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
6.5
Homework assignment
7
Time-series analysis
7.1
Properties of time-series
7.2
Trend and seasonality
7.3
Time-series analysis in RStudio
8
Nonstationary time-series
8.1
Unit root testing
9
Dynamic models and cointegration
9.1
Types of dynamic models
9.2
Impulse response function
9.3
Cointegration
10
VAR models
10.1
Granger causality
10.2
VEC representation
Econometrics
9.2
Impulse response function