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