Data Analysis in Economics

Indicative content

Week 1: Introduction to Research
Methodology of statistical research
Statistical method and logic of statistical reasoning
General laws and theories: objectivity and critical realism
Week 2: Introduction to Statistics and STATA Software
Statistical method and logic of statistical reasoning
Importance of significance level when studying the entire population
Analysis of quantitative research studies
Week 3: Basics of STATA Software
Getting acquainted with the STATA environment and its functions
Inserting and importing data into STATA from economic databases and various data files
Basic data management: opening, browsing, and modifying variables
Generating and labelling variables, data subsets
Week 4: Descriptive Statistics
Creating frequency distributions and summary statistics
Data handling: attaching, merging, and transforming data
Basics of graphing in STATA
Week 5: Data Visualization
Basics of graph creation
Visualization of continuous and categorical data types
Visualization of unconditional relationships
Week 6: Bivariate Inferential Statistics
Correlation and its applications
Independent t-tests, analysis of variance (ANOVA), chi-square tests
Week 7: Simple Linear Regression and Econometric Model
Introduction to regression analysis and the principle of the least squares method
Assessing model quality: model fit, hypothesis testing, and prediction
Week 8: Interaction Effects in Regression
Definition of interaction effects and their analysis
Working with interaction variables (continuous and discrete dummy variables)
Week 9: Assumptions and Diagnostics of Linear Regression
Correct model specification: linearity, additivity, multicollinearity
Assumptions about residuals: homoscedasticity, normality, and independence
Identifying influential observations: leverage, DFBETA, Cook's distance
Week 10: Introduction to Causal Inference
Why causal inference? The difference between correlation and causality.
Potential Outcomes Framework
Natural experiments and basic concepts of causality: Randomization and Counterfactual scenario
Week 11-13: Replication of Studies and Presentation of Projects Using Linear Regression, Data Handling, and Visualization
Presentation of examples using linear regression on selected case studies from public policy (discrimination in the labor market, the impact of smoking on health, the effect of increased tax on the price of sugary drinks, measuring willingness to pay for reducing environmental pollution rates from textbooks and other Doing Economics or practical studies from IFP, UHP, and other analytical units)