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Levels vs differences regression - motivation for cointegrated regression
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Classroom Contents
Undergraduate Econometrics
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- 1 Undergraduate econometrics syllabus
- 2 What is econometrics?
- 3 Econometrics vs hard science
- 4 Natural experiments in econometrics
- 5 Populations and samples in econometrics
- 6 Estimators - the basics
- 7 Estimator properties
- 8 Unbiasedness and consistency
- 9 Unbiasedness vs consistency of estimators - an example
- 10 Efficiency of estimators
- 11 Good estimator properties summary
- 12 Lines of best fit in econometrics
- 13 The mathematics behind drawing a line of best fit
- 14 Least Squares Estimators as BLUE
- 15 Deriving Least Squares Estimators - part 1
- 16 Deriving Least Squares Estimators - part 2
- 17 Deriving Least Squares Estimators - part 3
- 18 Deriving Least Squares Estimators - part 4
- 19 Deriving Least Squares Estimators - part 5
- 20 Least Squares Estimators - in summary
- 21 Taking expectations of a random variable
- 22 Moments of a random variable
- 23 Central moments of a random variable
- 24 Kurtosis
- 25 Skewness
- 26 Expectations and Variance properties
- 27 Covariance and correlation
- 28 Population vs sample quantities
- 29 The Population Regression Function
- 30 Problem set 1 - estimators introduction
- 31 Gauss-Markov assumptions part 1
- 32 Gauss-Markov assumptions part 2
- 33 Zero conditional mean of errors - Gauss-Markov assumption
- 34 Omitted variable bias - example 1
- 35 Omitted variable bias - example 2
- 36 Omitted variable bias - example 3
- 37 Omitted variable bias - proof part 1
- 38 Omitted variable bias - proof part 2
- 39 Reverse Causality - part 1
- 40 Reverse Causality - part 2
- 41 Measurement error in independent variable - part 1
- 42 Measurement error in independent variable - part 2
- 43 Functional misspecification 1
- 44 Functional misspecification 2
- 45 Linearity in parameters - Gauss-Markov
- 46 Random sample summary - Gauss-Markov
- 47 Gauss-Markov - explanation of random sampling and serial correlation
- 48 Serial Correlation summary
- 49 Serial Correlation - as a symptom of omitted variable bias
- 50 Serial Correlation - as a symptom of functional misspecification
- 51 Serial Correlation - caused by measurement error
- 52 Serial correlation biased standard errors (advanced topic) - part 1
- 53 Serial correlation biased standard errors (advanced topic) - part 2
- 54 Heteroskedasticity summary
- 55 Heteroskedastic errors - example 1
- 56 Heteroskedasticity - example 2
- 57 Heteroskedasticity caused by data aggregation (advanced topic)
- 58 Perfect collinearity - example 1
- 59 Perfect collinearity - example 2
- 60 Multicollinearity
- 61 Index - where we currently are in the overall plan of econometrics
- 62 Gauss-Markov proof part 1 (advanced)
- 63 Gauss-Markov proof part 2 (advanced)
- 64 Gauss-Markov proof part 3 (advanced)
- 65 Gauss-Markov proof part 4 (advanced)
- 66 Gauss-Markov proof part 5 (advanced)
- 67 Gauss-Markov proof part 6 (advanced)
- 68 Errors in populations vs estimated errors
- 69 Sum of squares
- 70 R squared part 1
- 71 R squared part 2
- 72 Degrees of freedom part 1
- 73 Degrees of freedom part 2 (advanced)
- 74 Overfitting in econometrics
- 75 Adjusted R squared
- 76 Unbiasedness of OLS - part one
- 77 Unbiasedness of OLS - part two
- 78 Variance of OLS estimators - part one
- 79 Variance of OLS estimators - part two
- 80 Estimator for the population error variance
- 81 Estimated variance of OLS estimators - intuition behind maths
- 82 Variance of OLS estimators in the presence of heteroscedasticity
- 83 Variance of OLS estimators in the presence of serial correlation
- 84 Gauss Markov conditions summary of problems of violation
- 85 Estimating the population variance from a sample - part one
- 86 Estimating the population variance from a sample - part two
- 87 Problem set 2 - OLS introduction - NBA players' wages
- 88 Hypothesis testing
- 89 Hypothesis testing - one and two tailed tests
- 90 Central Limit Theorem
- 91 Hypothesis testing in linear regression part 1
- 92 Hypothesis testing in linear regression part 2
- 93 Hypothesis testing in linear regression part 3
- 94 Hypothesis testing in linear regression part 4
- 95 Hypothesis testing in linear regression part 5
- 96 Normally distributed errors - finite sample inference
- 97 Tests for normally distributed errors
- 98 Interpreting Regression Coefficients in Linear Regression
- 99 Interpreting regression coefficients in log models part 1
- 100 Interpreting regression coefficients in log models part 2
- 101 The benefits of a log dependent variable
- 102 Dummy variables - an introduction
- 103 Dummy variables - interaction terms explanation
- 104 Continuous variables - interaction term interpretation
