## 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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