๐Ÿ“บ Ben Lambert - Econometrics ่ชฒ็จ‹ๆธ…ๅ–ฎ | ๐Ÿ“‹

ๅบ่™Ÿ ๅฝฑ็‰‡ๆจ™้กŒ ้•ทๅบฆ ๅฝฑ็‰‡็ถฒๅ€
1 Undergraduate econometrics syllabus 6ๅˆ†55็ง’ ่ง€็œ‹
2 What is econometrics? 7ๅˆ†45็ง’ ่ง€็œ‹
3 Econometrics vs hard science 7ๅˆ†11็ง’ ่ง€็œ‹
4 Natural experiments in econometrics 5ๅˆ†26็ง’ ่ง€็œ‹
5 Populations and samples in econometrics 5ๅˆ†49็ง’ ่ง€็œ‹
6 Estimators - the basics 3ๅˆ†4็ง’ ่ง€็œ‹
7 Estimator properties 5ๅˆ†21็ง’ ่ง€็œ‹
8 Unbiasedness and consistency 5ๅˆ†56็ง’ ่ง€็œ‹
9 Unbiasedness vs consistency of estimators - an example 4ๅˆ†8็ง’ ่ง€็œ‹
10 Efficiency of estimators 2ๅˆ†47็ง’ ่ง€็œ‹
11 Good estimator properties summary 2ๅˆ†12็ง’ ่ง€็œ‹
12 Lines of best fit in econometrics 6ๅˆ†31็ง’ ่ง€็œ‹
13 The mathematics behind drawing a line of best fit 5ๅˆ†25็ง’ ่ง€็œ‹
14 Least Squares Estimators as BLUE 7ๅˆ†19็ง’ ่ง€็œ‹
15 Deriving Least Squares Estimators - part 1 5ๅˆ†1็ง’ ่ง€็œ‹
16 Deriving Least Squares Estimators - part 2 6ๅˆ†6็ง’ ่ง€็œ‹
17 Deriving Least Squares Estimators - part 3 4ๅˆ†16็ง’ ่ง€็œ‹
18 Deriving Least Squares Estimators - part 4 3ๅˆ†16็ง’ ่ง€็œ‹
19 Deriving Least Squares Estimators - part 5 4ๅˆ†12็ง’ ่ง€็œ‹
20 Least Squares Estimators - in summary 4ๅˆ†52็ง’ ่ง€็œ‹
21 Taking expectations of a random variable 7ๅˆ†28็ง’ ่ง€็œ‹
22 Moments of a random variable 3ๅˆ†51็ง’ ่ง€็œ‹
23 Central moments of a random variable 4ๅˆ†16็ง’ ่ง€็œ‹
24 Kurtosis 5ๅˆ†20็ง’ ่ง€็œ‹
25 Skewness 4ๅˆ†57็ง’ ่ง€็œ‹
26 Expectations and Variance properties 5ๅˆ†18็ง’ ่ง€็œ‹
27 Covariance and correlation 5ๅˆ†56็ง’ ่ง€็œ‹
28 Population vs sample quantities 2ๅˆ†25็ง’ ่ง€็œ‹
29 The Population Regression Function 6ๅˆ†44็ง’ ่ง€็œ‹
30 Problem set 1 - estimators introduction 2ๅˆ†47็ง’ ่ง€็œ‹
31 Gauss-Markov assumptions part 1 5ๅˆ†21็ง’ ่ง€็œ‹
32 Gauss-Markov assumptions part 2 4ๅˆ†39็ง’ ่ง€็œ‹
33 Zero conditional mean of errors - Gauss-Markov assumption 2ๅˆ†57็ง’ ่ง€็œ‹
34 Omitted variable bias - example 1 4ๅˆ†47็ง’ ่ง€็œ‹
35 Omitted variable bias - example 2 5ๅˆ†30็ง’ ่ง€็œ‹
36 Omitted variable bias - example 3 3ๅˆ†36็ง’ ่ง€็œ‹
37 Omitted variable bias - proof part 1 4ๅˆ†1็ง’ ่ง€็œ‹
38 Omitted variable bias - proof part 2 6ๅˆ†8็ง’ ่ง€็œ‹
39 Reverse Causality - part 1 5ๅˆ†24็ง’ ่ง€็œ‹
40 Reverse Causality - part 2 4ๅˆ†27็ง’ ่ง€็œ‹
41 Measurement error in independent variable - part 1 5ๅˆ†26็ง’ ่ง€็œ‹
42 Measurement error in independent variable - part 2 4ๅˆ†7็ง’ ่ง€็œ‹
43 Functional misspecification 1 5ๅˆ†32็ง’ ่ง€็œ‹
44 Functional misspecification 2 6ๅˆ†25็ง’ ่ง€็œ‹
45 Linearity in parameters - Gauss-Markov 2ๅˆ†5็ง’ ่ง€็œ‹
