Warwick Econometrics Tutor Online

Warwick Econometrics Tutor Online – EconTutors

Econometrics Tutor for Warwick EC203 EC226

Experienced Econometrics tutors for online and in-person tutoring for Warwick economics courses.

Topics:

Part 1: Introduction

Causality and the selection problem. Mostly Harmless Econometrics: chapter 1 & 2. Mastering ‘Metrics: chapter 1. Introductory Econometrics, A Modern Approach: chapter 1.
Inference: random variables, estimators, distributions, hypothesis testing, confidence intervals. First year notes EC122/124. Using Statistics in Economics: chapter1-7. Introductory Econometrics, A Modern Approach: appendix B.

Part 2: Regression Models

Causality and the conditional independence assumption (CIA). Mastering ‘Metrics: chapter1.
The simple linear regression model (SLR) – OLS. Introductory Econometrics, A Modern Approach: chapter 2. Mastering ‘Metrics: chapter2. Econometrics by Example: part I. Introductory Econometrics, chapter 1.
The multiple linear regression model (MLR) – OLS – Restriction Tests – Coefficient Interpretations. Introductory Econometrics, A Modern Approach: chapter 3-4. Mastering ‘Metrics: chapter2. Econometrics by Example: part I. Introductory Econometrics: chapter 2.6/7 and chapter 3.
Qualitative/dummy variables – Chow Test – LPM. Introductory Econometrics, A Modern Approach: chapter 7. Econometrics by Example: chapter 3. Introductory Econometrics: chapter 5.
Standard Error Problems. Heteroskedasticity, Multicollinearity & Non-normality in the errors: consequences, tests and solutions. Introductory Econometrics, A Modern Approach: chapter 3,8. Econometrics by Example: chapter 3,5. Introductory Econometrics: chapter 4,5,7
Model misspecification. Introductory Econometrics, A Modern Approach: chapter 3, 5, 9. Econometrics by Example: chapter 7. Introductory Econometrics: chapter 6, 8.

Part 3: Extensions

Randomised control trials (RCTs). Mastering ‘Metrics: chapter 1. Mostly Harmless Econometrics: chapter 1.
Instrumental variables (IV). Introductory Econometrics, A Modern Approach: chapter 15-16. Mastering ‘Metrics: 98. Econometrics by Example: chapter 19. Introductory Econometrics: chapter 8-9. Mostly Harmless Econometrics: chapter 4.
Panel data methods, including: first differencing (FD) and fixed effects (FE). Introductory Econometrics, A Modern Approach: chapter 13-14. Mastering ‘Metrics: chapter 5. Econometrics by Example: chapter 17.
Differences in Differences (DiD). Introductory Econometrics: chapter 13. Mostly Harmless Econometrics: chapter 5.
Regression discontinuity design (RDD). Mostly Harmless Econometrics: chapter 6. Mastering ‘Metrics: chapter 4.

CLRM Assumptions

$E[\epsilon]=0$ holds whenever there is an intercept, it means the error term is zero, on average.
No perfect multicollinearity and all Xs must exhibit some variation (MLR)
- *No perfect linear relationship between the Xs
- *The higher the variation in independent variables, the lower the variance of estimators. To increase variation, increase the sample size.
Conditional independence assumption
- $E[\epsilon| X_1, X_2, ..., X_k]=E[\epsilon] =0$
- If this holds, there is no selection effect. (observed effect = causal effect)
$Cov(\epsilon_i, \epsilon_j | X_1, ..., X_k) =0$ Zero serial correlation in the errors
$V(\epsilon | X_1, ... ,X_k) = \sigma^2$ homoskedasticity, or constant conditional variance of the residual term
$\epsilon | X_1, ... , X_k ~ N (0, \sigma^2)$ errors are normally distributed with a mean zero and constant variance.

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