
Multiple regression models with qualitative information; Dummy variables; Restricted and unrestricted models; Introduction to heteroskedasticity.
Basic panel data; Difference-in-differences; Two-period panel data; Panel data estimators; Control groups.
Advanced panel data methods; Fixed effects; Fixed effects estimator; Random effects; Random effects estimators.
Time series data; stationarity.
Regression with time series data: distributed lag models.
Autocorrelation, correlogram and autoregressive models.
Regression with serially correlated errors.

Prerequisiti

Basic notions of probability and statistical inference

Core Documentation

J. Wooldridge, Introductory Econometrics: A Modern Approach, Cengage International Edition, 8th Edition (2025). Chapters: 1-14.

Attendance

Highly recommended

Type of evaluation

The examination consists of a written test with a number of multiple-choice questions and some open questions (one of which will require the interpretation of R output), followed by an oral discussion of the student’s written test.

Programme

Review of point estimation, interval estimation, hypothesis testing, and simple regression.
The nature of econometric and economic data; Further analysis of the simple regression model; Introduction to the multiple regression model.
Inference in the multiple regression model; The normality assumption; Hypothesis testing for a single parameter; Calculation and interpretation of the p-value; Confidence intervals.
Asymptotic least squares and further analysis of the multiple regression model; Interaction models; Goodness of fit and adjusted (R^2).
Multiple regression models with qualitative information; Dummy variables; Restricted and unrestricted models; Introduction to heteroskedasticity.
Basic panel data; Difference-in-differences; Two-period panel data; Panel data estimators; Control groups.
Advanced panel data methods; Fixed effects; Fixed effects estimator; Random effects; Random effects estimators.
Time series data; stationarity.
Regression with time series data: distributed lag models.
Autocorrelation, correlogram and autoregressive models.
Regression with serially correlated errors.

Prerequisiti

Basic notions of probability and statistical inference

Core Documentation

J. Wooldridge, Introductory Econometrics: A Modern Approach, Cengage International Edition, 8th Edition (2025). Chapters: 1-14.

Attendance

Highly recommended

Type of evaluation

The examination consists of a written test with a number of multiple-choice questions and some open questions (one of which will require the interpretation of R output), followed by an oral discussion of the student’s written test.

Programme

Review of point estimation, interval estimation, hypothesis testing, and simple regression.
The nature of econometric and economic data; Further analysis of the simple regression model; Introduction to the multiple regression model.
Inference in the multiple regression model; The normality assumption; Hypothesis testing for a single parameter; Calculation and interpretation of the p-value; Confidence intervals.
Asymptotic least squares and further analysis of the multiple regression model; Interaction models; Goodness of fit and adjusted (R^2).
Multiple regression models with qualitative information; Dummy variables; Restricted and unrestricted models; Introduction to heteroskedasticity.
Basic panel data; Difference-in-differences; Two-period panel data; Panel data estimators; Control groups.
Advanced panel data methods; Fixed effects; Fixed effects estimator; Random effects; Random effects estimators.
Time series data; stationarity.
Regression with time series data: distributed lag models.
Autocorrelation, correlogram and autoregressive models.
Regression with serially correlated errors.

Prerequisiti

Basic notions of probability and statistical inference

Core Documentation

J. Wooldridge, Introductory Econometrics: A Modern Approach, Cengage International Edition, 8th Edition (2025). Chapters: 1-14.

Attendance

Highly recommended

Type of evaluation

The examination consists of a written test with a number of multiple-choice questions and some open questions (one of which will require the interpretation of R output), followed by an oral discussion of the student’s written test.

Programme

Review of point estimation, interval estimation, hypothesis testing, and simple regression.
The nature of econometric and economic data; Further analysis of the simple regression model; Introduction to the multiple regression model.
Inference in the multiple regression model; The normality assumption; Hypothesis testing for a single parameter; Calculation and interpretation of the p-value; Confidence intervals.
Asymptotic least squares and further analysis of the multiple regression model; Interaction models; Goodness of fit and adjusted (R^2).
Multiple regression models with qualitative information; Dummy variables; Restricted and unrestricted models; Introduction to heteroskedasticity.
Basic panel data; Difference-in-differences; Two-period panel data; Panel data estimators; Control groups.
Advanced panel data methods; Fixed effects; Fixed effects estimator; Random effects; Random effects estimators.
Time series data; stationarity.
Regression with time series data: distributed lag models.
Autocorrelation, correlogram and autoregressive models.
Regression with serially correlated errors.

