Econometrics
Econometrics is the application of statistical methods to economic data, serving as a vital branch of economics that seeks to provide empirical insights into economic relationships. It functions as a powerful tool for empirical research, enabling economists to test hypotheses, forecast outcomes, and offer policy recommendations based on rigorous analysis of economic data.
I am fortunate to learn econometrics from Professor Mariya Naumova at Rutgers Business School. She is an exceptional teacher who delves into econometric topics with depth and practicality, making complex concepts accessible and relevant.
This topic also contains a practical component, where we will use Excel and R to implement econometric models and analyze real-world data.
Pre-requisite: Probability and Statistics, Calculus
1. Univariate Regression
- 1.1 How to find $\beta_0$ and $\beta_1$
- 1.2 OLS Estimation
- 1.3 Gauss-Markov Assumptions
- 1.4 MLE Approach
- 1.5 Variability in Data SST SSE SSR
- 1.6 Biased on SST
- 1.7 Hypothesis Testing on $\beta_1$
- 1.8 F-test
- 1.9 Test based on corralation coefficient
- 1.10 Interval estimator of $\beta_1$
- 1.11 t-test on $\beta_0$
- 1.12 Subjective check of assumptions
- 1.13 Statistic tests on assumptions
2. Multivariable Regression
- 2.1 OLS Estimators
- 2.2 F-test
- 2.3 Gauss-Markov Assumptions
- 2.4 Violation of Gauss-Markov Assumptions
- 2.5 Generalized Least Squares
- 2.6 Overfitting
- 2.7 R-squared and Adjusted R-squared
- 2.8 Categorical data
3. Logistic Regression
- 3.1 Sigmoid Function
- 3.2 Regression Model
- 3.3 Maximum Likelihood Estimation
- 3.4 M Categories of Y
4. Basic Time-Series Process
- 4.1 Basic Definitions
- 4.2 Example of non-stationary process
- 4.3 Lag operator
- 4.4 Autoregressive Process
- 4.5 Moving Average Process
- 4.6 ARMA Process
- 4.7 Tests for Stationarity
- 4.8 Conversion of Non-stationary to Stationary
- 4.9 ARIMA-GARCH Model
Additionally, further topics will be explored in Time Series Analysis.