46 Random sample summary - Gauss-Markov 3ๅˆ†57็ง’ ่ง€็œ‹
47 Gauss-Markov - explanation of random sampling and serial correlation 6ๅˆ†4็ง’ ่ง€็œ‹
48 Serial Correlation summary 5ๅˆ†49็ง’ ่ง€็œ‹
49 Serial Correlation - as a symptom of omitted variable bias 4ๅˆ†44็ง’ ่ง€็œ‹
50 Serial Correlation - as a symptom of functional misspecification 3ๅˆ†25็ง’ ่ง€็œ‹
51 Serial Correlation - caused by measurement error 2ๅˆ†32็ง’ ่ง€็œ‹
52 Serial correlation biased standard errors (advanced topic) - part 1 3ๅˆ†54็ง’ ่ง€็œ‹
53 Serial correlation biased standard errors (advanced topic) - part 2 4ๅˆ†28็ง’ ่ง€็œ‹
54 Heteroskedasticity summary 4ๅˆ†5็ง’ ่ง€็œ‹
55 Heteroskedastic errors - example 1 4ๅˆ†30็ง’ ่ง€็œ‹
56 Heteroskedasticity - example 2 4ๅˆ†18็ง’ ่ง€็œ‹
57 Heteroskedasticity caused by data aggregation (advanced topic) 6ๅˆ†37็ง’ ่ง€็œ‹
58 Perfect collinearity - example 1 3ๅˆ†41็ง’ ่ง€็œ‹
59 Perfect collinearity - example 2 3ๅˆ†22็ง’ ่ง€็œ‹
60 Multicollinearity 5ๅˆ†17็ง’ ่ง€็œ‹
61 Index - where we currently are in the overall plan of econometrics 3ๅˆ†2็ง’ ่ง€็œ‹
62 Gauss-Markov proof part 1 (advanced) 4ๅˆ†2็ง’ ่ง€็œ‹
63 Gauss-Markov proof part 2 (advanced) 7ๅˆ†4็ง’ ่ง€็œ‹
64 Gauss-Markov proof part 3 (advanced) 5ๅˆ†4็ง’ ่ง€็œ‹
65 Gauss-Markov proof part 4 (advanced) 4ๅˆ†26็ง’ ่ง€็œ‹
66 Gauss-Markov proof part 5 (advanced) 5ๅˆ†11็ง’ ่ง€็œ‹
67 Gauss-Markov proof part 6 (advanced) 3ๅˆ†44็ง’ ่ง€็œ‹
68 Errors in populations vs estimated errors 4ๅˆ†3็ง’ ่ง€็œ‹
69 Sum of squares 4ๅˆ†7็ง’ ่ง€็œ‹
70 R squared part 1 4ๅˆ†45็ง’ ่ง€็œ‹
71 R squared part 2 6ๅˆ†22็ง’ ่ง€็œ‹
72 Degrees of freedom part 1 3ๅˆ†30็ง’ ่ง€็œ‹
73 Degrees of freedom part 2 (advanced) 6ๅˆ†1็ง’ ่ง€็œ‹
74 Overfitting in econometrics 5ๅˆ†14็ง’ ่ง€็œ‹
75 Adjusted R squared 4ๅˆ†53็ง’ ่ง€็œ‹
76 Unbiasedness of OLS - part one 4ๅˆ†48็ง’ ่ง€็œ‹
77 Unbiasedness of OLS - part two 5ๅˆ†46็ง’ ่ง€็œ‹
78 Variance of OLS estimators - part one 7ๅˆ†9็ง’ ่ง€็œ‹
79 Variance of OLS estimators - part two 3ๅˆ†8็ง’ ่ง€็œ‹
80 Estimator for the population error variance 5ๅˆ†17็ง’ ่ง€็œ‹
81 Estimated variance of OLS estimators - intuition behind maths 3ๅˆ†51็ง’ ่ง€็œ‹
82 Variance of OLS estimators in the presence of heteroscedasticity 4ๅˆ†5็ง’ ่ง€็œ‹
83 Variance of OLS estimators in the presence of serial correlation 6ๅˆ†0็ง’ ่ง€็œ‹
84 Gauss Markov conditions summary of problems of violation 4ๅˆ†17็ง’ ่ง€็œ‹
85 Estimating the population variance from a sample - part one 6ๅˆ†55็ง’ ่ง€็œ‹
86 Estimating the population variance from a sample - part two 5ๅˆ†15็ง’ ่ง€็œ‹
