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 syllabus06:55
- What is econometrics?07:46
- Econometrics vs hard science07:11
- Natural experiments in econometrics05:26
- Populations and samples in econometrics05:49
- Estimators - the basics03:04
- Estimator properties05:22
- Unbiasedness and consistency05:57
- Unbiasedness vs consistency of estimators - an example04:09
- Efficiency of estimators02:47
- Good estimator properties summary02:13
- Lines of best fit in econometrics06:32
- The mathematics behind drawing a line of best fit05:26
- Least Squares Estimators as BLUE07:19
- Deriving Least Squares Estimators - part 105:02
- Deriving Least Squares Estimators - part 206:07
- Deriving Least Squares Estimators - part 304:16
- Deriving Least Squares Estimators - part 403:16
- Deriving Least Squares Estimators - part 504:13
- Least Squares Estimators - in summary04:52
- Taking expectations of a random variable07:28
- Moments of a random variable03:52
- Central moments of a random variable04:17
- Kurtosis05:21
- Skewness04:57
- Expectations and Variance properties05:19
- Covariance and correlation05:56
- Population vs sample quantities02:25
- The Population Regression Function06:44
- Problem set 1 - estimators introduction02:48
- Gauss-Markov assumptions part 105:22
- Gauss-Markov assumptions part 204:40
- Zero conditional mean of errors - Gauss-Markov assumption02:57
- Omitted variable bias - example 104:47
- Omitted variable bias - example 205:30
- Omitted variable bias - example 303:36
- Omitted variable bias - proof part 104:01
- Omitted variable bias - proof part 206:09
- Reverse Causality - part 105:24
- Reverse Causality - part 204:27
- Measurement error in independent variable - part 105:27
- Measurement error in independent variable - part 204:08
- Functional misspecification 105:33
- Functional misspecification 206:25
- Linearity in parameters - Gauss-Markov02:06
- Random sample summary - Gauss-Markov03:57
- Gauss-Markov - explanation of random sampling and serial correlation06:05
- Serial Correlation summary05:49
- Serial Correlation - as a symptom of omitted variable bias04:44
- Serial Correlation - as a symptom of functional misspecification03:25
- Serial Correlation - caused by measurement error02:33
- Serial correlation biased standard errors (advanced topic) - part 103:55
- Serial correlation biased standard errors (advanced topic) - part 204:28
- Heteroskedasticity summary04:06
- Heteroskedastic errors - example 104:30
- Heteroskedasticity - example 204:19
- Heteroskedasticity caused by data aggregation (advanced topic)06:37
- Perfect collinearity - example 103:41
- Perfect collinearity - example 203:23
- Multicollinearity05:17
- Index - where we currently are in the overall plan of econometrics03:03
- 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 errors04:03
- Sum of squares04:08
- R squared part 104:45
- R squared part 206:23
- Degrees of freedom part 103:30
- Degrees of freedom part 2 (advanced)06:01
- Overfitting in econometrics05:14
- Adjusted R squared04:53
- Unbiasedness of OLS - part one04:48
- Unbiasedness of OLS - part two05:46
- Variance of OLS estimators - part one07:10
- Variance of OLS estimators - part two03:08
- Estimator for the population error variance05:18
- Estimated variance of OLS estimators - intuition behind maths03:52
- Variance of OLS estimators in the presence of heteroscedasticity04:06
- Variance of OLS estimators in the presence of serial correlation06:00
- Gauss Markov conditions summary of problems of violation04:17
- Estimating the population variance from a sample - part one06:56
- Estimating the population variance from a sample - part two05:15
- Problem set 2 - OLS introduction - NBA players' wages02:27
- Hypothesis testing06:57
- Hypothesis testing - one and two tailed tests04:58
- Central Limit Theorem07:09
- Hypothesis testing in linear regression part 108:43
- Hypothesis testing in linear regression part 208:04
- Hypothesis testing in linear regression part 306:18
- Hypothesis testing in linear regression part 408:24
- Hypothesis testing in linear regression part 505:41
- Normally distributed errors - finite sample inference11:09
- Tests for normally distributed errors06:02
- Interpreting Regression Coefficients in Linear Regression05:41
- Interpreting regression coefficients in log models part 105:04
- Interpreting regression coefficients in log models part 204:40
- The benefits of a log dependent variable06:37
- Dummy variables - an introduction04:47
- Dummy variables - interaction terms explanation04:36
- Continuous variables - interaction term interpretation04:54
- The F statistic - an introduction10:15
- F test - example 107:07
- F test - example 206:19
- F test - the similarity with the t test04:39
- The F test - R Squared form07:06
