A full course in econometrics - undergraduate level - part 1
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1 modules • 199 lessons • 18.3 hours of video
A full course in econometrics - undergraduate level - part 1
199 lessons
• 18.3 hours
A full course in econometrics - undergraduate level - part 1
199 lessons
• 18.3 hours
- Undergraduate econometrics syllabus 06:55
- What is econometrics? 07:46
- Econometrics vs hard science 07:11
- Natural experiments in econometrics 05:26
- Populations and samples in econometrics 05:49
- Estimators - the basics 03:04
- Estimator properties 05:22
- Unbiasedness and consistency 05:57
- Unbiasedness vs consistency of estimators - an example 04:09
- Efficiency of estimators 02:47
- Good estimator properties summary 02:13
- Lines of best fit in econometrics 06:32
- The mathematics behind drawing a line of best fit 05:26
- Least Squares Estimators as BLUE 07:19
- Deriving Least Squares Estimators - part 1 05:02
- Deriving Least Squares Estimators - part 2 06:07
- Deriving Least Squares Estimators - part 3 04:16
- Deriving Least Squares Estimators - part 4 03:16
- Deriving Least Squares Estimators - part 5 04:13
- Least Squares Estimators - in summary 04:52
- Taking expectations of a random variable 07:28
- Moments of a random variable 03:52
- Central moments of a random variable 04:17
- Kurtosis 05:21
- Skewness 04:57
- Expectations and Variance properties 05:19
- Covariance and correlation 05:56
- Population vs sample quantities 02:25
- The Population Regression Function 06:44
- Problem set 1 - estimators introduction 02:48
- Gauss-Markov assumptions part 1 05:22
- Gauss-Markov assumptions part 2 04:40
- Zero conditional mean of errors - Gauss-Markov assumption 02:57
- Omitted variable bias - example 1 04:47
- Omitted variable bias - example 2 05:30
- Omitted variable bias - example 3 03:36
- Omitted variable bias - proof part 1 04:01
- Omitted variable bias - proof part 2 06:09
- Reverse Causality - part 1 05:24
- Reverse Causality - part 2 04:27
- Measurement error in independent variable - part 1 05:27
- Measurement error in independent variable - part 2 04:08
- Functional misspecification 1 05:33
- Functional misspecification 2 06:25
- Linearity in parameters - Gauss-Markov 02:06
- Random sample summary - Gauss-Markov 03:57
- Gauss-Markov - explanation of random sampling and serial correlation 06:05
- Serial Correlation summary 05:49
- Serial Correlation - as a symptom of omitted variable bias 04:44
- Serial Correlation - as a symptom of functional misspecification 03:25
- Serial Correlation - caused by measurement error 02:33
- Serial correlation biased standard errors (advanced topic) - part 1 03:55
- Serial correlation biased standard errors (advanced topic) - part 2 04:28
- Heteroskedasticity summary 04:06
- Heteroskedastic errors - example 1 04:30
- Heteroskedasticity - example 2 04:19
- Heteroskedasticity caused by data aggregation (advanced topic) 06:37
- Perfect collinearity - example 1 03:41
- Perfect collinearity - example 2 03:23
- Multicollinearity 05:17
- Index - where we currently are in the overall plan of econometrics 03:03
- Gauss-Markov proof part 1 (advanced) 04:02
- Gauss-Markov proof part 2 (advanced) 07:04
- Gauss-Markov proof part 3 (advanced) 05:05
- Gauss-Markov proof part 4 (advanced) 04:27
- Gauss-Markov proof part 5 (advanced) 05:11
- Gauss-Markov proof part 6 (advanced) 03:44
- Errors in populations vs estimated errors 04:03
- Sum of squares 04:08
- R squared part 1 04:45
- R squared part 2 06:23
- Degrees of freedom part 1 03:30
- Degrees of freedom part 2 (advanced) 06:01
- Overfitting in econometrics 05:14
- Adjusted R squared 04:53
- Unbiasedness of OLS - part one 04:48
- Unbiasedness of OLS - part two 05:46
- Variance of OLS estimators - part one 07:10
- Variance of OLS estimators - part two 03:08
- Estimator for the population error variance 05:18
- Estimated variance of OLS estimators - intuition behind maths 03:52
- Variance of OLS estimators in the presence of heteroscedasticity 04:06
- Variance of OLS estimators in the presence of serial correlation 06:00
- Gauss Markov conditions summary of problems of violation 04:17
- Estimating the population variance from a sample - part one 06:56
- Estimating the population variance from a sample - part two 05:15
- Problem set 2 - OLS introduction - NBA players' wages 02:27
- Hypothesis testing 06:57
- Hypothesis testing - one and two tailed tests 04:58
- Central Limit Theorem 07:09
- Hypothesis testing in linear regression part 1 08:43
- Hypothesis testing in linear regression part 2 08:04
- Hypothesis testing in linear regression part 3 06:18
- Hypothesis testing in linear regression part 4 08:24
- Hypothesis testing in linear regression part 5 05:41
- Normally distributed errors - finite sample inference 11:09
- Tests for normally distributed errors 06:02
- Interpreting Regression Coefficients in Linear Regression 05:41
- Interpreting regression coefficients in log models part 1 05:04
- Interpreting regression coefficients in log models part 2 04:40
- The benefits of a log dependent variable 06:37
- Dummy variables - an introduction 04:47
- Dummy variables - interaction terms explanation 04:36
