APEC 8212: Econometric Analysis II

Size: px
Start display at page:

Download "APEC 8212: Econometric Analysis II"

Transcription

1 APEC 8212: Econometric Analysis II Instructor: Paul Glewwe Spring, 2014 Office: 337a Ruttan Hall (formerly Classroom Office Building) Phone: Class Website: Office Hours: By Appointment (send me an to schedule an appointment) Lecture Hours: MW 10:40 a.m. 12:35 p.m. Classroom: Room 415, Alderman Hall Teaching Assistant: Jongwook Lee Office: 218a Ruttan Hall Phone: Course Description: This course is a continuation of Apec It will provide deeper coverage of some of the topics covered in Apec 8211 (heteroscedasticity, measurement error and maximum likelihood estimation) and will introduce many new topics (instrumental variables, panel data, simultaneous equation estimation, bootstrap methods, limited dependent variable models, density estimation, semi-parametric estimation, econometrics of program evaluation, time series analysis and hazard models). The focus will be on empirical work rather than on theoretical topics. Students should have completed Apec 8211 or an equivalent course. There are two required textbooks and three optional books for this course: Required: Jeff Wooldridge. Econometric Analysis of Cross Section and Panel Data. 2 nd ed Walter Enders. Applied Econometric Time Series (third edition) Optional: William Greene. Econometric Analysis (sixth edition) James Hamilton. Times Series Analysis Colin Cameron & Pravin Trivedi. Microeconometrics: Methods & Applications Joshua Angrist and Jörn-Steffen Pischke. Mostly Harmless Econometrics Grading: There will be homework, a midterm, and a final. Their weight in the final grade will be: Homework: 30% Mid-Term: 30% Final: 40% You must take the final at the scheduled time (Saturday May 17, 8:00 a.m.)! Computer Assignments: Much of the homework will involve econometric estimation. The software used will be Stata, which has been ordered for student use by the Department of Applied Economics.

2 Lectures: In addition to readings from Greene, Enders, Wooldridge, and Hamilton, there will also be many other readings. All will be available in Waite Library (and elsewhere, if requested). Advanced readings are marked by an asterisk. These are optional and given for anyone interested in further reading. They are not on reserve but are in the University libraries. 1. Introduction + Conditional Expectations and Related Concepts (January 22). Readings: Wooldridge, Chapters 1 and 2 2. Review of Basic Asymptotic Theory (January 24, 3:00 the 1 st of 2 lectures on a Friday). Readings: Wooldridge, Chapter 3 Review Greene, Appendix D * White, Halbert Asymptotic Theory for Econometricians (Revised Edition) Chapters 2, 3, 4 and 5. * Engle, Robert Wald, Likelihood Ratio and Langrange Multiplier Tests in Econometrics in Griliches and Intriligator, Handbook of Econometrics Volume 2. * Newey, Whitney, and Daniel McFadden Large Sample Estimation and Hypothesis Testing in Engle and McFadden, Handbook of Econometrics Volume Review of Single Equation Linear Model and OLS Estimation (January 27) Readings: Wooldridge, Chapter Instrumental Variable Methods (January 29 and February 3) Readings: Wooldridge, Chapters 5 and 6 Review Greene, Chapter 12, Sections 1-4 Bound, Jaeger and Baker Problems with Instrumental Variables Estimation Journal of American Statistical Association, 90(430): Shea, John Instrumental Relevance in Multivariate Linear Models: A Simple Measure. Review of Economics and Statistics 79(2): Stock and Yogo Testing for Weak Instruments in Linear IV Regression, in D. Andrews & J. Stock, eds. Identification and Inference for Econometric Models: Essays in Honor of Thomas Rothenberg. 2

3 * Staiger and Stock Instrumental Variables Regression with Weak Instruments. Econometrica, 65(3): * Stock and Wright GMM with Weak Identification. Econometrica 68(5): * Lewbel Constructing Instruments for Regressions with Measurement Error... Econometrica 65(5): * Bound, Brown and Mathiowetz Measurement Error in Survey Data. in Heckman and Leamer, Handbook of Econometrics Volume 5 * Hahn and Hausman Weak Instruments: Diagnosis and Cures in Empirical Econometrics American Economic Review 93(2): * Cruz and Moreira On the Validity of Econometric Techniques with Weak Instruments. Journal of Human Resources 40(2): Systems of Regression Equations & Simultaneous Equation Models (February 5, 10 and 12) Readings: Wooldridge, Chapters 7, 8 and 9. * Davidson and MacKinnon Estimation and Inference in Econometrics. Chapter 18, Section Panel Data (February 17 and 19) Readings: Wooldridge, Chapters 10 and 11, Review Greene, Chapter 9 Deaton pp Hausman and Taylor Panel Data and Unobservable Individual Effects. Econometrica 49(6): Deaton Panel Data from Time Series of Cross-Sections. Journal of Econometrics 30(1): *Bertrand, Duflo and Mullainathan How Much Should We Trust Difference in Difference Estimates? Quarterly Journal of Economics 119(1): * Mátyás and Sevestre The Econometrics of Panel Data. (2 nd ed.). * Arellano and Honoré Panel Data Models: Some Recent Developments in Heckman and Leamer. Handbook of Econometrics Volume 5 * Baltagi Econometric Analysis of Panel Data. (3 rd Edition) * Hsiao Analysis of Panel Data (2 nd Edition) 7. M-Estimation (nonlinear estimation) (February 24) Readings: Wooldridge, Chapter 12. 3

4 8. Maximum Likelihood Estimation (February 26) Readings: Wooldridge, Chapter 13. Review Greene, Chapter General Method of Moments Approach (March 3) Readings: Wooldridge, Chapter 14. Greene, Chapter 15. Wooldridge Application of Generalized Method of Moments Estimation Journal of Economic Perspectives 15(4): * Davidson and MacKinnon, Chapter 17. * Hansen Large Sample Properties of Generalized Method of Moments Estimators Econometrica 50(4): Limited Dependent Variable Models (March 5, 10 and 24) Maximum Likelihood Estimation and Probit and Logit. Readings: Wooldridge, Chapter 15 (sections 1 6) Review Greene, Chapter 23 (sections 1 4) Deaton, 1997, pp *Hajivassiliou and Ruud Classical Estimation Methods for LDV Models Using Simulation. in Engle and McFadden. Ordered Probits and Logits, Mutltinomial Probits and Logits Readings: Wooldridge, Chapter 15 (sections 9 and 10) Specification Tests, Endogenous Regressors and Panel Data Readings: Wooldridge, Chapter 16 Rivers and Vuong Limited Information Estimators and Exogeneity Tests Journal of Econometrics, 39: Tobits and Sample Selection Models Readings: Wooldridge, Chapters 17 and 19 Deaton, pp Smith and Blundell An Exogeneity Test for a Simultaneous Equation Tobit Model Econometrica 54(2): Chay and Powell Semiparametric Censored Regression Models Journal of Economic Perspectives 15(4): Midterm: Wednesday, March 12 Spring Break: March

