Empirical or Theoretical Paper, Case Studies, Examination Test
Language:
English
Prerequisites:
Mathematics, Statistics
Course content:
Lectures and Classes:
Simple Regression Model. Ordinary Least Squares (OLS) Estimation.
Multiple Regression Model. Diagnostic Tests. Specification Analysis and Model Selection. Transformation of Variables. Nonlinear Regression Models. Discrete and Limited Dependent Variable Models. Simultaneous-Equations Models. Time-Series and Dynamic Models. Applications in Marketing, Microeconomics, Macroeconomics and Finance.
Computer Classes:
Application of Econometric Methods in Marketing, Microeconomics, Macroeconomics and Finance with the Use of Econometric Computer Package GRETL.
Learning outcomes:
Knowledge:
knowledge of econometric models, methods and applications in economy
Competence and skills:
data analysis, applications of econometric methods in economy using software (MS Office and GRETL)
Contact person:
Prof. Józef Dziechciarz, Mgr Anna Król
Literature:
Maddala G.S.: Introduction to Econometrics, John Wiley & Sons 2001.
Heij Ch., de Boer P., Franses P.H., Kloek T., van Dijk H.K.: Econometric Methods with Application in Business and Economics, Oxford University Press 2004.
Brooks Ch.: Introductory Econometrics for Finance, Cambridge University Press 2002.
Dougherty Ch.: Introduction to Econometrics, Oxford University Press 2002.
Faculty:
All Faculties
Is this a copy of the lecture already taught on UE?
Lectures and Classes:
Research Design (Research Topic, Data Sources, Sample Selection, Literature Review, Ethical Aspects), Basic Data Analysis (Measurement Scales, Descriptive Statistics, Correlation Analysis, Regression Analysis, Hypothesis Testing and Inference), Advanced Data Analysis and Special Topics (Classification Trees, Clustering Analysis, Correspondence Analysis, Binary Choice Models, Models for Time Series Data, Survey Data Analysis, Simultaneous-Equations Models), Writing Research Report (Report Structure, Theoretical Introduction, Data Presentation, Results Presentation, Graphs and Plots, References), Presentation of the Results (Preparing Presentation, Effective Presentation Techniques).
Computer Classes:
Conducting Quantitative Economic Research with the Use of Computer Tools: MS Excel and Statistica. Preparing Presentation of the Research Results using Computer Tools: MS Power Point or Latex Beamer Class.
Learning outcomes:
Knowledge:
basic knowledge of research design and data analysis methods
Competence and skills:
designing economic research, mastering data analysis methods and techniques using software (MS Excel, Statistica), preparing presentations of the results using software (MS Power Point or Latex Beamer Class)
Contact person:
Prof. Józef Dziechciarz, Dr Klaudia Przybysz, Mgr Anna Król
Literature:
Kumar R.: Research Methodology, SAGE Publications, 2005.
Maddala G.S.: Introduction to Econometrics, John Wiley & Sons 2001.
Anderson T. W., Finn J. D.: The new statistical analysis of data, Springer-Verlag, 1997.
Churchill G.A. Jr.: Marketing Research: Methodological Foundations, Dryden Press, 1995.
Brooks Ch.: Introductory Econometrics for Finance, Cambridge University Press 2002.
Faculty:
All Faculties
Is this a copy of the lecture already taught on UE?
Lectures and Classes:
Survey Design, Sample Design, Survey Sampling, Survey Data Collection, Survey Errors, Missing Data, Survey Data Preparation and Transformation, Basic Survey Data Analysis (Measurement Scales, Descriptive Statistics, Correlation Analysis, Tabulation, Confidence Intervals, Weighted Estimation, Probability Distributions, Variance Estimation, Hypothesis Testing), Advance Survey Data Analysis (Classification Trees, Clustering Analysis, Correspondence Analysis, Logistic Regression), Reporting Survey Analysis Results, Graphical Presentation of Survey Data.
Computer Classes:
Application of Survey Data Analysis Methods with the Use of Computer Tools: MS Excel and Statistica.