- Testing hypothesis about linear combinations of parameters - part 105:00
- Testing hypothesis about linear combinations of parameters - part 204:45
- Testing hypothesis about linear combinations of parameters - part 304:57
- Testing hypothesis about linear combinations of parameters - part 406:15
- Confidence intervals04:32
- The Goldfeld-Quandt test for heteroscedasticity09:44
- The Breusch Pagan test for heteroscedasticity09:31
- The White test for heteroscedasticity07:40
- Serial correlation testing - introduction05:09
- Serial correlation - The Durbin-Watson test06:18
- Serial correlation testing - the Breusch-Godfrey test08:03
- Ramsey RESET test for functional misspecification07:25
- Gauss-Markov violations: summary of issues12:01
- Heteroscedasticity: as a symptom of omitted variable bias - part 112:29
- Heteroscedasticity: as symptom of omitted variable bias - part 205:25
- Serial correlation: a symptom of omitted variable bias05:51
- Heteroscedasticity: dealing with the problems caused08:56
- Problem set 3 - Presidential election data - hypothesis testing and model selection03:19
- Weighted Least Squares: an introduction09:42
- Weighted Least Squares: mathematical introduction06:33
- Weighted Least Squares: an example05:37
- Weighted Least Squares in practice - feasible GLS - part 105:30
- Weighted Least Squares in practice - feasible GLS - part 204:46
- How to address the issue of serial correlation03:36
- GLS estimation to correct for serial correlation04:56
- fGLS for serially correlated errors05:33
- Instrumental Variables - an introduction13:35
- Endogeneity and Instrumental Variables06:30
- Instrumental Variables intuition - part 106:00
- Instrumental Variables intuition - part 204:33
- Instrumental Variables example - returns to schooling08:24
- Instrumental Variables example - classroom size04:17
- Instrumental Variables estimation - colonial origins of economic development07:50
- Instrumental Variables as Two Stage Least Squares06:42
- Proof that Instrumental Variables estimators are Two Stage Least Squares04:40
- Bad instruments - part 106:07
- Bad instruments - part 205:45
- Bias of Instrumental Variables - part 106:09
- Bias of Instrumental Variables - part 203:37
- Bias of Instrumental Variables - intuition04:41
- Consistency of Instrumental Variables - intuition04:56
- Consistency - comparing Ordinary Least Squares with Instrumental Variables05:46
- Inference using Instrumental Variables estimators05:45
- Multiple regressor Instrumental Variables estimation05:28
- Two Stage Least Squares - an introduction08:25
- Two Stage Least Squares - example07:29
- Two Stage Least Squares - multiple endogenous explanatory variables05:16
- Testing for endogeneity07:31
- Testing for endogenous instruments - test for overidentifying restriction08:14
- Problem set 4 - the return to education - WLS and IV estimators03:01
- Time series vs cross sectional data03:56
- Time series Gauss Markov conditions04:36
- Strict exogeneity05:25
- Strict exogeneity assumption - intuition04:52
- Lagged dependent variable model - strict exogeneity03:34
- Asymptotic assumptions for time series least squares05:56
- Conditions for stationary and weakly dependent series04:37
- Stationary in mean05:03
- Spurious regression05:27
- Spurious regression03:25
- Variance stationary processes04:11
- Covariance stationary processes05:31
- Stationary series summary04:08
- Weakly dependent time series07:17
- An introduction to Moving Average Order One processes08:08
- Moving Average processes - Stationary and Weakly Dependent07:08
- Autoregressive Order one process introduction and example05:11
- Autoregressive order 1 process - conditions for stationary in mean03:49
- Autoregressive order 1 process - conditions for stationary in variance03:12
- Autoregressive order 1 process - conditions for Stationary Covariance and Weak Dependence05:49
- Autoregressive vs Moving Average Order One processes - part 103:49
- Autoregressive vs Moving Average Order One processes - part 204:29
- Partial vs total autocorrelation06:20
- A Random Walk - introduction and properties06:01
- The qualitative difference between stationary and non-stationary AR(1)07:57
- Random walk not weakly dependent03:00
- Random walk with drift05:04
- Deterministic vs stochastic trends05:07
- Dickey Fuller test for unit root05:49
- Augmented Dickey Fuller tests05:01
- Dickey fuller test with time trend04:51
- Highly persistent time series06:13
- Integrated order of processes04:05
- Cointegration - an introduction06:11
- Cointegration tests06:29
- Levels vs differences regression - motivation for cointegrated regression06:23
- Leads and lags estimator for inference in cointegrated models (advanced)07:42
- Lagged independent variables06:22
- Problem set 5 - an introduction to time series02:27
- Mean and median lag06:44