5 11. Hazard Models (March 14) [Optional Lecture at Friday recitation time] Readings: Wooldridge, Chapter 20. Kiefer Economic Duration Data and Hazard Functions Journal of Economic Literature. 26: * Lancaster The Economic Analysis of Transition Data. * van den Berg Duration Models: Specification, Identification, and Multiple Durations in Heckman & Leamer. 12. Robust Estimation Bootstrap and Related Methods (March 26) Readings: Cameron and Trivedi, Chapter 11. Deaton pp * Efron and Tibshirani An Introduction to the Bootstrap. * Horowitz The Bootstrap in Heckman & Leamer. * Hall Methodology & Theory of the Bootstrap in Engle & McFadden. 13. Density Estimation and Semi-parametric Econometrics (March 28 (Friday) and 31) Density Estimation Readings: Cameron and Trivedi, Chapter 9, Sections 1-3. Deaton pp * Hardle and Linton Applied Nonparametric Methods in Handbook of Econometrics, vol. 4, pp *Pagan and Ullah Nonparametric Econometrics. Chapter 2. General Methods of Nonparametric and Semiparametric Econometrics Readings: Cameron and Trivedi, , Deaton, pp Blundell and Duncan Kernel Regression in Empirical Microeconomics. Journal of Human Resources. 33(1): * Pagan and Ullah Nonparametric Econometrics. Chapter 3. * Yatchew Semiparametric Regression for the Applied Econometrican. Chapter 4. * Ichimura and Todd Implementing Nonparametric and Semiparametric Estimators, in Handbook of Econometrics, vol. 6B. Application to Index Models and Selection Models Readings: Wooldridge, Chapter 15 (subsections 7.5 and 8.6) Vella, Francis Estimating Models with Sample Selection Bias. Journal of Human Resources 33(1): Pagan and Ullah Nonparametric Econometrics. Chaps. 5, 7 & 8. * Powell Estimation of Semiparametric Models in Engle and McFadden. 5

6 14. Econometrics of Program Evaluation (April 2 and 7) Readings: Imbens and Wooldridge Recent Developments in the Econometrics of Program Evaluation. Journal of Economic Literature 47(1): Wooldridge, Chapter 21. Blundell and Dias Alternative Approaches to Evaluation in Empirical Microeconomics. Journal of Human Resources 44(3): * Manski Identification of Endogenous Social Effects: The Reflection Problem Review of Economic Studies 60: * Heckman and Vytlacil Econometric Evaluation of Social Programs, Part I, in Heckman and Leamer, eds. Handbook of Econometrics, vol 6B. * Heckman and Vytlacil Econometric Evaluation of Social Programs, Part II, in Heckman and Leamer, eds. Handbook of Econometrics, vol 6B. 15. Time Series Econometrics (April 9, 14, 16, 21, 23, 28, 30 and May 5 and 7) Difference Equations (April 9 and 14) Reading: Enders, Chapter 1. * Hamilton, Chapters 1 and 2 Stationary Time Series Models (April 16 and 21) Readings: Enders Chapter 2 * Hamilton, Chapter 3 Estimation of Time Series Models (April 23) Readings: Hamilton, Chapters 5 and 14 Modeling Economic Time Series (April 28) Readings: Enders Chapter 3 * Hamilton Chapter 21 Unit Roots (April 30) Readings: Enders Chapter 4 * Hamilton Chapter 15 Jones Time Series Tests of Endogenous Growth Models. Quarterly Journal of Economics 110(2): Introduction to Vector Autoregression (May 5) Readings: Enders Chapter 5 (sections 1 5) * Hamilton Chapters 10 and 11 * Watson Vector Autoregressions and Cointegration. in Engle and McFadden. Introduction to Cointegration (May 7) Readings: Enders Chapter 6 (sections 1 7) * Hamilton Chapters 19 and 20 6

Econ 273B Advanced Econometrics Spring

Econ 273B Advanced Econometrics Spring Econ 273B Advanced Econometrics Spring 2005-6 Aprajit Mahajan email: amahajan@stanford.edu Landau 233 OH: Th 3-5 or by appt. This is a graduate level course in econometrics. The rst part of the course

More information

12E016. Econometric Methods II 6 ECTS. Overview and Objectives

12E016. Econometric Methods II 6 ECTS. Overview and Objectives Overview and Objectives This course builds on and further extends the econometric and statistical content studied in the first quarter, with a special focus on techniques relevant to the specific field

More information

UNIVERSITY OF CALIFORNIA Spring Economics 241A Econometrics

UNIVERSITY OF CALIFORNIA Spring Economics 241A Econometrics DEPARTMENT OF ECONOMICS R. Smith, J. Powell UNIVERSITY OF CALIFORNIA Spring 2006 Economics 241A Econometrics This course will cover nonlinear statistical models for the analysis of cross-sectional and

More information

1. GENERAL DESCRIPTION

1. GENERAL DESCRIPTION Econometrics II SYLLABUS Dr. Seung Chan Ahn Sogang University Spring 2003 1. GENERAL DESCRIPTION This course presumes that students have completed Econometrics I or equivalent. This course is designed

More information

Course Description. Course Requirements

Course Description. Course Requirements University of Pennsylvania Spring 2007 Econ 721: Advanced Microeconometrics Petra Todd Course Description Lecture: 9:00-10:20 Tuesdays and Thursdays Office Hours: 10am-12 Fridays or by appointment. To

