Microeconometrics I - JCM029

• Title: Mikroekonometrie I
• Guaranteed by: CERGE (23-CERGE)
• Faculty: Faculty of Social Sciences
• Actual: from 2022
• Semester: winter
• E-Credits: 9
• Examination process: winter s.:
• Hours per week, examination: winter s.:4/2, Ex [HT]
• Capacity: 1 / unknown (20)
• Min. number of students: unlimited
• 4EU+: no
• Virtual mobility / capacity: no
• State of the course: taught
• Language: Czech
• Teaching methods: full-time
• Teaching methods: full-time
• Note: course can be enrolled in outside the study plan; enabled for web enrollment; priority enrollment if the course is part of the study plan
• Guarantor: prof. Ing. Štěpán Jurajda, Ph.D.
• Teacher(s): Paolo Zacchia, Ph.D.
• Incompatibility : JCM042
• Pre-requisite : JCM002, JCM017, JCM021
• Is incompatible with: JCM042
• Is pre-requisite for: JCM030, JCM053

Descriptors

Last update: Mgr. Anna Papariga (15.09.2022)

This is a graduate level course on selected topics in applied econometrics. Following a review of some econometric concepts and tools that are relevant for the so-called “structural” models, the course covers a broad menu of econometric methods that were originally devised for estimation in a specific domain of microeconomics. Emphasis is however placed on tools with broader applicability in current research.

 

Literature

Last update: Mgr. Anna Papariga (15.09.2022)

This list of readings is split by topic. Additional references not listed below will be mentioned in class.

 

“Theory: structure, identification and causality.”

·         Main material: class notes prepared by the lecturer and made available to students.

·         Bruce E. Hansen (2022), Econometrics, working draft available online on the author’s website.

 

“Tools: discrete choice methods with simulations.”

·         A. Colin Cameron and Pravin K. Trivedi (2013), Microeconometrics: Methods and Applications. Cambridge University Press.

·         Kenneth Train (2009), Discrete Choice Methods with Simulation, Cambridge University Press.

 

“Demand: estimation of random utility models.”

·         Steven T. Berry (1994), Estimating Discrete-choice Models of Product Differentiation, The RAND Journal of Economics, 25(2), 242-262.

·         Steven T. Berry, James A. Levinshon and Ariel Pakes, (1995), Automobile Prices in Market Equi-librium, Econometrica, 63(4), 841-890.

·         Timothy F. Bresnahan (1987), Competition and Collusion in the American Automobile Industry: The 1955 Price War, Journal of Industrial Economics, 35(4), 457-482.

·         Laurits R. Christensen, Dale W. Jorgenson and Lawrence J. Lau (1976), Transcendental Logarith-mic Utility Functions, American Economic Review, 65(3), 367-383.

·         Angus Deaton and John Muellbauer (1980), An Almost Ideal Demand System, American Economic Review, 70(3), 312-326.

·         Aviv Nevo (2001), Measuring Market Power in the Ready-to-Eat Cereal Industry, Econometrica, 69(2), 307-342.

 

“Supply: estimation of production functions.”

·         Daniel A. Ackerberg, Kevin Caves, and Garth Frazer (2015), Identification Properties of Recent Production Function Estimators, Econometrica, 83(6), 2411-2451.

·         Richard Blundell and Stephen Bond (2000), GMM Estimation with persistent panel data: an appli-cation to production functions, Econometric Reviews, 19(3), 321-340.

·         Amit Gandhi, Salvador Navarro and David A. Rivers (2020), On the Identification of Gross Out-put Production Functions, Journal of Political Economy, 128(8), 2973-3016.

·         Jan De Loecker (2011), Product Differentiation, Multiproduct Firms, and Estimating the Impact of Trade Liberalization on Productivity, Econometrica, 79(5) 1407-1451.

·         Jan De Loecker and Frederic Warzynski (2012), Markups and Firm-Level Export Status, American Economic Review, 102(6) 2437-2471.

·         James A. Levinshon and Ariel Petrin, (2003), Estimating Production Functions Using Inputs to Control for Unobservables, The Review of Economic Studies, 70(2), 317-341.

·         G. Steven Olley and Ariel Pakes (1996), The Dynamics of Productivity in the Telecommunications Equipment Industry, Econometrica, 64(6), 1996, 1263-1297.

·         Jeffrey M. Wooldridge (2009), On estimating firm-level production functions using proxy vari-ables to control for unobservables, Economic Letters, 104(3), 112-114.

“Games: estimation of strategic choices.”

·         Victor Aguirregabiria and Pedro Mira (2007), Sequential Estimation of Dynamic Discrete Games, Econometrica, 75(1), 1-53.

·         Timothy F. Bresnahan and Peter C. Reiss (1991), Entry and Competition in Concentrated Markets. Journal of Political Economy, 99(5), 977-1009.

·         Steven T. Berry (1992), Estimation of a Model of Entry in the Airline Industry, Econometrica, 60(4), 889-917.

·         Federico Ciliberto and Elie Tamer (2009), Market Structure and Multiple Equilibria in Airline Markets, Econometrica, 77(6), 1791-1828.

·         Andrew Sweeting (2009), The strategic timing incentives of commercial radio stations: An empiri-cal analysis using multiple equilibria, 40(4), 710-742.