- Continuous variables - interaction term interpretation 04:54
- The F statistic - an introduction 10:15
- F test - example 1 07:07
- F test - example 2 06:19
- F test - the similarity with the t test 04:39
- The F test - R Squared form 07:06
- Testing hypothesis about linear combinations of parameters - part 1 05:00
- Testing hypothesis about linear combinations of parameters - part 2 04:45
- Testing hypothesis about linear combinations of parameters - part 3 04:57
- Testing hypothesis about linear combinations of parameters - part 4 06:15
- Confidence intervals 04:32
- The Goldfeld-Quandt test for heteroscedasticity 09:44
- The Breusch Pagan test for heteroscedasticity 09:31
- The White test for heteroscedasticity 07:40
- Serial correlation testing - introduction 05:09
- Serial correlation - The Durbin-Watson test 06:18
- Serial correlation testing - the Breusch-Godfrey test 08:03
- Ramsey RESET test for functional misspecification 07:25
- Gauss-Markov violations: summary of issues 12:01
- Heteroscedasticity: as a symptom of omitted variable bias - part 1 12:29
- Heteroscedasticity: as symptom of omitted variable bias - part 2 05:25
- Serial correlation: a symptom of omitted variable bias 05:51
- Heteroscedasticity: dealing with the problems caused 08:56
- Problem set 3 - Presidential election data - hypothesis testing and model selection 03:19
- Weighted Least Squares: an introduction 09:42
- Weighted Least Squares: mathematical introduction 06:33
- Weighted Least Squares: an example 05:37
- Weighted Least Squares in practice - feasible GLS - part 1 05:30
- Weighted Least Squares in practice - feasible GLS - part 2 04:46
- How to address the issue of serial correlation 03:36
- GLS estimation to correct for serial correlation 04:56
- fGLS for serially correlated errors 05:33
- Instrumental Variables - an introduction 13:35
- Endogeneity and Instrumental Variables 06:30
- Instrumental Variables intuition - part 1 06:00
- Instrumental Variables intuition - part 2 04:33
- Instrumental Variables example - returns to schooling 08:24
- Instrumental Variables example - classroom size 04:17
- Instrumental Variables estimation - colonial origins of economic development 07:50
- Instrumental Variables as Two Stage Least Squares 06:42
- Proof that Instrumental Variables estimators are Two Stage Least Squares 04:40
- Bad instruments - part 1 06:07
- Bad instruments - part 2 05:45
- Bias of Instrumental Variables - part 1 06:09
- Bias of Instrumental Variables - part 2 03:37
- Bias of Instrumental Variables - intuition 04:41
- Consistency of Instrumental Variables - intuition 04:56
- Consistency - comparing Ordinary Least Squares with Instrumental Variables 05:46
- Inference using Instrumental Variables estimators 05:45
- Multiple regressor Instrumental Variables estimation 05:28
- Two Stage Least Squares - an introduction 08:25
- Two Stage Least Squares - example 07:29
- Two Stage Least Squares - multiple endogenous explanatory variables 05:16
- Testing for endogeneity 07:31
- Testing for endogenous instruments - test for overidentifying restriction 08:14
- Problem set 4 - the return to education - WLS and IV estimators 03:01
- Time series vs cross sectional data 03:56
- Time series Gauss Markov conditions 04:36
- Strict exogeneity 05:25
- Strict exogeneity assumption - intuition 04:52
- Lagged dependent variable model - strict exogeneity 03:34
- Asymptotic assumptions for time series least squares 05:56
- Conditions for stationary and weakly dependent series 04:37
- Stationary in mean 05:03
- Spurious regression 05:27
- Spurious regression 03:25
- Variance stationary processes 04:11
- Covariance stationary processes 05:31
- Stationary series summary 04:08
- Weakly dependent time series 07:17
- An introduction to Moving Average Order One processes 08:08
- Moving Average processes - Stationary and Weakly Dependent 07:08
- Autoregressive Order one process introduction and example 05:11
- Autoregressive order 1 process - conditions for stationary in mean 03:49
- Autoregressive order 1 process - conditions for stationary in variance 03:12
- Autoregressive order 1 process - conditions for Stationary Covariance and Weak Dependence 05:49
- Autoregressive vs Moving Average Order One processes - part 1 03:49
- Autoregressive vs Moving Average Order One processes - part 2 04:29
- Partial vs total autocorrelation 06:20
- A Random Walk - introduction and properties 06:01
- The qualitative difference between stationary and non-stationary AR(1) 07:57
- Random walk not weakly dependent 03:00
- Random walk with drift 05:04
- Deterministic vs stochastic trends 05:07
- Dickey Fuller test for unit root 05:49
- Augmented Dickey Fuller tests 05:01
- Dickey fuller test with time trend 04:51
- Highly persistent time series 06:13
- Integrated order of processes 04:05
- Cointegration - an introduction 06:11
- Cointegration tests 06:29
- Levels vs differences regression - motivation for cointegrated regression 06:23
- Leads and lags estimator for inference in cointegrated models (advanced) 07:42
- Lagged independent variables 06:22
- Problem set 5 - an introduction to time series 02:27
- Mean and median lag 06:44
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