More information

Course Description. Course Requirements

Course Description. Course Requirements University of Pennsylvania Fall, 2016 Econ 721: Advanced Micro Econometrics Petra Todd Course Description Lecture: 10:30-11:50 Mondays and Wednesdays Office Hours: 10-11am Fridays or by appointment. To

More information

Course Description. Course Requirements

Course Description. Course Requirements University of Pennsylvania Fall, 2015 Econ 721: Econometrics III Advanced Petra Todd Course Description Lecture: 10:30-11:50 Mondays and Wednesdays Office Hours: 10-11am Tuesdays or by appointment. To

More information

Syllabus. By Joan Llull. Microeconometrics. IDEA PhD Program. Fall Chapter 1: Introduction and a Brief Review of Relevant Tools

Syllabus. By Joan Llull. Microeconometrics. IDEA PhD Program. Fall Chapter 1: Introduction and a Brief Review of Relevant Tools Syllabus By Joan Llull Microeconometrics. IDEA PhD Program. Fall 2017 Chapter 1: Introduction and a Brief Review of Relevant Tools I. Overview II. Maximum Likelihood A. The Likelihood Principle B. The

More information

UNIVERSITY OF MARYLAND Department of Economics. Part A: Guido Kuersteiner Econ 722 Part B: Ingmar R. Prucha Spring 2018

UNIVERSITY OF MARYLAND Department of Economics. Part A: Guido Kuersteiner Econ 722 Part B: Ingmar R. Prucha Spring 2018 UNIVERSITY OF MARYLAND Department of Economics Part A: Guido Kuersteiner Econ 722 Part B: Ingmar R. Prucha Spring 2018 Lecture: Part A, Tu 5:00-7:30pm, Part B, Tu 5:00-7:30pm (TYD 0102) If needed, some

More information

Microeconometrics. C. Hsiao (2014), Analysis of Panel Data, 3rd edition. Cambridge, University Press.

Microeconometrics. C. Hsiao (2014), Analysis of Panel Data, 3rd edition. Cambridge, University Press. Cheng Hsiao Microeconometrics Required Text: C. Hsiao (2014), Analysis of Panel Data, 3rd edition. Cambridge, University Press. A.C. Cameron and P.K. Trivedi (2005), Microeconometrics, Cambridge University

More information

EC 709: Advanced Econometrics II Fall 2009

EC 709: Advanced Econometrics II Fall 2009 EC 709: Advanced Econometrics II Fall 2009 Instructor: Zhongjun Qu O ce: Room 312, 270 Bay State Road Email: qu@bu.edu O ce Hours: Tuesday 3:30-5:00, Wednesday 2:00-3:30 TA: Denis Tkachenko, tkatched@bu.edu;

More information

Increasing the Power of Specification Tests. November 18, 2018

Increasing the Power of Specification Tests. November 18, 2018 Increasing the Power of Specification Tests T W J A. H U A MIT November 18, 2018 A. This paper shows how to increase the power of Hausman s (1978) specification test as well as the difference test in a

More information

UNIVERSITY OF STELLENBOSCH Optional course in Advanced Econometrics: Cross section 2016

UNIVERSITY OF STELLENBOSCH Optional course in Advanced Econometrics: Cross section 2016 UNIVERSITY OF STELLENBOSCH Optional course in Advanced Econometrics: Cross section 2016 Convener: Prerequisites: Lectures & tutorials: Lecturers: Rulof Burger (rulof@sun.ac.za) This course presumes some

More information

Jinyong Hahn. Department of Economics Tel: (310) Bunche Hall Fax: (310) Professional Positions

Jinyong Hahn. Department of Economics Tel: (310) Bunche Hall Fax: (310) Professional Positions Jinyong Hahn Department of Economics Tel: (310) 825-2523 8283 Bunche Hall Fax: (310) 825-9528 Mail Stop: 147703 E-mail: hahn@econ.ucla.edu Los Angeles, CA 90095 Education Harvard University, Ph.D. Economics,

More information

Econometric Analysis of Cross Section and Panel Data

Econometric Analysis of Cross Section and Panel Data Econometric Analysis of Cross Section and Panel Data Jeffrey M. Wooldridge / The MIT Press Cambridge, Massachusetts London, England Contents Preface Acknowledgments xvii xxiii I INTRODUCTION AND BACKGROUND

More information

A Course in Applied Econometrics Lecture 14: Control Functions and Related Methods. Jeff Wooldridge IRP Lectures, UW Madison, August 2008

A Course in Applied Econometrics Lecture 14: Control Functions and Related Methods. Jeff Wooldridge IRP Lectures, UW Madison, August 2008 A Course in Applied Econometrics Lecture 14: Control Functions and Related Methods Jeff Wooldridge IRP Lectures, UW Madison, August 2008 1. Linear-in-Parameters Models: IV versus Control Functions 2. Correlated

More information

Problem Set 3: Bootstrap, Quantile Regression and MCMC Methods. MIT , Fall Due: Wednesday, 07 November 2007, 5:00 PM

Problem Set 3: Bootstrap, Quantile Regression and MCMC Methods. MIT , Fall Due: Wednesday, 07 November 2007, 5:00 PM Problem Set 3: Bootstrap, Quantile Regression and MCMC Methods MIT 14.385, Fall 2007 Due: Wednesday, 07 November 2007, 5:00 PM 1 Applied Problems Instructions: The page indications given below give you

More information

Microeconometrics MSc. in Economic Analysis 2014/2015

Microeconometrics MSc. in Economic Analysis 2014/2015 FACULTAD DE CIENCIAS SOCIALES Microeconometrics MSc. in Economic Analysis 2014/2015 DEPARTAMENTO DE ECONOMIA Instructor: Jesús M. Carro Office: 15.2.28 E-mail: jcarro@eco.uc3m.es The objective of this

More information

ECO 2403 TOPICS IN ECONOMETRICS

ECO 2403 TOPICS IN ECONOMETRICS ECO 2403 TOPICS IN ECONOMETRICS Department of Economics. University of Toronto Winter 2019 Instructors: Victor Aguirregabiria Phone: 416-978-4358 Office: 150 St. George Street, Room 309 E-mail: victor.aguirregabiria@utoronto.ca