Learning outcomes:
Knowledge:
basic knowledge of survey data analysis theory and methods
Competence and skills:
designing surveys, mastering survey data analysis methods and techniques using software (MS Excel, Statistica)
Contact person:
Prof. Józef Dziechciarz, Dr Klaudia Przybysz, Mgr Anna Król
Literature:
Rossi P. H., Wright J. D., Anderson A. B. (ed.): Handbook of survey research, Academic Press, 1983.
Heeringa S. G., West B. T., Berglund P. A.: Applied Survey Data Analysis, Chapman & Hall, 2010.
Chambers R. L., Skinner C. J.: Analysis of Survey Data, John Wiley & Sons, 2005.
Anderson T. W., Finn J. D.: The new statistical analysis of data, Springer-Verlag, 1997.
Faculty:
All Faculties
Is this a copy of the lecture already taught on UE?
Lectures:
Introduction to R Environment, Simple Data Manipulations, Numbers and Vectors, Objects, Arrays and Matrices, Lists and Data Frames, Working with Files, Probability Distributions, Grouping, Loops and Conditional Execution, Statistical Models in R (Descriptive Statistics, Cross Sectional Models, Time Series Models, Limited Dependent Variable Models, Panel Data Models, Multivariate Statistical Analysis), Graphs and Plots in R, Writing Functions and Programs in R Environment.
Computer Classes:
Introduction to R Environment. Solving Economic Problems and Case Studies Using R Environment.
Learning outcomes:
Knowledge:
basic knowledge of economic data analysis theory and methods, basic knowledge of programming in R Environment
Competence and skills:
mastering data analysis methods and techniques using R Environment, writing own functions and programmes in R Environment
Contact person:
Prof. Józef Dziechciarz, Dr Klaudia Przybysz, Mgr Anna Król
Literature:
Venables W. N., Smith D., M.: An Introduction to R, www.r-project.org, 2010.
Farnsworth G.V., Econometrics in R, http://cran.r-project.org/doc/contrib/Farnsworth-EconometricsInR.pdf, 2008.
Maddala G.S.: Introduction to Econometrics, John Wiley & Sons 2001.
Heiberger R. M., Holland B.: Statistical analysis and data display : an intermediate course with examples in S-Plus, R, and SAS, New York, 2004.
Faculty:
All Faculties
Is this a copy of the lecture already taught on UE?
Lectures and Classes:
Correlation. Simple Regression Model. Ordinary Least Squares (OLS) Estimation. Assumptions Underlying Classical Linear Regression Model.
Multiple Regression Model. Properties of the OLS Estimators. Goodness of Fit. Hypothesis Testing: t-test, F-test. Normality of the Disturbance Term. Heteroscedasticity. Autocorrelation. Specification Analysis and Model Selection. Multicollinearity. Transformation of Variables. Nonlinear Regression Models. Dummy Variables. Binary Choice Models. Maximum Likelihood (ML) Estimation. Simultaneous-Equations Models.
Introduction to Time-Series Models. Laboratories:
Application of Econometric Methods in Economics, Finance and Business with the Use of Computer Tools: MS Excel and Econometric Computer Package GRETL.
Learning outcomes:
Knowledge:
basic knowledge of econometric theory, models and methods
Competence and skills:
data analysis, techniques of estimation and verification of econometric models using software (MS Excel and GRETL)
Contact person:
Prof. Józef Dziechciarz, Mgr Anna Król
Literature:
Maddala G.S.: Introduction to Econometrics, John Wiley & Sons 2001.
Greene W.H.: Econometric Analysis, Prentice Hall 1999.
Empirical or Theoretical Paper, Case Studies, Examination Test
Language:
English
Prerequisites:
Mathematics, Statistics
Course content:
Lectures and Classes:
Simple Regression Model. Ordinary Least Squares (OLS) Estimation.
