Master
2021/2022
Experimetrics
Category 'Best Course for Broadening Horizons and Diversity of Knowledge and Skills'
Category 'Best Course for New Knowledge and Skills'
Type:
Elective course (Economics and Economic Policy)
Area of studies:
Economics
Delivered by:
Department of Theoretical Economics
Where:
Faculty of Economic Sciences
When:
1 year, 3, 4 module
Mode of studies:
offline
Open to:
students of one campus
Instructors:
James Christopher Ross Tremewan
Master’s programme:
Economics and Economic policy
Language:
English
ECTS credits:
6
Contact hours:
80
Course Syllabus
Abstract
This course provides a comprehensive introduction to the statistical tools required to analyse experimental data, with a focus on applications in Behavioural and Experimental Economics. We will cover both parametric and non-parametric approaches to identifying and estimating treatment effects. More advanced topics include estimating the magnitude and heterogeneity of social preferences and bounded rationality, and models of learning. The course will also address how statistical considerations should influence experimental design, and how to critically evaluate the validity of statistical claims in scientific articles.
Learning Objectives
- Design experiments to maximize statistical power.
- Apply appropriate statistical tests to experimental data.
- Make correct inferences from statistical tests.
- Correctly account for non-independence of experimental data.
- Identify and correct for issues leading to non-replicability of experimental results.
- Estimate structural models derived from behavioural economics.
- Critique inference in experimental studies.
Expected Learning Outcomes
- To adjust for multiple hypotheses
- To Introduce to hypothesis testing. To learn Binomial test, exact Z-test, Mann-Whitney, stochastic inequality test, Wilcoxon rank sum, sign test, Spearman rank-correlation, Kendall rank correlation. To use Monte Carlo simulations to estimate size and power of tests
- To learn Heterogeneity and structural modelling
- To learn how to design experiments to maximize statistical power
- To use matching-group averages. To make Multi-level modelling
Course Contents
- Experimental design and experimetrics • How to design experiments to maximize statistical power.
- Reading and critiquing experimental studies
- Regression analysis and dealing with dependence of observations
- Heterogeneity and structural modelling
- The replication crisis
- Hypothesis testing and non-parametric tests • How to choose which test to apply to experimental data and what inferences can be drawn.
Assessment Elements
- Non-parametric tests
- Multiple hypothesis testing and accounting for non-independent data
- Estimating structural models
- Critique of experimental study
- Presentation of own critique and class discussion
- Final testRetake and comission will be held in the same format as the final test. Grades will be recalculated
Interim Assessment
- 2021/2022 4th module0.15 * Multiple hypothesis testing and accounting for non-independent data + 0.15 * Non-parametric tests + 0.3 * Final test + 0.15 * Estimating structural models + 0.1 * Presentation of own critique and class discussion + 0.15 * Critique of experimental study