More information

Econometrics I G (Part I) Fall 2004

Econometrics I G (Part I) Fall 2004 Econometrics I G31.2100 (Part I) Fall 2004 Instructor: Time: Professor Christopher Flinn 269 Mercer Street, Room 302 Phone: 998 8925 E-mail: christopher.flinn@nyu.edu Homepage: http://www.econ.nyu.edu/user/flinnc

More information

Economics 308: Econometrics Professor Moody

Economics 308: Econometrics Professor Moody Economics 308: Econometrics Professor Moody References on reserve: Text Moody, Basic Econometrics with Stata (BES) Pindyck and Rubinfeld, Econometric Models and Economic Forecasts (PR) Wooldridge, Jeffrey

More information

11. Bootstrap Methods

11. Bootstrap Methods 11. Bootstrap Methods c A. Colin Cameron & Pravin K. Trivedi 2006 These transparencies were prepared in 20043. They can be used as an adjunct to Chapter 11 of our subsequent book Microeconometrics: Methods

More information

Microeconometrics and Stata over the Past Thirty Years

Microeconometrics and Stata over the Past Thirty Years Microeconometrics and Stata over the Past Thirty Years A. Colin Cameron Department of Economics, University of California - Davis. This version: August 28, 2014 Abstract This article discusses how microeconometrics

More information

Informational Content in Static and Dynamic Discrete Response Panel Data Models

Informational Content in Static and Dynamic Discrete Response Panel Data Models Informational Content in Static and Dynamic Discrete Response Panel Data Models S. Chen HKUST S. Khan Duke University X. Tang Rice University May 13, 2016 Preliminary and Incomplete Version Abstract We

More information

Comments on: Panel Data Analysis Advantages and Challenges. Manuel Arellano CEMFI, Madrid November 2006

Comments on: Panel Data Analysis Advantages and Challenges. Manuel Arellano CEMFI, Madrid November 2006 Comments on: Panel Data Analysis Advantages and Challenges Manuel Arellano CEMFI, Madrid November 2006 This paper provides an impressive, yet compact and easily accessible review of the econometric literature

More information

Finite Sample Performance of A Minimum Distance Estimator Under Weak Instruments

Finite Sample Performance of A Minimum Distance Estimator Under Weak Instruments Finite Sample Performance of A Minimum Distance Estimator Under Weak Instruments Tak Wai Chau February 20, 2014 Abstract This paper investigates the nite sample performance of a minimum distance estimator

More information

A Guide to Modern Econometric:

A Guide to Modern Econometric: A Guide to Modern Econometric: 4th edition Marno Verbeek Rotterdam School of Management, Erasmus University, Rotterdam B 379887 )WILEY A John Wiley & Sons, Ltd., Publication Contents Preface xiii 1 Introduction

More information

SS 222C: Econometrics California Institute of Technology Spring 2006

SS 222C: Econometrics California Institute of Technology Spring 2006 SS 222C: Econometrics California Institute of Technology Spring 2006 Professor: Tae-Hwy Lee (Email: tlee@hss.caltech.edu; Phone 626-395-3531; 104 Baxter) Lecture: MW 2:30-3:55 p.m., 210 Baxter Office Hours:

More information

G. S. Maddala Kajal Lahiri. WILEY A John Wiley and Sons, Ltd., Publication

G. S. Maddala Kajal Lahiri. WILEY A John Wiley and Sons, Ltd., Publication G. S. Maddala Kajal Lahiri WILEY A John Wiley and Sons, Ltd., Publication TEMT Foreword Preface to the Fourth Edition xvii xix Part I Introduction and the Linear Regression Model 1 CHAPTER 1 What is Econometrics?

More information

Econometrics of Panel Data

Econometrics of Panel Data Econometrics of Panel Data Jakub Mućk Meeting # 1 Jakub Mućk Econometrics of Panel Data Meeting # 1 1 / 31 Outline 1 Course outline 2 Panel data Advantages of Panel Data Limitations of Panel Data 3 Pooled

More information

Non-linear panel data modeling

Non-linear panel data modeling Non-linear panel data modeling Laura Magazzini University of Verona laura.magazzini@univr.it http://dse.univr.it/magazzini May 2010 Laura Magazzini (@univr.it) Non-linear panel data modeling May 2010 1

More information

NBER WORKING PAPER SERIES A NOTE ON ADAPTING PROPENSITY SCORE MATCHING AND SELECTION MODELS TO CHOICE BASED SAMPLES. James J. Heckman Petra E.

NBER WORKING PAPER SERIES A NOTE ON ADAPTING PROPENSITY SCORE MATCHING AND SELECTION MODELS TO CHOICE BASED SAMPLES. James J. Heckman Petra E. NBER WORKING PAPER SERIES A NOTE ON ADAPTING PROPENSITY SCORE MATCHING AND SELECTION MODELS TO CHOICE BASED SAMPLES James J. Heckman Petra E. Todd Working Paper 15179 http://www.nber.org/papers/w15179

More information

New York University Department of Economics. Applied Statistics and Econometrics G Spring 2013

New York University Department of Economics. Applied Statistics and Econometrics G Spring 2013 New York University Department of Economics Applied Statistics and Econometrics G31.1102 Spring 2013 Text: Econometric Analysis, 7 h Edition, by William Greene (Prentice Hall) Optional: A Guide to Modern

More information

IDENTIFICATION OF THE BINARY CHOICE MODEL WITH MISCLASSIFICATION

IDENTIFICATION OF THE BINARY CHOICE MODEL WITH MISCLASSIFICATION IDENTIFICATION OF THE BINARY CHOICE MODEL WITH MISCLASSIFICATION Arthur Lewbel Boston College December 19, 2000 Abstract MisclassiÞcation in binary choice (binomial response) models occurs when the dependent

More information

Conditions for the Existence of Control Functions in Nonseparable Simultaneous Equations Models 1

Conditions for the Existence of Control Functions in Nonseparable Simultaneous Equations Models 1 Conditions for the Existence of Control Functions in Nonseparable Simultaneous Equations Models 1 Richard Blundell UCL and IFS and Rosa L. Matzkin UCLA First version: March 2008 This version: October 2010

More information

Parametric identification of multiplicative exponential heteroskedasticity ALYSSA CARLSON