Multiple Regression Model. Diagnostic Tests. Specification Analysis and Model Selection. Introduction to Time Series Models. Stationary and Non-stationary Stochastic Processes. Seasonality. Stationarity. Testing for Stationarity. ARIMA Models. ARCH Models. Cointegration. Testing for Contegration. Error Correction Models.
Computer Classes:
Application of Econometric Methods in Modelling Financial Time Series with the Use of Computer Tools MS Office and gretl.
Learning outcomes:
Knowledge:
knowledge of econometric models, methods and applications in finance
Competence and skills:
data analysis, applications of econometric methods in modelling financial time series using software (MS Office and GRETL)
Contact person:
Prof. Józef Dziechciarz, Mgr Anna Król
Literature:
Taylor S.: Modelling financial time series, John Wiley & Sons,1992.
Brooks Ch.: Introductory Econometrics for Finance, Cambridge University Press 2002.
Mills T. C., Markellos R. N.: The econometric modelling of financial time series, Cambridge University Press, 2008.
Greene W.H.: Econometric Analysis, Prentice Hall 1999.
Faculty:
All Faculties
Is this a copy of the lecture already taught on UE?
Lectures and Classes:
Introduction to Multivariate Data Analysis, Measurement Scales, Graphical Presentation of Multidimensional Data, Correlation Analysis, Regression Analysis, Principal Component Analysis, Factor Analysis, Classification, Clustering, Classification Trees, Correspondence Analysis, Multidimensional Scaling.
Computer Classes:
Application of Multivariate Statistical Analysis Methods with the Use of Computer Tools: MS Excel and Statistica.
Learning outcomes:
Knowledge:
basic knowledge of multivariate statistical analysis theory and methods
Competence and skills:
mastering multivariate statistical analysis methods and techniques using software (MS Excel, Statistica)
Contact person:
Prof. Józef Dziechciarz, Dr Klaudia Przybysz, Mgr Anna Król
Literature:
Gnanadesikan R.: Methods for statistical data analysis of multivariate observations, John Wiley & Sons, 1997.
Anderson T. W., Finn J. D.: The new statistical analysis of data, Springer-Verlag, 1997.
Heiberger R. M., Holland B.: Statistical analysis and data display: an intermediate course with examples in S-Plus, R, and SAS, New York, 2004.
Andersen E. B.: Introduction to the statistical analysis of categorical data, Springer-Verlag, 1997.
Faculty:
All Faculties
Is this a copy of the lecture already taught on UE?
Lectures and Classes:
Introduction to Marketing Research, Research Design, Data Collection and Analysis, Survey and Quantitative Observation Techniques, Measurement and Scaling, Questionnaire Design, Sampling: Design and Procedures, Data Preparation, Frequency Distribution, Cross-tabulation and Hypothesis Testing, Analysis of Variance and Covariance, Correlation and Regression, Discriminant Analysis, Factor Analysis, Cluster Analysis, Multidimensional Scaling and Conjoint Analysis, Writing Marketing Research Report.
Computer Classes:
Application of Marketing Research Methods with the Use of Computer Tools: MS Excel and Statistica.
Learning outcomes:
Knowledge:
basic knowledge of marketing research theory and methods
Competence and skills:
mastering marketing research methods and techniques using software (MS Excel, Statistica)
Contact person:
Prof. Józef Dziechciarz, Dr Klaudia Przybysz, Mgr Anna Król
Literature:
Churchill G.A. Jr.: Marketing Research: Methodological Foundations, Dryden Press, 1995.
Zikmund W. G.: Exploring marketing research, Dryden Press, 1994.
Anderson T. W., Finn J. D.: The new statistical analysis of data, Springer-Verlag, 1997.
Andersen E. B.: Introduction to the statistical analysis of categorical data, Springer-Verlag, 1997.
Malhotra N. K., Birks D. F.: Marketing research : an applied approach, Prentice Hall, 1999.
Webb J. R.: Understanding and designing marketing research, Academic Press, 1992.
Faculty:
All Faculties
Is this a copy of the lecture already taught on UE?
