Time Schedule:
Myung Jig Kim
ECON 483
Seattle Campus
Provides opportunity to learn econometric model building for a particular problem while applying the theory learned in various courses to specific economic cases. Estimate, test, and forecast economic models. Extensive use of the computer and econometric programs. Prerequisite: 2.0 in ECON 301; either ECON/STAT 311, STAT 341, MATH/STAT 390, or QMETH 300.
Class description
This course is an introduction to data analysis and econometric modeling using applications in finance such as asset pricing, forecasting term structure of interest rates, strategic and tactical asset allocation, value at risk, and index tracking. Equivalently, this course is an introduction to computational finance and financial econometrics. As such, the course utilizes concepts from microeconomics, finance, mathematical optimization, data analysis, probability models, statistical analysis and econometrics.
The emphasis of the course will be on making the transition from an economic model of asset return behavior to an econometric model using real data. This involves: (1) specification of an economic model; (2) estimation of an econometric model; (3) testing of the assumptions of the econometric model; (4) testing the implications of the economic model; (5) forecasting from the econometric model. The modeling process requires the use of economic theory, probability models, optimization techniques and statistical analysis.
Topics in financial economics include asset return calculations, portfolio theory, index models, the capital asset pricing model and investment performance analysis. Mathematical topics covered include optimization methods involving equality and inequality constraints and basic matrix algebra. Statistical topics to be covered include probability and statistics (expectation, joint distributions, covariance, normal distribution, sampling distributions, estimation and hypothesis testing etc.) with the use of calculus, descriptive statistics and data analysis, linear regression, basic time series methods and the simulation of random data.
The course will mostly utilize Microsoft Excel for spreadsheet modeling, data analysis and statistical modeling. Eviews (econometrics software) and GAUSS (matrix computer language) will be occasionally introduced to supplement and to extend the data analysis and forecasting.
Student learning goals
General method of instruction
1. Lecture 2. Computer Demonstration 3. Assignments
Recommended preparation
1. Attendence 2. Reading lecture notes 3. Going over sample/practice questions 4. Supplement readings from textbooks
Class assignments and grading
1. Numerical examples about key concepts and real world problems.
1. attendece: 10% 2. Assignments: 40%(=4 assignments, 10% each) 3. Final exam: 50%