Parametric identification of multiplicative exponential heteroskedasticity ALYSSA CARLSON Parametric identification of multiplicative exponential heteroskedasticity ALYSSA CARLSON Department of Economics, Michigan State University East Lansing, MI 48824-1038, United States (email: carls405@msu.edu)

More information

Purpose and Objectives. Grading. Office Hours. G-83 McCarty Mon. & Wed. 3:00-4:30. Web Site

Purpose and Objectives. Grading. Office Hours. G-83 McCarty Mon. & Wed. 3:00-4:30. Web Site ECONOMETRIC METHODS I AEB 6571 Spring 2007 Instructor: R. D. Emerson remerson@ufl.edu 392-1881 x 316 Purpose and Objectives The purpose of the course is to introduce the student to current econometric

More information

E c o n o m e t r i c s

E c o n o m e t r i c s H:/Lehre/Econometrics Master/Lecture slides/chap 0.tex (October 7, 2015) E c o n o m e t r i c s This course 1 People Instructor: Professor Dr. Roman Liesenfeld SSC-Gebäude, Universitätsstr. 22, Room 4.309

More information

A Note on Adapting Propensity Score Matching and Selection Models to Choice Based Samples

A Note on Adapting Propensity Score Matching and Selection Models to Choice Based Samples DISCUSSION PAPER SERIES IZA DP No. 4304 A Note on Adapting Propensity Score Matching and Selection Models to Choice Based Samples James J. Heckman Petra E. Todd July 2009 Forschungsinstitut zur Zukunft

More information

Birkbeck Working Papers in Economics & Finance

Birkbeck Working Papers in Economics & Finance ISSN 1745-8587 Birkbeck Working Papers in Economics & Finance Department of Economics, Mathematics and Statistics BWPEF 1809 A Note on Specification Testing in Some Structural Regression Models Walter

More information

Efficient Estimation of Non-Linear Dynamic Panel Data Models with Application to Smooth Transition Models

Efficient Estimation of Non-Linear Dynamic Panel Data Models with Application to Smooth Transition Models Efficient Estimation of Non-Linear Dynamic Panel Data Models with Application to Smooth Transition Models Tue Gørgens Christopher L. Skeels Allan H. Würtz February 16, 2008 Preliminary and incomplete.

More information

A Robust Test for Weak Instruments in Stata

A Robust Test for Weak Instruments in Stata A Robust Test for Weak Instruments in Stata José Luis Montiel Olea, Carolin Pflueger, and Su Wang 1 First draft: July 2013 This draft: November 2013 Abstract We introduce and describe a Stata routine ivrobust

More information

The method of moments approach to parameter estimation dates back

The method of moments approach to parameter estimation dates back Journal of Economic Perspectives Volume 15, Number 4 Fall 2001 Pages 87 100 Applications of Generalized Method of Moments Estimation Jeffrey M. Wooldridge The method of moments approach to parameter estimation

More information

Introduction to Eco n o m et rics

Introduction to Eco n o m et rics 2008 AGI-Information Management Consultants May be used for personal purporses only or by libraries associated to dandelon.com network. Introduction to Eco n o m et rics Third Edition G.S. Maddala Formerly

More information

WHITNEY K. NEWEY July 2017

WHITNEY K. NEWEY July 2017 WHITNEY K. NEWEY July 2017 PERSONAL DATA Address: Department of Economics Massachusetts Institute of Technology Cambridge, MA 02139 Telephone: (617) 253-6420, Fax: (617) 253-1330, Email: wnewey@mit.edu.

More information

Identification in Nonparametric Limited Dependent Variable Models with Simultaneity and Unobserved Heterogeneity

Identification in Nonparametric Limited Dependent Variable Models with Simultaneity and Unobserved Heterogeneity Identification in Nonparametric Limited Dependent Variable Models with Simultaneity and Unobserved Heterogeneity Rosa L. Matzkin 1 Department of Economics University of California, Los Angeles First version:

More information

A CONDITIONAL LIKELIHOOD RATIO TEST FOR STRUCTURAL MODELS. By Marcelo J. Moreira 1

A CONDITIONAL LIKELIHOOD RATIO TEST FOR STRUCTURAL MODELS. By Marcelo J. Moreira 1 Econometrica, Vol. 71, No. 4 (July, 2003), 1027 1048 A CONDITIONAL LIKELIHOOD RATIO TEST FOR STRUCTURAL MODELS By Marcelo J. Moreira 1 This paper develops a general method for constructing exactly similar

More information

Panel Data: Fixed and Random Effects

Panel Data: Fixed and Random Effects Short Guides to Microeconometrics Fall 2016 Kurt Schmidheiny Unversität Basel Panel Data: Fixed and Random Effects 1 Introduction In panel data, individuals (persons, firms, cities, ) are observed at several

More information

Imbens/Wooldridge, Lecture Notes 13, Summer 07 1

Imbens/Wooldridge, Lecture Notes 13, Summer 07 1 Imbens/Wooldridge, Lecture Notes 13, Summer 07 1 What s New in Econometrics NBER, Summer 2007 Lecture 13, Wednesday, Aug 1st, 2.00-3.00pm Weak Instruments and Many Instruments 1. Introduction In recent

More information

1 Estimation of Persistent Dynamic Panel Data. Motivation

1 Estimation of Persistent Dynamic Panel Data. Motivation 1 Estimation of Persistent Dynamic Panel Data. Motivation Consider the following Dynamic Panel Data (DPD) model y it = y it 1 ρ + x it β + µ i + v it (1.1) with i = {1, 2,..., N} denoting the individual

More information

A Course on Advanced Econometrics

A Course on Advanced Econometrics A Course on Advanced Econometrics Yongmiao Hong The Ernest S. Liu Professor of Economics & International Studies Cornell University Course Introduction: Modern economies are full of uncertainties and risk.

