Econometrics 2400-ZU2EKO
1. Introduction. The concept of econometrics and econometric model. Examples of econometric models.
2. Ordinary Least Squares (OLS). Properties of regression hyperplane.
3. Decomposition of the residual sum of the squares, R2 and its properties.
4. Assumptions of the Classic Linear Regression Model (CLRM). Estimation of CLRM by OLS.
5. Properties of OLS estimators in CLRM (Gauss-Markov's theorem). Estimator of variance-covariance matrix.
6. Statistical reasoning in CLRM. Distribution of OLS estimators in CLRM. Testing of simple and complex hypotheses: t - test and test F.
7. Interpretation of model parameters.
8. Diagnostics in CLRM I - testing: normality of random error (Jarque-Berra test), correctness of functional form (RESET test), stability of parameters (Chow test).
9. Diagnostics in CLRM I - testing: homoscedascity (White test, Breusch-Pagan test), autocorrelation (Durbin-Watson test, Breusch-Godfrey test).
10. Specific problems: collinearity (concept, detection, handling); qualitative explanatory variables (coding, interpretation of parameters); omitted variables and insignificant variables; atypical observations and outliers (concept, detection, handling).
11. Aspherical random errors - causes and consequences of heteroscedasticity and autocorrelation. Methods: robust estimators of variance-covariance matrix; Generalized Least Squares Method (GLS).
12. Dynamic models I: Distributed lags models (DL), Autoregressive distributed lags models (ADL). Granger causality. The problem of autocorrelation in dynamic models.
13. Stationary and non-stationary variables. Variable integration order testing - Dickey-Fuller test, KPSS test. Problem of spurious regression.
14. Models for discrete dependent variables: logit, probit, ordered logit and ordered probit, Poisson's model.
Type of course
Prerequisites (description)
Course coordinators
Learning outcomes
A) Knowledge
The student knows and understands the assumptions of the most popular statistical models used in econometric analysis. The student has an in-depth knowledge of the basic problems of empirical data analysis. He knows and understands advanced methods of statistical inference used in econometrics.
1. The student is familiar with OLS and understands the need to use more advanced econometric techniques when the assumptions of the CLRM are not met.
2. The student knows the assumptions of the Classical Linear Regression Model and ways of testing them.
3. The student knows the basic problems related to failure to meet the assumptions of the Classical Linear Regression Model, omitted variables, insignificant variables, atypical and erroneous observations, collinearity. The student knows the ways of obtaining data and their limitations.
4. The student knows and understands the concept oftime series, dynamic model, lag, long-term equilibrium, short and long-term multiplier. He understands the concept of Granger causality, nonstationarity and the problem of spurious regression.
5. The student knows how to define statistical models used in the analysis of binary and discrete variables.
6. The student knows models for count data.
B) Skills
The student is able to choose a statistical model and estimation method for the analyzed problem and set of data. The student is able to use advanced econometric methods to detect quantitative and qualitative relations between variables from data, verify theoretical hypotheses on the basis of data and formulate forecasts.
1. The student has an in-depth ability to select a statistical model and estimation method for the analyzed problem and set of data. S2A_U02
2. The student is able to detect atypical and erroneous observations in empirical data.
3. The student is able to diagnose the problem of collinearity in the model.
4. The student is able to test the assumptions of the Classical Linear Regression Model and is able to act in the event of failure to meet these assumptions.
5. The student is able to select an appropriate set of explanatory variables for the model on the basis of statistical criteria, and can compare competing models using information criteria and methods from general to specific and conclusion tests.
6. The student has the ability to build simple forecasting models estimated on time series, investigate the occurrence of causal relationships between variables and formulate forecasts on the basis of constructed models. The student can quantify the short and long term effects of changes in explanatory variables on the explained variable.
7. The student is able to select and use a model for empirical data in case the dependent variable is a binary, discrete, cropped or censored variable. The student knows how to examine the quality of fit in case of such regressions.
The student is able to put forward research hypotheses, which can be verified on the basis of empirical material. The student is able to relate the formulated hypotheses to the literature of the subject. The student is able to construct an appropriate set of data, perform estimation and interpret the results obtained. The student is able to present the obtained results in the form of a written report from the study.
C) Social competences
The student is aware of the necessity of verifying economic theories with the use of empirical data. At the same time the student is aware of the limitations of models used in empirical analysis. The student is able to plan and cooperate in a group to conduct an empirical research.
1. The student understands that economic theories are controversial and that it is necessary to confront them with empirical data. The student is able to imagine whether a given economic hypothesis can be subject to empirical verification.
2. The student is aware that quantitative methods have limitations due to imperfect data sets at our disposal and the simplified nature of the models used. He understands that quantitative research methods in economics are constantly evolving.
3. The student is ready to complete his knowledge on the basis of independently selected literature on a specific subject.
4. The student is able to work in a team, can plan an empirical study and jointly prepare a report presenting the results of these studies.
KW01, KU01
Assessment criteria
Written exam: 3 theoretical questions and 3 tasks;
Bibliography
Mandatory literature
Ekonometria, Jerzy Mycielski, 2010
Additional literature:
Greene William H., Econometric Analysis, 5th edition;
Gruszczyński Marcin, Podgórska Maria, Ekonometria, Warszawa 2004;
Kufel Tadeusz, Ekonometria. Rozwiązywanie problemów z wykorzystaniem programu GRETL, Warszawa 2007
Maddala G.S., Ekonometria, Warszawa 2006;
Additional information
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