ECMT - Econometrics (ECMT)
ECMT 461 Economic Data Analysis
Credits 3.
3 Lecture Hours.
Introduction to statistical methods and data analysis in economics; covers descriptive statistics, probability theory and the principles of statistical inference as foundations for empirical research; introduces data management and analysis techniques using spreadsheet and statistical software; emphasizes hypothesis testing and the fundamentals of regression analysis; develops skills for summarizing, visualizing and interpreting economic data to draw evidence-based conclusions about real-world economic relationships. Prerequisites: Grade of C or better in MATH 142, MATH 151, or MATH 171; junior or senior classification, or approval of instructor.
ECMT 463 Introduction to Econometrics
Credits 3.
3 Lecture Hours.
Application of mathematics and statistics to interpret economic phenomena; elementary econometric models and estimation techniques useful for estimating economic relationships and theories. Prerequisites: Grade of C or better in ECON 323; grade of C or better in ECMT 461, STAT 211 or STAT 303.
ECMT 464 Applied Econometrics
Credits 3.
3 Lecture Hours.
Application of econometric methods to analyze economic data with a focus on modern data analytics; focuses on the application of statistical software for model specification, estimation, and evaluation of complex economic relationships; designed for data-driven analysis in academic and professional settings. Prerequisites: Grade of C or better in ECON 323; grade of C or better in ECMT 461, STAT 211, or STAT 303; grade of C or better in CSCE 110, CSCE 111, or CSCE 121.
ECMT 475 Economic Forecasting
Credits 3.
3 Lecture Hours.
Application of econometric techniques to the prediction of economic and financial variables; develops tools for modeling and forecasting with time-series data; covers model specification, estimation and evaluation using methods such as ARMA models, trend analysis and tests for structural change; examines issues of overfitting, model selection and forecast combination; explores the assessment of forecast accuracy; integrates theoretical and empirical approaches using statistical software to generate and evaluate forecasts in real-world settings. Prerequisites: Grade of C or better in ECMT 463; junior or senior classification.