More information

Parametric Identification of Multiplicative Exponential Heteroskedasticity

Parametric Identification of Multiplicative Exponential Heteroskedasticity Parametric Identification of Multiplicative Exponential Heteroskedasticity Alyssa Carlson Department of Economics, Michigan State University East Lansing, MI 48824-1038, United States Dated: October 5,

More information

New Developments in Econometrics Lecture 16: Quantile Estimation

New Developments in Econometrics Lecture 16: Quantile Estimation New Developments in Econometrics Lecture 16: Quantile Estimation Jeff Wooldridge Cemmap Lectures, UCL, June 2009 1. Review of Means, Medians, and Quantiles 2. Some Useful Asymptotic Results 3. Quantile

More information

The Exact Distribution of the t-ratio with Robust and Clustered Standard Errors

The Exact Distribution of the t-ratio with Robust and Clustered Standard Errors The Exact Distribution of the t-ratio with Robust and Clustered Standard Errors by Bruce E. Hansen Department of Economics University of Wisconsin October 2018 Bruce Hansen (University of Wisconsin) Exact

More information

A Course in Applied Econometrics Lecture 18: Missing Data. Jeff Wooldridge IRP Lectures, UW Madison, August Linear model with IVs: y i x i u i,

A Course in Applied Econometrics Lecture 18: Missing Data. Jeff Wooldridge IRP Lectures, UW Madison, August Linear model with IVs: y i x i u i, A Course in Applied Econometrics Lecture 18: Missing Data Jeff Wooldridge IRP Lectures, UW Madison, August 2008 1. When Can Missing Data be Ignored? 2. Inverse Probability Weighting 3. Imputation 4. Heckman-Type

More information

Generated Covariates in Nonparametric Estimation: A Short Review.

Generated Covariates in Nonparametric Estimation: A Short Review. Generated Covariates in Nonparametric Estimation: A Short Review. Enno Mammen, Christoph Rothe, and Melanie Schienle Abstract In many applications, covariates are not observed but have to be estimated

More information

A Simple Estimator for Binary Choice Models With Endogenous Regressors

A Simple Estimator for Binary Choice Models With Endogenous Regressors A Simple Estimator for Binary Choice Models With Endogenous Regressors Yingying Dong and Arthur Lewbel University of California Irvine and Boston College Revised June 2012 Abstract This paper provides

More information

Quantile methods. Class Notes Manuel Arellano December 1, Let F (r) =Pr(Y r). Forτ (0, 1), theτth population quantile of Y is defined to be

Quantile methods. Class Notes Manuel Arellano December 1, Let F (r) =Pr(Y r). Forτ (0, 1), theτth population quantile of Y is defined to be Quantile methods Class Notes Manuel Arellano December 1, 2009 1 Unconditional quantiles Let F (r) =Pr(Y r). Forτ (0, 1), theτth population quantile of Y is defined to be Q τ (Y ) q τ F 1 (τ) =inf{r : F

More information

CURRICULUM VITAE. December Robert M. de Jong Tel: (614) Ohio State University Fax: (614)

CURRICULUM VITAE. December Robert M. de Jong Tel: (614) Ohio State University Fax: (614) CURRICULUM VITAE December 2011 Robert M. de Jong Tel: (614) 292-2051 Ohio State University Fax: (614) 292-3906 Department of Economics Email: de-jong.8@osu.edu 444 Arps Hall Columbus, Ohio 43210, USA PERSONAL

More information

Nonparametric Econometrics

Nonparametric Econometrics Applied Microeconometrics with Stata Nonparametric Econometrics Spring Term 2011 1 / 37 Contents Introduction The histogram estimator The kernel density estimator Nonparametric regression estimators Semi-

More information

Identification and Estimation Using Heteroscedasticity Without Instruments: The Binary Endogenous Regressor Case

Identification and Estimation Using Heteroscedasticity Without Instruments: The Binary Endogenous Regressor Case Identification and Estimation Using Heteroscedasticity Without Instruments: The Binary Endogenous Regressor Case Arthur Lewbel Boston College Original December 2016, revised July 2017 Abstract Lewbel (2012)

More information

ESTIMATION OF TREATMENT EFFECTS VIA MATCHING

ESTIMATION OF TREATMENT EFFECTS VIA MATCHING ESTIMATION OF TREATMENT EFFECTS VIA MATCHING AAEC 56 INSTRUCTOR: KLAUS MOELTNER Textbooks: R scripts: Wooldridge (00), Ch.; Greene (0), Ch.9; Angrist and Pischke (00), Ch. 3 mod5s3 General Approach The

More information

A Course in Applied Econometrics Lecture 7: Cluster Sampling. Jeff Wooldridge IRP Lectures, UW Madison, August 2008

A Course in Applied Econometrics Lecture 7: Cluster Sampling. Jeff Wooldridge IRP Lectures, UW Madison, August 2008 A Course in Applied Econometrics Lecture 7: Cluster Sampling Jeff Wooldridge IRP Lectures, UW Madison, August 2008 1. The Linear Model with Cluster Effects 2. Estimation with a Small Number of roups and

More information

What s New in Econometrics? Lecture 14 Quantile Methods

What s New in Econometrics? Lecture 14 Quantile Methods What s New in Econometrics? Lecture 14 Quantile Methods Jeff Wooldridge NBER Summer Institute, 2007 1. Reminders About Means, Medians, and Quantiles 2. Some Useful Asymptotic Results 3. Quantile Regression

More information

New Developments in Econometrics Lecture 11: Difference-in-Differences Estimation

New Developments in Econometrics Lecture 11: Difference-in-Differences Estimation New Developments in Econometrics Lecture 11: Difference-in-Differences Estimation Jeff Wooldridge Cemmap Lectures, UCL, June 2009 1. The Basic Methodology 2. How Should We View Uncertainty in DD Settings?

More information

ECONOMICS 7200 MODERN TIME SERIES ANALYSIS Econometric Theory and Applications

ECONOMICS 7200 MODERN TIME SERIES ANALYSIS Econometric Theory and Applications ECONOMICS 7200 MODERN TIME SERIES ANALYSIS Econometric Theory and Applications Yongmiao Hong Department of Economics & Department of Statistical Sciences Cornell University Spring 2019 Time and uncertainty

More information

The New Palgrave Dictionary of Economics Online

The New Palgrave Dictionary of Economics Online The New Palgrave Dictionary of Economics Online identification Jean-Marie Dufour Cheng Hsiao From The New Palgrave Dictionary of Economics, Second Edition, 2008 Edited by Steven N. Durlauf and Lawrence

