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
9
Nonstationary time-series