87 Problem set 2 - OLS introduction - NBA players' wages 2ๅˆ†27็ง’ ่ง€็œ‹
88 Hypothesis testing 6ๅˆ†56็ง’ ่ง€็œ‹
89 Hypothesis testing - one and two tailed tests 4ๅˆ†58็ง’ ่ง€็œ‹
90 Central Limit Theorem 7ๅˆ†9็ง’ ่ง€็œ‹
91 Hypothesis testing in linear regression part 1 8ๅˆ†42็ง’ ่ง€็œ‹
92 Hypothesis testing in linear regression part 2 8ๅˆ†4็ง’ ่ง€็œ‹
93 Hypothesis testing in linear regression part 3 6ๅˆ†18็ง’ ่ง€็œ‹
94 Hypothesis testing in linear regression part 4 8ๅˆ†24็ง’ ่ง€็œ‹
95 Hypothesis testing in linear regression part 5 5ๅˆ†40็ง’ ่ง€็œ‹
96 Normally distributed errors - finite sample inference 11ๅˆ†8็ง’ ่ง€็œ‹
97 Tests for normally distributed errors 6ๅˆ†1็ง’ ่ง€็œ‹
98 Interpreting Regression Coefficients in Linear Regression 5ๅˆ†40็ง’ ่ง€็œ‹
99 Interpreting regression coefficients in log models part 1 5ๅˆ†4็ง’ ่ง€็œ‹
100 Interpreting regression coefficients in log models part 2 4ๅˆ†40็ง’ ่ง€็œ‹
101 The benefits of a log dependent variable 6ๅˆ†36็ง’ ่ง€็œ‹
102 Dummy variables - an introduction 4ๅˆ†47็ง’ ่ง€็œ‹
103 Dummy variables - interaction terms explanation 4ๅˆ†36็ง’ ่ง€็œ‹
104 Continuous variables - interaction term interpretation 4ๅˆ†54็ง’ ่ง€็œ‹
105 The F statistic - an introduction 10ๅˆ†14็ง’ ่ง€็œ‹
106 F test - example 1 7ๅˆ†6็ง’ ่ง€็œ‹
107 F test - example 2 6ๅˆ†19็ง’ ่ง€็œ‹
108 F test - the similarity with the t test 4ๅˆ†39็ง’ ่ง€็œ‹
109 The F test - R Squared form 7ๅˆ†6็ง’ ่ง€็œ‹
110 Testing hypothesis about linear combinations of parameters - part 1 4ๅˆ†59็ง’ ่ง€็œ‹
111 Testing hypothesis about linear combinations of parameters - part 2 4ๅˆ†44็ง’ ่ง€็œ‹
112 Testing hypothesis about linear combinations of parameters - part 3 4ๅˆ†56็ง’ ่ง€็œ‹
113 Testing hypothesis about linear combinations of parameters - part 4 6ๅˆ†15็ง’ ่ง€็œ‹
114 Confidence intervals 4ๅˆ†32็ง’ ่ง€็œ‹
115 The Goldfeld-Quandt test for heteroscedasticity 9ๅˆ†44็ง’ ่ง€็œ‹
116 The Breusch Pagan test for heteroscedasticity 9ๅˆ†31็ง’ ่ง€็œ‹
117 The White test for heteroscedasticity 7ๅˆ†39็ง’ ่ง€็œ‹
118 Serial correlation testing - introduction 5ๅˆ†9็ง’ ่ง€็œ‹
119 Serial correlation - The Durbin-Watson test 6ๅˆ†18็ง’ ่ง€็œ‹
120 Serial correlation testing - the Breusch-Godfrey test 8ๅˆ†3็ง’ ่ง€็œ‹
121 Ramsey RESET test for functional misspecification 7ๅˆ†24็ง’ ่ง€็œ‹
122 Gauss-Markov violations: summary of issues 12ๅˆ†0็ง’ ่ง€็œ‹
123 Heteroscedasticity: as a symptom of omitted variable bias - part 1 12ๅˆ†28็ง’ ่ง€็œ‹
124 Heteroscedasticity: as symptom of omitted variable bias - part 2 5ๅˆ†25็ง’ ่ง€็œ‹