- 105 The F statistic - an introduction
- 106 F test - example 1
- 107 F test - example 2
- 108 F test - the similarity with the t test
- 109 The F test - R Squared form
- 110 Testing hypothesis about linear combinations of parameters - part 1
- 111 Testing hypothesis about linear combinations of parameters - part 2
- 112 Testing hypothesis about linear combinations of parameters - part 3
- 113 Testing hypothesis about linear combinations of parameters - part 4
- 114 Confidence intervals
- 115 The Goldfeld-Quandt test for heteroscedasticity
- 116 The Breusch Pagan test for heteroscedasticity
- 117 The White test for heteroscedasticity
- 118 Serial correlation testing - introduction
- 119 Serial correlation - The Durbin-Watson test
- 120 Serial correlation testing - the Breusch-Godfrey test
- 121 Ramsey RESET test for functional misspecification
- 122 Gauss-Markov violations: summary of issues
- 123 Heteroscedasticity: as a symptom of omitted variable bias - part 1
- 124 Heteroscedasticity: as symptom of omitted variable bias - part 2
- 125 Serial correlation: a symptom of omitted variable bias
- 126 Heteroscedasticity: dealing with the problems caused
- 127 Problem set 3 - Presidential election data - hypothesis testing and model selection
- 128 Weighted Least Squares: an introduction
- 129 Weighted Least Squares: mathematical introduction
- 130 Weighted Least Squares: an example
- 131 Weighted Least Squares in practice - feasible GLS - part 1
- 132 Weighted Least Squares in practice - feasible GLS - part 2
- 133 How to address the issue of serial correlation
- 134 GLS estimation to correct for serial correlation
- 135 fGLS for serially correlated errors
- 136 Instrumental Variables - an introduction
- 137 Endogeneity and Instrumental Variables
- 138 Instrumental Variables intuition - part 1
- 139 Instrumental Variables intuition - part 2
- 140 Instrumental Variables example - returns to schooling
- 141 Instrumental Variables example - classroom size
- 142 Instrumental Variables estimation - colonial origins of economic development
- 143 Instrumental Variables as Two Stage Least Squares
- 144 Proof that Instrumental Variables estimators are Two Stage Least Squares
- 145 Bad instruments - part 1
- 146 Bad instruments - part 2
- 147 Bias of Instrumental Variables - part 1
- 148 Bias of Instrumental Variables - part 2
- 149 Bias of Instrumental Variables - intuition
- 150 Consistency of Instrumental Variables - intuition
- 151 Consistency - comparing Ordinary Least Squares with Instrumental Variables
- 152 Inference using Instrumental Variables estimators
- 153 Multiple regressor Instrumental Variables estimation
- 154 Two Stage Least Squares - an introduction
- 155 Two Stage Least Squares - example
- 156 Two Stage Least Squares - multiple endogenous explanatory variables
- 157 Testing for endogeneity
- 158 Testing for endogenous instruments - test for overidentifying restriction
- 159 Problem set 4 - the return to education - WLS and IV estimators
- 160 Time series vs cross sectional data
- 161 Time series Gauss Markov conditions
- 162 Strict exogeneity
- 163 Strict exogeneity assumption - intuition
- 164 Lagged dependent variable model - strict exogeneity
- 165 Asymptotic assumptions for time series least squares
- 166 Conditions for stationary and weakly dependent series
- 167 Stationary in mean
- 168 Spurious regression
- 169 Spurious regression
- 170 Variance stationary processes
- 171 Covariance stationary processes
- 172 Stationary series summary
- 173 Weakly dependent time series
- 174 An introduction to Moving Average Order One processes
- 175 Moving Average processes - Stationary and Weakly Dependent
- 176 Autoregressive Order one process introduction and example
- 177 Autoregressive order 1 process - conditions for stationary in mean
- 178 Autoregressive order 1 process - conditions for stationary in variance
- 179 Autoregressive order 1 process - conditions for Stationary Covariance and Weak Dependence
- 180 Autoregressive vs Moving Average Order One processes - part 1
- 181 Autoregressive vs Moving Average Order One processes - part 2
- 182 Partial vs total autocorrelation
- 183 A Random Walk - introduction and properties
- 184 The qualitative difference between stationary and non-stationary AR(1)
- 185 Random walk not weakly dependent
- 186 Random walk with drift
- 187 Deterministic vs stochastic trends
- 188 Dickey Fuller test for unit root
- 189 Augmented Dickey Fuller tests
- 190 Dickey fuller test with time trend
- 191 Highly persistent time series
- 192 Integrated order of processes
- 193 Cointegration - an introduction
- 194 Cointegration tests
- 195 Levels vs differences regression - motivation for cointegrated regression
- 196 Leads and lags estimator for inference in cointegrated models (advanced)
- 197 Lagged independent variables
- 198 Problem set 5 - an introduction to time series
- 199 Mean and median lag
