Econ 115a: Econometrics


Class schedule: Monday & Thursday | 13:00 - 14:00
Laboratory schedule: Wednesday | 13:00 - 16:00
Instructor: Christopher Llones
e-mail: christopher.llones@vsu.edu.ph
Pre-requisites: Stat21 or introductory statistics
Course credits: 3 units
Number of hours: 2 hrs lectures and 3 hrs laboratory per week

Course description

This course provides a comprehensive introduction to statistical methods used in economic research and policy analysis. Students will explore both foundational and advanced techniques for analyzing data, with a strong emphasis on practical application and critical interpretation.

Topics include:

  • Introduction to statistics and the logic of inference
  • Exploratory data analysis and assumption testing
  • Measures of association, including correlations
  • Linear and logistic regression modeling
  • Hypothesis testing on means and group comparisons
  • Non-parametric statistical methods
  • Exploratory factor analysis for uncovering latent structures

Throughout the course, students will gain hands-on experience using R, a versatile and widely adopted programming language for statistical computing and visualization. R will be used to implement analytical techniques, assess model assumptions, and produce reproducible, publication-ready outputs. By the end of the course, students will be equipped to conduct rigorous empirical analyses and communicate statistical findings with clarity and precision.

Course objectives

  • Understand foundational statistical principles
  • Conduct exploratory data analysis (EDA)
  • Evaluate statistical assumptions
  • Apply correlation and regression techniques
  • Perform hypothesis testing
  • Implement multivariate analysis
  • Utilize R for statistical computing
  • Interpret and communicate results effectively
  • Critically assess empirical research

Course outline

Week Topics Lessons Description
Week 1-2 Module 1: Introduction to Statistical Methods

  • Role of statistics in economics

  • Discuss the importance of statistics in economic research and policy analysis. Introduce types of data and variables.

  • Overview of statistical inference

  • Explain sampling, estimation, and hypothesis testing.

  • Introduce population vs. sample concepts.
  • Familiarize students with RStudio, basic syntax, and data structures in R.
Week 3-4 Module 2: Exploratory Data Analysis (EDA)
  • Data visualization techniques
  • Measures of central tendency and dispersion
  • Distributional properties

  • Use R to create histograms, boxplots, scatterplots, and bar charts.

  • Discuss mean, median, mode, range, variance, and standard deviation. Apply these using R.

  • Examine skewness, kurtosis, and normality. Use graphical and statistical tools to assess distributions.
Week 5 Module 3: Exploring Assumptions
  • Statistical assumptions
  • Diagnostic tools in R
  • Discuss assumptions of normality, linearity, independence, and homoscedasticity. Use residual plots, QQ plots, and statistical tests to evaluate assumptions.
Week 6-7 Module 4: Correlation Analysis
  • Pearson and Spearman correlations
  • Correlation matrices and heatmaps

  • Introduce correlation coefficients and their interpretation.

  • Use R to compute and visualize correlation matrices. Discuss multicollinearity.
Week 8-9 Module 5: Regression Analysis
  • Simple linear regression
  • Multiple regression
  • Model diagnostics

  • Estimate and interpret regression coefficients.

  • Discuss model fit and R².

  • Extend regression to multiple predictors.

  • Discuss interaction terms and dummy variables.

  • Use R to assess residuals, leverage, and influence.

  • Discuss multicollinearity and heteroscedasticity. |
Week 10 Module 6: Logistic Regression
  • Binary outcome modeling
  • Odds ratios and interpretation

  • Introduce logistic regression for categorical dependent variables.

  • Discuss log-odds, odds ratios, and model fit statistics.

  • Apply logistic regression in R.
Week 11 Module 7: Tests on Means
  • One-sample and two-sample t-tests
  • ANOVA

  • Conduct hypothesis tests on means.

  • Discuss assumptions and interpretation.

  • Introduce analysis of variance for comparing multiple group means.

  • Apply tests using R.
Week 12 Module 8: Non-Parametric Tests
  • Mann-Whitney U and Kruskal-Wallis tests
  • Chi-square tests

  • Introduce non-parametric alternatives to t-tests and ANOVA.

  • Discuss when and why to use them.

  • Apply tests for independence and goodness-of-fit.

  • Use R for implementation and interpretation.
Week 13-14 Module 9: Exploratory Factor Analysis (EFA)
  • Purpose and logic of EFA
  • Extraction and rotation methods
  • Interpretation and application

  • Discuss the rationale for uncovering latent constructs and reducing dimensionality.

  • Introduce principal components, eigenvalues, and rotation techniques.

  • Interpret factor loadings and apply EFA to real-world datasets using R.