More information

ORTHOGONALITY TESTS IN LINEAR MODELS SEUNG CHAN AHN ARIZONA STATE UNIVERSITY ABSTRACT

ORTHOGONALITY TESTS IN LINEAR MODELS SEUNG CHAN AHN ARIZONA STATE UNIVERSITY ABSTRACT ORTHOGONALITY TESTS IN LINEAR MODELS SEUNG CHAN AHN ARIZONA STATE UNIVERSITY ABSTRACT This paper considers several tests of orthogonality conditions in linear models where stochastic errors may be heteroskedastic

More information

Random Coefficients on Endogenous Variables in Simultaneous Equations Models

Random Coefficients on Endogenous Variables in Simultaneous Equations Models Review of Economic Studies (207) 00, 6 0034-6527/7/0000000$02.00 c 207 The Review of Economic Studies Limited Random Coefficients on Endogenous Variables in Simultaneous Equations Models MATTHEW A. MASTEN

More information

13 Endogeneity and Nonparametric IV

13 Endogeneity and Nonparametric IV 13 Endogeneity and Nonparametric IV 13.1 Nonparametric Endogeneity A nonparametric IV equation is Y i = g (X i ) + e i (1) E (e i j i ) = 0 In this model, some elements of X i are potentially endogenous,

More information

A Simple Estimator for Binary Choice Models With Endogenous Regressors

A Simple Estimator for Binary Choice Models With Endogenous Regressors A Simple Estimator for Binary Choice Models With Endogenous Regressors Yingying Dong and Arthur Lewbel University of California Irvine and Boston College Revised February 2012 Abstract This paper provides

More information

CAUSALITY AND EXOGENEITY IN ECONOMETRICS

CAUSALITY AND EXOGENEITY IN ECONOMETRICS EDITORS INTRODUCTION CAUSALITY AND EXOGENEITY IN ECONOMETRICS Luc Bauwens CORE and Department of Economics, Université catholique de Louvain bauwens@core.ucl.ac.be H. Peter Boswijk Department of Quantitative

More information

ADVANCED ECONOMETRICS I. Course Description. Contents - Theory 18/10/2017. Theory (1/3)

ADVANCED ECONOMETRICS I. Course Description. Contents - Theory 18/10/2017. Theory (1/3) ADVANCED ECONOMETRICS I Theory (1/3) Instructor: Joaquim J. S. Ramalho E.mail: jjsro@iscte-iul.pt Personal Website: http://home.iscte-iul.pt/~jjsro Office: D5.10 Course Website: http://home.iscte-iul.pt/~jjsro/advancedeconometricsi.htm

More information

Pseudo panels and repeated cross-sections

Pseudo panels and repeated cross-sections Pseudo panels and repeated cross-sections Marno Verbeek November 12, 2007 Abstract In many countries there is a lack of genuine panel data where speci c individuals or rms are followed over time. However,

More information

Instrumental Variables Estimation in Stata

Instrumental Variables Estimation in Stata Christopher F Baum 1 Faculty Micro Resource Center Boston College March 2007 1 Thanks to Austin Nichols for the use of his material on weak instruments and Mark Schaffer for helpful comments. The standard

More information

ivporbit:an R package to estimate the probit model with continuous endogenous regressors

ivporbit:an R package to estimate the probit model with continuous endogenous regressors MPRA Munich Personal RePEc Archive ivporbit:an R package to estimate the probit model with continuous endogenous regressors Taha Zaghdoudi University of Jendouba, Faculty of Law Economics and Management

More information

Location Properties of Point Estimators in Linear Instrumental Variables and Related Models

Location Properties of Point Estimators in Linear Instrumental Variables and Related Models Location Properties of Point Estimators in Linear Instrumental Variables and Related Models Keisuke Hirano Department of Economics University of Arizona hirano@u.arizona.edu Jack R. Porter Department of

More information

Christopher Dougherty London School of Economics and Political Science

Christopher Dougherty London School of Economics and Political Science Introduction to Econometrics FIFTH EDITION Christopher Dougherty London School of Economics and Political Science OXFORD UNIVERSITY PRESS Contents INTRODU CTION 1 Why study econometrics? 1 Aim of this

More information

A Note on Bootstraps and Robustness. Tony Lancaster, Brown University, December 2003.

A Note on Bootstraps and Robustness. Tony Lancaster, Brown University, December 2003. A Note on Bootstraps and Robustness Tony Lancaster, Brown University, December 2003. In this note we consider several versions of the bootstrap and argue that it is helpful in explaining and thinking about

More information

NONPARAMETRIC IDENTIFICATION AND ESTIMATION OF TRANSFORMATION MODELS. [Preliminary and Incomplete. Please do not circulate.]

NONPARAMETRIC IDENTIFICATION AND ESTIMATION OF TRANSFORMATION MODELS. [Preliminary and Incomplete. Please do not circulate.] NONPARAMETRIC IDENTIFICATION AND ESTIMATION OF TRANSFORMATION MODELS PIERRE-ANDRE CHIAPPORI, IVANA KOMUNJER, AND DENNIS KRISTENSEN Abstract. This paper derives sufficient conditions for nonparametric transformation

More information

Estimation and Inference with Weak Identi cation

Estimation and Inference with Weak Identi cation Estimation and Inference with Weak Identi cation Donald W. K. Andrews Cowles Foundation Yale University Xu Cheng Department of Economics University of Pennsylvania First Draft: August, 2007 Revised: March

More information

Econometrics I. Professor William Greene Stern School of Business Department of Economics 1-1/40. Part 1: Introduction

Econometrics I. Professor William Greene Stern School of Business Department of Economics 1-1/40. Part 1: Introduction Econometrics I Professor William Greene Stern School of Business Department of Economics 1-1/40 http://people.stern.nyu.edu/wgreene/econometrics/econometrics.htm 1-2/40 Overview: This is an intermediate

More information

The Identification Zoo - Meanings of Identification in Econometrics: PART 3

The Identification Zoo - Meanings of Identification in Econometrics: PART 3 The Identification Zoo - Meanings of Identification in Econometrics: PART 3 Arthur Lewbel Boston College original 2015, heavily revised 2018 Lewbel (Boston College) Identification Zoo 2018 1 / 85 The Identification

More information

CONTENTS OF THE HANDBOOK VOLUME 1. Part 1: MATHEMATICAL AND STATISTICAL METHODS IN ECONOMETRICS. Part 2: ECONOMETRIC MODELS