·         Elie Tamer (2003), Incomplete Simultaneous Discrete Response Model with Multiple Equilibria, The Review of Economic Studies, 70(1), 147-175.

 

“Labor market: wage decomposition.”

·         John M. Abowd, Francis Kramarz and David N. Margolis (1999), High Wage Workers and High Wage Firms, Econometrica, 67(2), 251-333.

·         Martyn J. Andrews, Leonard Gill, Thorsten Schank and Richard Upward (2008), High wage wor-kers and low wage firms: negative assortative matching or limited mobility bias?, Journal of the Royal Statistical Society A, 171(3), 673-697.

·         Martyn J. Andrews, Leonard Gill, Thorsten Schank and Richard Upward (2012), High wage wor-kers match with high wage firms: Clear evidence of the effects of limited mobility bias?, Econo-mics Letters, 117(3), 824-827.

·         Stéphane Bonhomme, Thibaut Lamadon and Elena Manresa (2019), A Distributional Framework for Matched Employer Employee Data, Econometrica, 87(3), 699-739.

·         David Card, Ana Rute Cardoso, Jörg Heining and Patrick Kline (2018), Firms and Labor Market Inequality: Evidence and Some Theory, Journal of Labor Economics, 36(S1), S13-S70.

·         David Card, Jörg Heining and Patrick Kline (2013), Workplace Heterogeneity and the Rise of West German Wage Inequality, Quarterly Journal of Economics, 128(3), 967-1015.

·         Mons Chan, Sergio Salgado and Ming Xu (2020), Heterogeneous passthrough from TFP to wages, available at SSRN 3538503.

·         Sabrina Di Addario, Patrick Kline, Raffaele Saggio and Mikkel Sølvsten (2020), It Ain’t Where You’re From, It’s Where You’re At: Hiring Origins, Firm Heterogeneity, and Wages, forthcoming at the Journal of Econometrics.

·         Patrick Kline, Raffaele Saggio and Mikkel Sølvsten (2020), Leave-Out Estimation of Variance Components, Econometrica, 88(5), 1859-1898.

 

“Interactions: econometrics of networks.”

·         Yann Bramoullé, Habiba Djebbari and Bernard Fortin (2009), Identification of Peer Effects through Social Networks, Journal of Econometrics, 150(1), 41-55.

·         Yann Bramoullé, Habiba Djebbari and Bernard Fortin (2020), Peer Effects in Networks: A Survey, Annual Review of Economics, 12, 603-629.

·         Giacomo De Giorgi, Michele Pellizzari, and Silvia Redaelli (2010), Identification of Social Inter-actions through Partially Overlapping Peer Groups, American Economic Journal: Applied Econo-mics, Vol. 2, No. 2, pp. 241–275.

·         Áureo de Paula, Imran Rasul and Pedro C. Souza (2020), Identifying network ties from panel data: theory and an application to tax competition, CeMMAP Working Paper 55/19.

·         Bryan Graham (2008), Identifying Social Interactions Through Conditional Variance Restrictions, Econometrica, 76(3), 643-660.

·         Bryan Graham (2017), An Econometric Model of Network Formation with Degree Heterogeneity, Econometrica, 85(4), 1033-1063.

·         Charles F. Manski (1993), Identification of Endogenous Social Effects: The Reflection Problem,

Requirements to the exam

Last update: Mgr. Anna Papariga (15.09.2022)

To receive a final evaluation for this course, attendants are expected to submit an econometric exercise (inclusive of original code, and commentary or explanatory short paper) on a topic agreed in advance. The exercise may consist of a paper replication, or of an original analysis with real or simulated data. It is expected that the exercise is in line with a student’s research interests; it would be desirable if it were related to a research project or proposal to be further expanded in the Research Methodology Seminar, in a later course, and possibly in the dissertation stage. The grade will be based on the evaluation of the econometric exercise and, to a lesser extent, of some minor assignments handed during the course.

Syllabus

Last update: Mgr. Anna Papariga (15.09.2022)

·         Concepts: structure, identification, causality

1. Identification in structural models
2. Linear simultaneous equations models
3. Causality in structural models

 

·         Tools: discrete choice methods with simulations

1. Random parameters and simulations
2. Multinomial choice models with simulations
3. Discrete choice models for panel data

 

·         Demand: estimation of random utility models

·         Traditional approaches to demand estimation

·         The workhorse Berry-Levinsohn-Pakes model

·         Refinements: panel data and demographics

 

·         Supply: estimation of production functions

·         The transmission bias and panel data solutions

·         Varieties of control function approaches

·         A modern non-parametric treatment

 

·         Games: estimation of strategic choices

·         Traditional analyses of market entry games

·         Partial identification under equilibrium multiplicity

·         Extensions: incomplete information, dynamics

 

·         Labor market: wage decomposition

·         The workhorse Abowd-Kramarz-Margolis model

·         Addressing issues: event studies, leave-out estimation

·         Extensions: discretization, job history, productivity

 

·         Interactions: the econometrics of networks

·         The linear-in-means model and reflection

·         Identifying networks, identifying ‘the’ networks

·         Models of non-strategic network formation

Entry requirements

Last update: Mgr. Anna Papariga (15.09.2022)

Mathematical prerequisites

It is expected that students possess a solid understanding of graduate level microeconomics, statistics and econometrics as taught over the first year of the Ph.D. or Master’s program.