University Year 2 (Sophomore) Introductory Econometrics Rubric Description This rubric outlines the key learning objectives and assessment criteria for an introductory econometrics course designed for second-year university students. The course introduces foundational concepts in econometrics; emphasizing the application of statistical methods to economic data. Students will develop skills in data analysis; hypothesis testing; and regression modeling; preparing them for advanced coursework and real-world problem-solving. The rubric evaluates students on four core areas: theoretical understanding; practical application; analytical reasoning; and communication of results. In theoretical understanding; students must demonstrate knowledge of basic econometric principles; including linear regression; hypothesis testing; and model assumptions. Practical application assesses their ability to use statistical software (e.g.; R; Stata; or Python) to estimate and interpret econometric models. Analytical reasoning measures their capacity to critique models; identify potential biases; and suggest improvements. Finally; communication evaluates how clearly and effectively students present their findings; both in writing and verbally. By meeting these criteria; students will gain a strong foundation in econometric methods; enhancing their ability to analyze economic data and make evidence-based decisions. The course emphasizes hands-on learning through problem sets; case studies; and a final project; ensuring students can apply theoretical knowledge to real-world scenarios. Successful completion of the course will equip students with valuable quantitative skills for academic research; policy analysis; and careers in economics; finance; or data science. The rubric ensures fair and transparent assessment while encouraging continuous improvement in critical thinking and technical proficiency.
