Lecturer(s)
|
-
Homolka Lubor, Ing. Ph.D.
|
Course content
|
"Introduction to the history of econometrics, historical overview. "Model design process and its verification. Data type, coding of qualitative variables, reparameterization of general model. "Types of regression functions. Methods for estimating regression parameters of functions. "Statistical verification of the model based on statistical hypotheses and other indicators. "Empirical analysis of basic micro- and macro-economic models. "Decomposition approach to time series. Seasonal adjustment. "Filters and other time series smoothing methods. "Analysis of the residual component of the econometric model. Econometric verification of the model and the consequences of violation of assumptions.
|
Learning activities and teaching methods
|
unspecified
|
learning outcomes |
---|
Knowledge |
---|
use regression analysis toolkit |
use regression analysis toolkit |
compute expected values of an economic event based on quantitative data |
compute expected values of an economic event based on quantitative data |
build a complex time-series model |
build a complex time-series model |
predict value of the time series based on the regression model |
predict value of the time series based on the regression model |
posoudit kvalitu odhadnutého modelu |
posoudit kvalitu odhadnutého modelu |
use regression analysis toolkit |
use regression analysis toolkit |
compute expected values of an economic event based on quantitative data |
compute expected values of an economic event based on quantitative data |
build a complex time-series model |
build a complex time-series model |
predict value of the time series based on the regression model |
predict value of the time series based on the regression model |
assess a quality of the econometrical model |
assess a quality of the econometrical model |
Skills |
---|
uses advanced regression analysis tools for the economic modelling tasks |
uses advanced regression analysis tools for the economic modelling tasks |
enumerates expected out-of-sample or out-of-time value of an economic phenomenon |
enumerates expected out-of-sample or out-of-time value of an economic phenomenon |
builds econometric model for the decision making purposes |
builds econometric model for the decision making purposes |
predicts time-series values allowing better understanding and decision making |
predicts time-series values allowing better understanding and decision making |
assess a quality of an estimated model in the light of historical values and underlying uncertainty |
assess a quality of an estimated model in the light of historical values and underlying uncertainty |
uses advanced regression analysis tools for the economic modelling tasks |
uses advanced regression analysis tools for the economic modelling tasks |
enumerates expected out-of-sample or out-of-time value of an economic phenomenon |
enumerates expected out-of-sample or out-of-time value of an economic phenomenon |
builds econometric model for the decision making purposes |
builds econometric model for the decision making purposes |
predicts time-series values allowing better understanding and decision making |
predicts time-series values allowing better understanding and decision making |
assesses a quality of an estimated model in the light of historical values and underlying uncertainty |
assesses a quality of an estimated model in the light of historical values and underlying uncertainty |
Recommended literature
|
-
GUJARATI, D.N., PORTER, D.C. Basic econometrics. 5th ed. Boston: McGraw-Hill, 2009. ISBN 978-0-07-337577-9.
-
KLÍMEK, P. Ekonometrie: studijní pomůcka pro distanční studium. Zlín: Univerzita Tomáše Bati ve Zlíně, 2010. ISBN 978-80-7318-942-6.
|