A full course in econometrics by Ben Lambert (Undergraduate level)

by The Foundation

Videos
Serial Correlation summary 5:48
Serial Correlation summary The Foundation
Serial Correlation - as a symptom of omitted variable bias 4:43
Serial Correlation - as a symptom of omitted variable bias The Foundation
Serial Correlation - as a symptom of functional misspecification 3:24
Serial Correlation - as a symptom of functional misspecification The Foundation
Serial Correlation - caused by measurement error 2:32
Serial Correlation - caused by measurement error The Foundation
Serial correlation biased standard errors (advanced topic) - part 1 3:54
Serial correlation biased standard errors (advanced topic) - part 1 The Foundation
Serial correlation biased standard errors (advanced topic) - part 2 4:27
Serial correlation biased standard errors (advanced topic) - part 2 The Foundation
Heteroskedasticity summary 4:05
Heteroskedasticity summary The Foundation
Heteroskedastic errors - example 1 4:29
Heteroskedastic errors - example 1 The Foundation
Heteroskedastic errors - example 2 4:18
Heteroskedastic errors - example 2 The Foundation
Heteroskedasticity caused by data aggregation (advanced topic) 6:36
Heteroskedasticity caused by data aggregation (advanced topic) The Foundation
Perfect collinearity - example 1 3:40
Perfect collinearity - example 1 The Foundation
Perfect collinearity - example 2 3:22
Perfect collinearity - example 2 The Foundation
Multicollinearity 5:16
Multicollinearity The Foundation
Index - where we currently are in the overall plan of econometrics 3:02
Index - where we currently are in the overall plan of econometrics The Foundation
Gauss-Markov proof part 1 (advanced) 4:01
Gauss-Markov proof part 1 (advanced) The Foundation
Gauss-Markov proof part 2 (advanced) 7:03
Gauss-Markov proof part 2 (advanced) The Foundation
Gauss-Markov proof part 3 (advanced) 5:04
Gauss-Markov proof part 3 (advanced) The Foundation
Gauss-Markov proof part 4 (advanced) 4:26
Gauss-Markov proof part 4 (advanced) The Foundation
Gauss-Markov proof part 5 (advanced) 5:10
Gauss-Markov proof part 5 (advanced) The Foundation
Gauss-Markov proof part 6 (advanced) 3:43
Gauss-Markov proof part 6 (advanced) The Foundation
Errors in populations vs estimated errors 4:02
Errors in populations vs estimated errors The Foundation
Estimated variance of OLS estimators - intuition behind maths 3:51
Estimated variance of OLS estimators - intuition behind maths The Foundation
Variance of OLS estimators in the presence of heteroscedasticity 4:05
Variance of OLS estimators in the presence of heteroscedasticity The Foundation
Variance of OLS estimators in the presence of serial correlation 5:59
Variance of OLS estimators in the presence of serial correlation The Foundation
Gauss Markov conditions summary of problems of violation 4:16
Gauss Markov conditions summary of problems of violation The Foundation
Estimating the population variance from a sample - part one 6:55
Estimating the population variance from a sample - part one The Foundation
Estimating the population variance from a sample - part two 5:14
Estimating the population variance from a sample - part two The Foundation
Problem set 2 - OLS introduction - NBA players' wages 2:26
Problem set 2 - OLS introduction - NBA players' wages The Foundation
Hypothesis testing 6:56
Hypothesis testing The Foundation
Hypothesis testing - one and two tailed tests 4:57
Hypothesis testing - one and two tailed tests The Foundation
Hypothesis testing in linear regression part 2 8:03
Hypothesis testing in linear regression part 2 The Foundation
Hypothesis testing in linear regression part 3 6:17
Hypothesis testing in linear regression part 3 The Foundation
Hypothesis testing in linear regression part 4 8:24
Hypothesis testing in linear regression part 4 The Foundation
Hypothesis testing in linear regression part 5 5:40
Hypothesis testing in linear regression part 5 The Foundation
Tests for normally distributed errors 11:08
Tests for normally distributed errors The Foundation
Interpreting Regression Coefficients in Linear Regression 5:40
Interpreting Regression Coefficients in Linear Regression The Foundation
Interpreting regression coefficients in log models part 1 5:03
Interpreting regression coefficients in log models part 1 The Foundation
Normally distributed errors - finite sample inference 11:08
Normally distributed errors - finite sample inference The Foundation
The benefits of a log dependent variable 6:36
The benefits of a log dependent variable The Foundation
Interpreting regression coefficients in log models part 2 4:39
Interpreting regression coefficients in log models part 2 The Foundation
Dummy variables - an introduction 4:46
Dummy variables - an introduction The Foundation
Dummy variables - interaction terms explanation 4:35
Dummy variables - interaction terms explanation The Foundation
The F statistic - an introduction 10:14
The F statistic - an introduction The Foundation
F test - example 1 7:06
F test - example 1 The Foundation
F test - example 2 6:18
F test - example 2 The Foundation
F test - the similarity with the t test 4:38
F test - the similarity with the t test The Foundation
The F test - R Squared form 7:05
The F test - R Squared form The Foundation

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