CONTENTS OF THE HANDBOOK VOLUME 1. Part 1: MATHEMATICAL AND STATISTICAL METHODS IN ECONOMETRICS. Part 2: ECONOMETRIC MODELS CONTENTS OF THE HANDBOOK VOLUME 1 Part 1: MATHEMATICAL AND STATISTICAL METHODS IN ECONOMETRICS Chapter 1 Linear Algebra and Matrix Methods in Econometrics HENRI THEIL Chapter 2 Statistical Theory and Econometrics

More information

Lecture 8 Panel Data

Lecture 8 Panel Data Lecture 8 Panel Data Economics 8379 George Washington University Instructor: Prof. Ben Williams Introduction This lecture will discuss some common panel data methods and problems. Random effects vs. fixed

More information

Conducting Multivariate Analyses of Social, Economic, and Political Data

Conducting Multivariate Analyses of Social, Economic, and Political Data Conducting Multivariate Analyses of Social, Economic, and Political Data ICPSR Summer Program Concordia Workshops May 25-29, 2015 Dr. Harold D. Clarke University of Texas, Dallas hclarke@utdallas.edu Dr.

More information

A nonparametric test for path dependence in discrete panel data

A nonparametric test for path dependence in discrete panel data A nonparametric test for path dependence in discrete panel data Maximilian Kasy Department of Economics, University of California - Los Angeles, 8283 Bunche Hall, Mail Stop: 147703, Los Angeles, CA 90095,

More information

ECONOMETRICS II (ECO 2401S) University of Toronto. Department of Economics. Spring 2013 Instructor: Victor Aguirregabiria

ECONOMETRICS II (ECO 2401S) University of Toronto. Department of Economics. Spring 2013 Instructor: Victor Aguirregabiria ECONOMETRICS II (ECO 2401S) University of Toronto. Department of Economics. Spring 2013 Instructor: Victor Aguirregabiria SOLUTION TO FINAL EXAM Friday, April 12, 2013. From 9:00-12:00 (3 hours) INSTRUCTIONS:

More information

Discussion Paper Series

Discussion Paper Series INSTITUTO TECNOLÓGICO AUTÓNOMO DE MÉXICO CENTRO DE INVESTIGACIÓN ECONÓMICA Discussion Paper Series Size Corrected Power for Bootstrap Tests Manuel A. Domínguez and Ignacio N. Lobato Instituto Tecnológico

More information

A Local Generalized Method of Moments Estimator

A Local Generalized Method of Moments Estimator A Local Generalized Method of Moments Estimator Arthur Lewbel Boston College June 2006 Abstract A local Generalized Method of Moments Estimator is proposed for nonparametrically estimating unknown functions

More information

An Overview of the Special Regressor Method

An Overview of the Special Regressor Method An Overview of the Special Regressor Method Arthur Lewbel Boston College September 2012 Abstract This chapter provides background for understanding and applying special regressor methods. This chapter

More information

Wild Bootstrap Inference for Wildly Dierent Cluster Sizes

Wild Bootstrap Inference for Wildly Dierent Cluster Sizes Wild Bootstrap Inference for Wildly Dierent Cluster Sizes Matthew D. Webb October 9, 2013 Introduction This paper is joint with: Contributions: James G. MacKinnon Department of Economics Queen's University

More information

Do Markov-Switching Models Capture Nonlinearities in the Data? Tests using Nonparametric Methods

Do Markov-Switching Models Capture Nonlinearities in the Data? Tests using Nonparametric Methods Do Markov-Switching Models Capture Nonlinearities in the Data? Tests using Nonparametric Methods Robert V. Breunig Centre for Economic Policy Research, Research School of Social Sciences and School of

More information

DEEP, University of Lausanne Lectures on Econometric Analysis of Count Data Pravin K. Trivedi May 2005

DEEP, University of Lausanne Lectures on Econometric Analysis of Count Data Pravin K. Trivedi May 2005 DEEP, University of Lausanne Lectures on Econometric Analysis of Count Data Pravin K. Trivedi May 2005 The lectures will survey the topic of count regression with emphasis on the role on unobserved heterogeneity.

More information

Research Statement. Zhongwen Liang

Research Statement. Zhongwen Liang Research Statement Zhongwen Liang My research is concentrated on theoretical and empirical econometrics, with the focus of developing statistical methods and tools to do the quantitative analysis of empirical

More information

Identification and Estimation Using Heteroscedasticity Without Instruments: The Binary Endogenous Regressor Case

Identification and Estimation Using Heteroscedasticity Without Instruments: The Binary Endogenous Regressor Case Identification and Estimation Using Heteroscedasticity Without Instruments: The Binary Endogenous Regressor Case Arthur Lewbel Boston College December 2016 Abstract Lewbel (2012) provides an estimator

More information

Instructor: Dr. Darryl Kropf 203 S. Biology ; please put cell biology in subject line

Instructor: Dr. Darryl Kropf 203 S. Biology ; please put cell biology in subject line Biology 2020 PRINCIPLES OF CELL BIOLOGY Spring 2004 In Principles of Cell Biology, we will explore the structure, function, and evolution of living cells, including prokaryotes (archae and eubacteria)

More information

AMSC/MATH 673, CLASSICAL METHODS IN PDE, FALL Required text: Evans, Partial Differential Equations second edition

AMSC/MATH 673, CLASSICAL METHODS IN PDE, FALL Required text: Evans, Partial Differential Equations second edition AMSC/MATH 673, CLASSICAL METHODS IN PDE, FALL 2018. MWF 2:00pm - 2:50pm MTH 0407 Instructor: M. Machedon Office: MTH 3311 e-mail: mxm@math.umd.edu Required text: Evans, Partial Differential Equations second

More information

Correct Specification and Identification

Correct Specification and Identification THE MILTON FRIEDMAN INSTITUTE FOR RESEARCH IN ECONOMICS MFI Working Paper Series No. 2009-003 Correct Specification and Identification of Nonparametric Transformation Models Pierre-Andre Chiappori Columbia

More information

Interpreting Tests of School VAM Validity

Interpreting Tests of School VAM Validity Interpreting Tests of School VAM Validity By Joshua Angrist, Peter Hull, Parag Pathak, and Christopher Walters Public school districts increasingly look to the estimates generated by value-added models

More information