125 Serial correlation: a symptom of omitted variable bias 5ๅˆ†50็ง’ ่ง€็œ‹
126 Heteroscedasticity: dealing with the problems caused 8ๅˆ†55็ง’ ่ง€็œ‹
127 Problem set 3 - Presidential election data - hypothesis testing and model selection 3ๅˆ†19็ง’ ่ง€็œ‹
128 Weighted Least Squares: an introduction 9ๅˆ†42็ง’ ่ง€็œ‹
129 Weighted Least Squares: mathematical introduction 6ๅˆ†33็ง’ ่ง€็œ‹
130 Weighted Least Squares: an example 5ๅˆ†37็ง’ ่ง€็œ‹
131 Weighted Least Squares in practice - feasible GLS - part 1 5ๅˆ†29็ง’ ่ง€็œ‹
132 Weighted Least Squares in practice - feasible GLS - part 2 4ๅˆ†45็ง’ ่ง€็œ‹
133 How to address the issue of serial correlation 3ๅˆ†35็ง’ ่ง€็œ‹
134 GLS estimation to correct for serial correlation 4ๅˆ†56็ง’ ่ง€็œ‹
135 fGLS for serially correlated errors 5ๅˆ†32็ง’ ่ง€็œ‹
136 Instrumental Variables - an introduction 13ๅˆ†35็ง’ ่ง€็œ‹
137 Endogeneity and Instrumental Variables 6ๅˆ†29็ง’ ่ง€็œ‹
138 Instrumental Variables intuition - part 1 6ๅˆ†0็ง’ ่ง€็œ‹
139 Instrumental Variables intuition - part 2 4ๅˆ†32็ง’ ่ง€็œ‹
140 Instrumental Variables example - returns to schooling 8ๅˆ†23็ง’ ่ง€็œ‹
141 Instrumental Variables example - classroom size 4ๅˆ†17็ง’ ่ง€็œ‹
142 Instrumental Variables estimation - colonial origins of economic development 7ๅˆ†49็ง’ ่ง€็œ‹
143 Instrumental Variables as Two Stage Least Squares 6ๅˆ†42็ง’ ่ง€็œ‹
144 Proof that Instrumental Variables estimators are Two Stage Least Squares 4ๅˆ†40็ง’ ่ง€็œ‹
145 Bad instruments - part 1 6ๅˆ†7็ง’ ่ง€็œ‹
146 Bad instruments - part 2 5ๅˆ†44็ง’ ่ง€็œ‹
147 Bias of Instrumental Variables - part 1 6ๅˆ†9็ง’ ่ง€็œ‹
148 Bias of Instrumental Variables - part 2 3ๅˆ†36็ง’ ่ง€็œ‹
149 Bias of Instrumental Variables - intuition 4ๅˆ†41็ง’ ่ง€็œ‹
150 Consistency of Instrumental Variables - intuition 4ๅˆ†56็ง’ ่ง€็œ‹
151 Consistency - comparing Ordinary Least Squares with Instrumental Variables 5ๅˆ†46็ง’ ่ง€็œ‹
152 Inference using Instrumental Variables estimators 5ๅˆ†45็ง’ ่ง€็œ‹
153 Multiple regressor Instrumental Variables estimation 5ๅˆ†28็ง’ ่ง€็œ‹
154 Two Stage Least Squares - an introduction 8ๅˆ†24็ง’ ่ง€็œ‹
155 Two Stage Least Squares - example 7ๅˆ†29็ง’ ่ง€็œ‹
156 Two Stage Least Squares - multiple endogenous explanatory variables 5ๅˆ†16็ง’ ่ง€็œ‹
157 Testing for endogeneity 7ๅˆ†31็ง’ ่ง€็œ‹
158 Testing for endogenous instruments - test for overidentifying restriction 8ๅˆ†14็ง’ ่ง€็œ‹
159 Problem set 4 - the return to education - WLS and IV estimators 3ๅˆ†0็ง’ ่ง€็œ‹
160 Time series vs cross sectional data 3ๅˆ†55็ง’ ่ง€็œ‹
