Econometrics II: Quantitative Methods in Finance II (FINA 8397)

This course is the second part of the first year Ph.D. Econometrics sequence. The pre-requisite for the Econometrics sequence is linear algebra and an introductory graduate econometrics/statistics course. The goal of this sequence is to provide you with a broad overview of modern econometric tools. This means understanding when to use what test, which estimator, and why. This sequence is NOT designed to teach you how to use SAS, or Eviews. A fundamental knowledge of linear and matrix algebra, calculus and statistics is a prerequisite for the Econometrics sequence.

Office Hours:
Tuesdays and Thursdays: 2:30-3:30 (MH 210-D) or by appointment.

Textbook:
Econometric Analysis , 5th Edition, by William H. Greene, Prentice Hall, 2003.
Time Series Analysis, by J. D. Hamilton, Princeton University Press, 1994.


Other useful references:
Estimation and Inference in Econometrics, by R. Davidon and J. MacKinnon, Oxford University Press, 1993.
Econometric Analysis of Cross Section and Panel Data, by J. Wooldridge, MIT Press, 1999.

These texts will be supplemented by articles I will be assigning throughout the semester.

Outline of the course:
  • Review
    Lecture 1 – Download Lecture 1- Review
    Lecture 2 – Download Lecture 2- Review
  • Qualitative and Count Data
    Lecture 3 – Download Lecture 3 - Discrete Choice Models
    Lecture 4 – Download Lecture 4 - Binary Choice Models
    Lecture 5 – Download Lecture 5 - Multiple Choice Models I
    Lecture 6 – Download Lecture 6 - Multiple Choice Models II
    Lecture 7 – Download Lecture 7 - Count Data Models
  • Tobit & Sample Selection Models, Quantile Regression and Non-parametric Estimation
    Lecture 8 – Download Lecture 8 - Tobit Model
    Lecture 9 – Download Lecture 9 - Truncated Regression and Sample Selection Models
    Lecture 10 - Download Lecture 10 - Robust and Quantile Regressions
    Lecture 11 - Download Lecture 11 - Density Estimation
    Lecture 12 - Download Lecture 12 - Non-parametric Regression
  • Time Series
    Lecture 13 - Download Lecture 13 - Time Series: Stationarity, AR(p) & MA(q)
    Lecture 14 - Download Lecture 14 - Time Series: ARIMA
    Lecture 15 - Download Lecture 15 - Time Series: Forecasting
    Lecture 16 - Download Lecture 16 - Time Series: Unit Roots
    Lecture 17 - Download Lecture 17 - Multivariate Time Series: VAR & SVAR
    Lecture 18 - Download Lecture 18 - Multivariate Time Series: Cointegration
    Lecture 19 - Download Lecture 19 - Kalman Filter


    Readings
  • Discrete Choice Models
    Pagan's (2004) - DCM (Lecture Notes)
    McFadden's Nobel Prize Lecture
    Greene's Survey on DCM
  • Ordered Choice Models
    Greene's Survey on OC Models
  • Simulation-based inference ML
    Steve Stern (1999) - Lecture Notes
    Jan Yu (2010) - Simulation in Financial Time Series
  • Count Data Models
    Greene's Survey on Count Data Models
  • Censored Truncated Data Models
    Pagan's (2004) - Censored and Truncated Regressions (Lecture Notes)
    Imbens (2004) - Model Selection (Lecture Notes)
  • M-Estimation
    Martin & Zamar - Robust Statistics (Lecture Notes)
    Fox and Weisberg (2012) - Robust Regression
  • Quantile Regression
    Koenker & Hallock (2000) - Quantile Regression: An Introduction
    Powell's Lecture Notes on Median and Quantile Regression (Asymptotics)
    Koenker's (2005) - Vignette (R quantile estimation program)
  • Non-parametrics
    Yatchew (1998) - Nonparametric Regression Techniques in Economics
    R Nonparametric Package - Vignette

    Exams and Grading:
    Exams (60%) - Three to be scheduled (2/23, 3/30, 4/25)
    Project (20%)
    Homework (20%) - Regular assignments at the end of each topic


    Homework
  • Homework 1 (Qualitative Data) (doc file) - Data (zip file)
  • Homework 2 (Tobit, LAD, Non-parametric) (zip file) - Data (xls file)
  • Homework 3 (Time Series). (zip file)


  • Midterms 2013. Download Old Exams (zip file)

    Old Exams
  • Midterms 2013. Download Old Exams (zip file)


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