Prerequisiti

Basic notions of probability and statistical inference

Core Documentation

J. Wooldridge, Introductory Econometrics: A Modern Approach, Cengage International Edition, 8th Edition (2025). Chapters: 1-14.

Attendance

Highly recommended

Type of evaluation

The examination consists of a written test with a number of multiple-choice questions and some open questions (one of which will require the interpretation of R output), followed by an oral discussion of the student’s written test.

Programme

Review of point estimation, interval estimation, hypothesis testing, and simple regression.
The nature of econometric and economic data; Further analysis of the simple regression model; Introduction to the multiple regression model.
Inference in the multiple regression model; The normality assumption; Hypothesis testing for a single parameter; Calculation and interpretation of the p-value; Confidence intervals.
Asymptotic least squares and further analysis of the multiple regression model; Interaction models; Goodness of fit and adjusted (R^2).
Multiple regression models with qualitative information; Dummy variables; Restricted and unrestricted models; Introduction to heteroskedasticity.
Basic panel data; Difference-in-differences; Two-period panel data; Panel data estimators; Control groups.
Advanced panel data methods; Fixed effects; Fixed effects estimator; Random effects; Random effects estimators.
Time series data; stationarity.
Regression with time series data: distributed lag models.
Autocorrelation, correlogram and autoregressive models.
Regression with serially correlated errors.

Prerequisiti

Basic notions of probability and statistical inference

Core Documentation

J. Wooldridge, Introductory Econometrics: A Modern Approach, Cengage International Edition, 8th Edition (2025). Chapters: 1-14.

Attendance

Highly recommended

Type of evaluation

The examination consists of a written test with a number of multiple-choice questions and some open questions (one of which will require the interpretation of R output), followed by an oral discussion of the student’s written test.

Programme

Review of point estimation, interval estimation, hypothesis testing, and simple regression.
The nature of econometric and economic data; Further analysis of the simple regression model; Introduction to the multiple regression model.
Inference in the multiple regression model; The normality assumption; Hypothesis testing for a single parameter; Calculation and interpretation of the p-value; Confidence intervals.
Asymptotic least squares and further analysis of the multiple regression model; Interaction models; Goodness of fit and adjusted (R^2).
Multiple regression models with qualitative information; Dummy variables; Restricted and unrestricted models; Introduction to heteroskedasticity.
Basic panel data; Difference-in-differences; Two-period panel data; Panel data estimators; Control groups.
Advanced panel data methods; Fixed effects; Fixed effects estimator; Random effects; Random effects estimators.
Time series data; stationarity.
Regression with time series data: distributed lag models.
Autocorrelation, correlogram and autoregressive models.
Regression with serially correlated errors.

Prerequisiti

Basic notions of probability and statistical inference

Core Documentation

J. Wooldridge, Introductory Econometrics: A Modern Approach, Cengage International Edition, 8th Edition (2025). Chapters: 1-14.

Attendance

Highly recommended

Type of evaluation

The examination consists of a written test with a number of multiple-choice questions and some open questions (one of which will require the interpretation of R output), followed by an oral discussion of the student’s written test.

Programme

Review of point estimation, interval estimation, hypothesis testing, and simple regression.
The nature of econometric and economic data; Further analysis of the simple regression model; Introduction to the multiple regression model.
Inference in the multiple regression model; The normality assumption; Hypothesis testing for a single parameter; Calculation and interpretation of the p-value; Confidence intervals.
Asymptotic least squares and further analysis of the multiple regression model; Interaction models; Goodness of fit and adjusted (R^2).
Multiple regression models with qualitative information; Dummy variables; Restricted and unrestricted models; Introduction to heteroskedasticity.
Basic panel data; Difference-in-differences; Two-period panel data; Panel data estimators; Control groups.
Advanced panel data methods; Fixed effects; Fixed effects estimator; Random effects; Random effects estimators.
Time series data; stationarity.
Regression with time series data: distributed lag models.
Autocorrelation, correlogram and autoregressive models.
Regression with serially correlated errors.

Prerequisiti

Basic notions of probability and statistical inference

Core Documentation

J. Wooldridge, Introductory Econometrics: A Modern Approach, Cengage International Edition, 8th Edition (2025). Chapters: 1-14.

Attendance

Highly recommended

Type of evaluation

The examination consists of a written test with a number of multiple-choice questions and some open questions (one of which will require the interpretation of R output), followed by an oral discussion of the student’s written test.