161 Time series Gauss Markov conditions 4ๅˆ†36็ง’ ่ง€็œ‹
162 Strict exogeneity 5ๅˆ†25็ง’ ่ง€็œ‹
163 Strict exogeneity assumption - intuition 4ๅˆ†52็ง’ ่ง€็œ‹
164 Lagged dependent variable model - strict exogeneity 3ๅˆ†34็ง’ ่ง€็œ‹
165 Asymptotic assumptions for time series least squares 5ๅˆ†56็ง’ ่ง€็œ‹
166 Conditions for stationary and weakly dependent series 4ๅˆ†36็ง’ ่ง€็œ‹
167 Stationary in mean 5ๅˆ†3็ง’ ่ง€็œ‹
168 Spurious regression 5ๅˆ†27็ง’ ่ง€็œ‹
169 Spurious regression 3ๅˆ†24็ง’ ่ง€็œ‹
170 Variance stationary processes 4ๅˆ†10็ง’ ่ง€็œ‹
171 Covariance stationary processes 5ๅˆ†31็ง’ ่ง€็œ‹
172 Stationary series summary 4ๅˆ†7็ง’ ่ง€็œ‹
173 Weakly dependent time series 7ๅˆ†17็ง’ ่ง€็œ‹
174 An introduction to Moving Average Order One processes 8ๅˆ†8็ง’ ่ง€็œ‹
175 Moving Average processes - Stationary and Weakly Dependent 7ๅˆ†7็ง’ ่ง€็œ‹
176 Autoregressive Order one process introduction and example 5ๅˆ†10็ง’ ่ง€็œ‹
177 Autoregressive order 1 process - conditions for stationary in mean 3ๅˆ†48็ง’ ่ง€็œ‹
178 Autoregressive order 1 process - conditions for stationary in variance 3ๅˆ†12็ง’ ่ง€็œ‹
179 Autoregressive order 1 process - conditions for Stationary Covariance and Weak Dependence 5ๅˆ†49็ง’ ่ง€็œ‹
180 Autoregressive vs Moving Average Order One processes - part 1 3ๅˆ†49็ง’ ่ง€็œ‹
181 Autoregressive vs Moving Average Order One processes - part 2 4ๅˆ†28็ง’ ่ง€็œ‹
182 Partial vs total autocorrelation 6ๅˆ†19็ง’ ่ง€็œ‹
183 A Random Walk - introduction and properties 6ๅˆ†1็ง’ ่ง€็œ‹
184 The qualitative difference between stationary and non-stationary AR(1) 7ๅˆ†56็ง’ ่ง€็œ‹
185 Random walk not weakly dependent 2ๅˆ†59็ง’ ่ง€็œ‹
186 Random walk with drift 5ๅˆ†3็ง’ ่ง€็œ‹
187 Deterministic vs stochastic trends 5ๅˆ†7็ง’ ่ง€็œ‹
188 Dickey Fuller test for unit root 5ๅˆ†49็ง’ ่ง€็œ‹
189 Augmented Dickey Fuller tests 5ๅˆ†0็ง’ ่ง€็œ‹
190 Dickey fuller test with time trend 4ๅˆ†50็ง’ ่ง€็œ‹
191 Highly persistent time series 6ๅˆ†12็ง’ ่ง€็œ‹
192 Integrated order of processes 4ๅˆ†5็ง’ ่ง€็œ‹
193 Cointegration - an introduction 6ๅˆ†11็ง’ ่ง€็œ‹
194 Cointegration tests 6ๅˆ†28็ง’ ่ง€็œ‹
195 Levels vs differences regression - motivation for cointegrated regression 6ๅˆ†23็ง’ ่ง€็œ‹
196 Leads and lags estimator for inference in cointegrated models (advanced) 7ๅˆ†42็ง’ ่ง€็œ‹
197 Lagged independent variables 6ๅˆ†22็ง’ ่ง€็œ‹
198 Problem set 5 - an introduction to time series 2ๅˆ†27็ง’ ่ง€็œ‹
199 Mean and median lag 6ๅˆ†44็ง’ ่ง€็œ‹
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