PMTEI Theory of Econometrics I

Faculty of Economics and Administration
Spring 2009
Extent and Intensity
2/1/0. 4 credit(s). Type of Completion: zk (examination).
Teacher(s)
RNDr. Dalibor Moravanský, CSc. (lecturer)
RNDr. Dalibor Moravanský, CSc. (seminar tutor)
doc. Ing. Daniel Němec, Ph.D. (seminar tutor)
Guaranteed by
prof. Ing. Osvald Vašíček, CSc.
Department of Economics – Faculty of Economics and Administration
Contact Person: Lydie Pravdová
Timetable
Wed 11:05–12:45 P106
  • Timetable of Seminar Groups:
PMTEI/1: each odd Wednesday 15:30–17:05 VT206, D. Moravanský
PMTEI/2: each even Wednesday 15:30–17:05 VT206, D. Moravanský
Prerequisites (in Czech)
Pro náležité porozumění obsahu předmětu je nutná znalost látky z předmětů: Mikroekonomie-Makroekonomie-Matematika-Statistika
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
Theory of Econometrics (TEEO, TEEK) The purpose of the course is to acquaint students with the quantitative analysis of economic systems, which results in an economic model that is, by means of mathematical transformation and statistic specification, transformed into an econometric model. Procedures of the econometric model quantification will be clarified, which consists in parameter estimate, data-based model verification, its statistic and econometric verification and in exploiting the model for economic analysis and for programming. Procedures will be presented on real economic systems and data with examples of practical use of the model, serving as a tool of economic decision-making.
Syllabus
  • 1.An introduction to econometrics.Literature. The importance of economic theory in econometrics (the correct specification of the model). The task of mathematical statistics in econometrics (the exact quantification).
  • The brief historical development of econometrics. The most important persons. 2.The classical linear regression one-equation model: The estimation of parameters by the ordinary least squares method OLS: the algorithm, the expression of estimator, properties of the parameters estimated, the estimate of the residual variance - formulation, derivation.
  • 3. The classical linear regression one-equation model with normally distributed disturbances: distribution of the parameter vector, distribution of the SSE - detailed derivation. Distribution of the t-statistics and their application at testing of significancy
  • 4. The classical normal linear regression one-equation model: testing of the regression parameters (with tables of the t-distribution). Testing of the goodness of fit with the data. coefficient of determination R2. The Outline of the confidence intervals/regions.
  • 5. The classical normal linear regression one-equation model: The conditions of the rational-prediction of the model relative to behaviour of the explanatory variables and of disturbances. The investigation of the stability of the model parameters as necessary condition for the predictive purpose. 6.Classical normal linear regression one-equation model: The evaluation of the prediction error of the model, construction of the confidence interval for the predicted values, the properties of the error vector. The possibilities of the using of one-equation model for forecasting.
  • 7.The generalized linear regression one-equation model: The generalized least squares method GLS: the algorithm, motivation, the expression for the estimator, properties. The heteroskedasticity problem. The problem of the evaluation of the covariance matrix sigma. The WLS method
  • 8. The generalized linear regression one-equation model: The problem of autocorrelated residuals and methods of its solution. Autocovariance and autocorrelation function. The estimate of the coefficient of autocorrelation. Cochrane-Orchutte method. Two-stage Durbin method.
  • 9. Special tools for quantification of the regression model: The multicollinearity problem, the ways and methods of its solution:(omitting of the variable,ridge regression, principal components, using of the a priori information
  • 10.Special tools for quantification of the regression model: Using of the dummy variables in an econometric model. The actual situations connected with using of dummy variables: seasonality, age and social structure, etc. Combinations of the criteria.
  • 11.Distributed lag models(exogennou variables): Models with finite distributed lags. (linear, double-linear).The Almon model: polynomial approximation,formulation, derivation, estimation. he Koyck model: Geometrically distributed lags: formulation, derivation, estimation
  • 12.Distributed lag models(endogenous variables): The partial adaptation model and its estimation.- The adaptive expectations and its estimation. The rational expectations model and its estimation
  • 13.Non-standard situations at the quantification of one-equation model(expression for the estimation bias). Errors in variables and in the measurement (the inconsistency rate). The instrumental variables in one-equation model.
  • 14. Nonlinear specification of the model and its quantification ( the outline of the NLLS-estimation). The basic types of the nonlinear specification (production, cost, utility, and expenditure function, the commodity/factor demand functions. The linearization of the model.
Literature
  • HUŠEK, Roman. Základy ekonometrie. 2. přeprac. vyd. Praha: Vysoká škola ekonomická v Praze, 1992, 221 s. ISBN 8070795662. info
  • HUŠEK, Roman. Základy ekonometrické analýzy. Modely a metody. Praha: Vysoká škola ekonomická v Praze, 1998. info
Assessment methods
Presentation of the subject matter is performed using the audio-visual devices. The written exam lasts 80 minutes and its content consists of 7-8 tasks. In order to pass it, 55% points(of the 100%) are required. The oral part of the exam consists in talking about 1-2 special econometric themes.
Language of instruction
Czech
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
Information on course enrolment limitations: v případě více než 24 studentů proběhne cvičení ve 2 skupinách
The course is also listed under the following terms Spring 2002, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008.
  • Enrolment Statistics (recent)
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