|Title||INTRODUCTION TO ECONOMETRICS|
|Field of Study||Economics|
|Professor||Jong-Min Kim (firstname.lastname@example.org)|
|Delivery Type||Online Track (100% online course): Pre-recorded|
First, this course will cover the statistical concepts for econometric analysis such as single (multiple) regression analysis, ordinary least squares (OLS), Logit and Probit Models and violations of standard assumptions such as heteroskedasticity and multicollinearity. Second, this course will introduce the basic models for time series analysis such as stationary and nonstationary time series, analysis of trends using regression methods, ARIMA, model specification, transformations, parameter estimation, model diagnostics, forecasting, Seasonal ARIMA time series models, and GARCH models.
The objective of this course is to introduce basic concepts and theory of econometric analysis with practical applications, mainly stock financial data.
Basic College Algebra.
Materials: Regular Calculator needed.
Textbook: Damodar Gujarati, Econometrics by Example, 2nd Edition, Palgrave Macmillan (2014). (Optional)
Class 1: Chapter 1: The linear regression model: an overview
Class 2: Chapter 2: Functional forms of regression models
Class 3: Chapter 3: Qualitative explanatory variables regression models
Class 4: Chapter 4: Regression diagnostic I: multicollinearity
Class 5: Chapter 5: Regression diagnostic II: heteroscedasticity
Class 6: Chapter 6: Regression diagnostic III: autocorrelation
Class 7: Midterm Exam
Class 8: Chapter 7: Regression diagnostic IV: model specification errors
Class 9: Chapter 8: The logit and probit models
Class 10: Chapter 10: Ordinal regression models
Class 11: Chapter 12: Modeling count data: the Poisson and negative binomial regression models
Class 12: Chapter 13: Stationary and nonstationary time series
Class 13: Chapter 15: Asset price volatility: the ARCH and GARCH models
Class 14: Chapter 16: Economics forecasting
Class 15: Final Exam
|Last Updated||April 16, 2021|