Free online courseEconometrics

Course Description

Welcome to the world of Econometrics, masterfully presented by [Author]. This comprehensive course spans 18 hours and 21 minutes of in-depth instruction, giving students a thorough grounding in the principles and applications of econometrics within the broader context of Business Administration, specifically under the banner of Corporate Finance.

Rated an impressive 4.1 out of 5 stars, this course is designed to cater to undergraduate students who are stepping into the realm of econometrics. The curriculum initiates with foundational concepts, addressing the fundamental question, "What is econometrics?" and setting it against hard sciences. It provides a solid understanding of natural experiments and transitions seamlessly into the exploration of populations and samples in econometrics.

Students embark on a journey through the intricacies of estimators, learning about their essential properties, including unbiasedness and consistency, and the trade-off between these two. The course delves into the efficiency of estimators and summarizes the characteristics that constitute a good estimator. One of the key aspects covered is the concept of the line of best fit in econometrics, supported by the necessary mathematical underpinnings.

The derivation and application of Least Squares Estimators are thoroughly dissected over several parts, ensuring students grasp the significance of these estimators as Best Linear Unbiased Estimators (BLUE). The exploration of random variable properties, such as expectations, moments, skewness, kurtosis, and the nuances between population and sample quantities, builds a robust statistical foundation.

The instructor meticulously unpacks the Gauss-Markov assumptions essential for Best Linear Unbiased Estimators, addressing potential problems such as omitted variable bias, reverse causality, measurement error, and functional misspecification. Serial correlation, heteroskedasticity, and multicollinearity are analyzed alongside remedies and testing methods, ensuring students are well-versed in identifying and correcting these issues.

The course advances to explore the elegance of hypothesis testing within linear regression models, grounded firmly in the Central Limit Theorem. Various tests for normally distributed errors, the interpretation of regression coefficients in both linear and log models, and the application of dummy variables are discussed in depth.

The lecture series extends into advanced hypothesis testing, using F tests and examining linear combinations of parameters, alongside interpreting confidence intervals. Several tests for heteroscedasticity and serial correlation, including the Goldfeld-Quandt, Breusch-Pagan, White, Durbin-Watson, and Breusch-Godfrey tests, are elaborated upon.

Students also explore sophisticated estimation techniques like Weighted Least Squares (WLS) and Generalized Least Squares (GLS), especially when dealing with serial correlation. An introduction to Instrumental Variables (IV) and Two Stage Least Squares (TSLS) provides a bridge to handling endogeneity in econometric models.

Further exploration of time series analysis brings the course to its advanced segments, discussing stationary processes, autoregressive and moving average models, and tests for unit roots like the Dickey-Fuller test. The concept of cointegration and its testing mechanisms are introduced, culminating in a robust framework for analyzing time series data.

By the end of this course, participants will have a comprehensive understanding of econometrics, well-equipped to apply these principles in corporate finance and beyond. With problem sets and practical examples enriching the learning experience, this course is a valuable resource for aspiring econometricians.