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# Statistics and Econometrics - ECO00094M

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• Department: Economics and Related Studies
• Module co-ordinator: Prof. Takashi Yamagata
• Credit value: 20 credits
• Credit level: M
• Academic year of delivery: 2023-24

## Module summary

This module introduces basic statistical and probability concepts and various econometric methods commonly used in quantitative analysis.

## Module will run

Occurrence Teaching cycle
A Semester 1 2023-24

## Module aims

The module covers the following topics:

• Probability, random variables, point and interval estimation, small and large sample properties of estimators, hypotheses testing;

• Simple linear regression models, the OLS estimator, t and F tests, properties of the OLS estimator, Gauss-Markov theorem

• Multiple linear regression models, heteroskedasticity, autocorrelation, specification errors, dummy variables, variables of interactions

• Endogeneity and instrumental variable estimators

• Binary dependent variable models and maximum likelihood estimators

• Treatment effects and difference-in-difference estimators

## Module learning outcomes

Having successfully completed this module you will be able to:

• demonstrate understanding of key statistical concepts

• select the appropriate statistical models for the data set, estimate them and perform appropriate statistical tests using statistical computer package software.

• analyse, interpret and summarise estimation and inference results and present them in an accessible manner to the audience

## Module content

Week No. and Contents (4 hours each week, including seminars/practical’s)

1. Basic probabilities and random variables

2. Joint distributions, linear combinations, sampling distributions

3. Point and interval estimation, Hypothesis testing

4. Simple linear regression: OLS estimator and its properties

5. Multiple linear regression: Estimation

6. Multiple linear regression: Inference

7. Multiple linear regressions with binary, interactive variables, squared variables

8. Multiple linear regression: Heteroskedastic errors, specification tests, time-series data

9. Multiple linear regression: Endogeneity and 2SLS estimation

10. Introduction to Treatment effects: randomised trial, difference-in-difference estimator

11. Maximum Likelihood Estimation and Binary Choice Models

## Assessment

Task Length % of module mark
Closed/in-person Exam (Centrally scheduled)
Statistics and Econometrics
3 hours 100

None

### Reassessment

Task Length % of module mark
Closed/in-person Exam (Centrally scheduled)
Statistics and Econometrics
3 hours 100

## Module feedback

Feedback will be provided in line with University policy