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1 - Introduction to the matrix formulation of econometrics
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2 - Matrix formulation of econometrics - example
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3 - How to differentiate with respect to a vector - part 1
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4 - How to differentiate with respect to a vector - part 2
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5 - How to differentiate with respect to a vector - part 3
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6 - Ordinary Least Squares Estimators - derivation in matrix form - part 1
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7 - Ordinary Least Squares Estimators - derivation in matrix form - part 2
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8 - Ordinary Least Squares Estimators - derivation in matrix form - part 3
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9 - Expectations and variance of a random vector - part 1
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10 - Expectations and variance of a random vector - part 2
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11 - Expectations and variance of a random vector - part 3
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12 - Expectations and variance of a random vector - part 4
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13 - Least Squares as an unbiased estimator - matrix formulation
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14 - Variance of Least Squares Estimators - Matrix Form
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15 - The Gauss-Markov Theorem proof - matrix form - part 1
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16 - The Gauss-Markov Theorem proof - matrix form - part 2
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17 - The Gauss-Markov Theorem proof - matrix form - part 3
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18 - Geometric Interpretation of Ordinary Least Squares: An Introduction
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19 - Geometric Interpretation of Ordinary Least Squares: An Example
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20 - Geometric Least Squares Column Space Intuition
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21 - Geometric intepretation of least squares - orthogonal projection
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22 - Geometric interpretation of Least Squares: geometrical derivation of estimator
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23 - Orthogonal Projection Operator in Least Squares - part 1
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24 - Orthogonal Projection Operator in Least Squares - part 2
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25 - Orthogonal Projection Operator in Least Squares - part 3
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26 - Estimating the error variance in matrix form - part 1
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27 - Estimating the error variance in matrix form - part 2
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28 - Estimating the error variance in matrix form - part 3
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29 - Estimating the error variance in matrix form - part 4
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30 - Estimating the error variance in matrix form - part 5
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31 - Estimating the error variance in matrix form - part 6
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32 - Proof that the trace of Mx is p
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33 - Representing homoscedasticity and no autocorrelation in matrix form - part 1
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34 - Representing homoscedasticity and no autocorrelation in matrix form - part 2
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35 - Representing heteroscedasticity in matrix form
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36 - BLUE estimators in presence of heteroscedasticity - GLS - part 1
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37 - BLUE estimators in presence of heteroscedasticity - GLS - part 2
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38 - GLS estimators in matrix form - part 1
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39 - GLS estimators in matrix form - part 2
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40 - GLS estimators in matrix form - part 3
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41 - The variance of GLS estimators
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42 - GLS - example in matrix form
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43 - GLS estimators in the presence of autocorrelation and heteroscedasticity in matrix form
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44 - The Kronecker Product of two matrices - an introduction
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45 - SURE estimation - an introduction - part 1
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46 - SURE estimation - an introduction - part 2
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47 - SURE estimation - autocorrelation and heteroscedasticity
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48 - SURE estimator derivation - part 1
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49 - SURE estimator derivation - part 2
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50 - Kronecker Matrix Product - properties
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51 - SURE estimator - same independent variables - part 1
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52 - SURE estimator - same independent variables - part 2
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53 - SURE estimator - same independent variables - part 3
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54 - Causality - an introduction
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55 - The Rubin Causal model - an introduction
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56 - Causation in econometrics - a simple comparison of group means
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57 - Causation in econometrics - selection bias and average causal effect
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58 - Random assignment - removes selection bias
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59 - How to check if treatment is randomly assigned?
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60 - The conditional independence assumption: introduction
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61 - The conditional independence assumption - intuition
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62 - The average causal effect - an example
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63 - The average causal effect with continuous treatment variables
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64 the conditional independence assumption example
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65 - Linear regression and causality
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66 - Selection bias as viewed as a problem with samples
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67 - Sample balancing via stratification and matching
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68 - Propensity score - introduction and theorem
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69 - The Law of Iterated Expectations: an introduction
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70 - The Law of Iterated Expectations: introduction to nested form
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71 - Propensity score theorem proof - part 1
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72 - Propensity score theorem proof - part 2
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73 - Propensity score matching: an introduction
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74 - Propensity score matching - mathematics behind estimation
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Method of moments and generalised method of moments - basic introduction
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Method of Moments and Generalised Method of Moments Estimation - part 1
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Method of Moments and Generalised Method of Moments Estimation part 2
Description:
Dive into a comprehensive 7-hour graduate-level econometrics course covering advanced topics such as matrix formulation, geometric interpretation, GLS estimators, SURE models, LAD estimators, GIV estimators, and Generalised Method of Moments. Explore causality in regression, asymptotic theory of estimators, Kalman Filters, VARs, VECMs, and advanced time series modeling. Examine Fixed Effects, Random Effects, Unbalanced Panel model estimators, censored regression models, and conclude with an introduction to Bayesian estimators as a new paradigm. Progress through 74 detailed lectures, starting with matrix formulation basics and advancing to complex topics like propensity score matching and Generalised Method of Moments estimation.

Graduate Econometrics

Ox educ
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#Social Sciences #Economics #Econometrics #Data Science #Data Analysis #Time Series Analysis #Mathematics #Algebra #Linear Algebra #Matrix Operations
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1 - Introduction to the matrix formulation of econometrics