Applied Econometrics with R

Topics:


This course covers the econometric models discussed in the lectures "Econometrics I" and "Econometrics II". Within this course R (http://www.r-project.org) is presented as an alternative software for empirical analyses by means of selected case studies.

Course overview (tentative schedule):

(i) Introduction:
Getting started & working with R.

(ii) R basics:
R as a calculator; matrix operations; R as a programming language; data management; R graphics; exploratory data analysis.

(iii) Linear regression:
Simple/multiple linear regression; factors/interactions and weights; linear regression with time series data and with panel data; diagnostics & alternative methods of linear regression.

(iv) Diagnostics and alternative methods of regression:
Diagnostic tests; testing for heteroskedasticity/functional form/autocorrelation; robust standard errors & tests; HC/HAC estimators; resistant/quantile regression.

(v) Models of Microeconometrics:
Generalized linear models / binary dependent variables / models for count data; censored dependent variables.

(vi) Time Series:
Classical decomposition / filtering / exponential smoothing; stationarity, unit roots and cointegration; time series regression; extensions.


Weitere Informationen finden Sie im UniVZ.