Course outline
1
Introductory
1.1
Why a separate discipline?
1.2
Basic steps of the analysis
2
Model specification
2.1
Variable selection
2.2
Functional form selection
3
Data collection
3.1
Data structure
3.2
Data description
4
R programming environment
4.1
RStudio – part A
4.2
RStudio – part B
5
Parameters estimation
5.1
OLS method
5.2
OLS method in RStudio
5.3
OLS assumptions
5.4
OLS properties
6
Post estimation steps
6.1
Significance testing
6.2
Goodness-of-fit measuring
6.3
Diagnostick checking
6.4
Diagnostic checking in RStudio
7
Utilizing dummy variables
8
Time-series analysis
8.1
Properties of time-series
8.2
Trend and seasonality
9
Nonstationary time-series
9.1
Unit root testing
10
Dynamic models and cointegration
10.1
Types of dynamic models
10.2
Impulse response function
10.3
Cointegration
11
VAR models
11.1
Granger causality
Econometrics
10
Dynamic models and cointegration