Econometric approach to prediction and forecasting; data mining and in-sample overfitting; exploratory data analysis; model selection; recursive techniques; structural change; nonlinear models; causality; forecast evaluation and combination; practical issues in real world prediction and forecasting. Prerequisites: ECMT 463 with a grade of C or better; junior or senior classification Credits 3. 3 Lecture Hours.
Econometric approach to prediction and forecasting; data mining and in-sample overfitting; exploratory data analysis; model selection; recursive techniques; structural change; nonlinear models; causality; forecast evaluation and combination; practical issues in real world prediction and forecasting. Prerequisites: ECMT 463 with a grade of C or better; junior or senior classification Credits 3. 3 Lecture Hours.
