Structural Econometric Modeling in Industrial Organization Handout 1


 Camilla Spencer
 1 years ago
 Views:
Transcription
1 Structural Econometric Modeling in Industrial Organization Handout 1 Professor Matthijs Wildenbeest 16 May
2 Reading Peter C. Reiss and Frank A. Wolak A. Structural Econometric Modeling: Rationales and Examples from Industrial Organization. Handbook of Econometrics 6A, Chapter 64, Sections 14,
3 Background on Empirical IO Structural versus nonstructural econometrics Constructing structural models Framework for structural econometrics models in IO 3
4 Structural versus Nonstructural Econometrics Example: auctions Suppose we observe winning bids, y = {y 1,..., y T }, in a large number of T similar auctions, as well as the number of bidders in each market, x = {x 1,..., x T }. Goal exercise: understand equilibrium relationship between winning bids and the number of firms. Nonstructural approach: regress winning bids on the number of bidders. use nonparametric smoothing techniques to estimate the conditional density of winning bids given the observed number of bidders, i.e., f (y x). Does the regression coefficient tell us what happens when we add another bidder? Not without further knowledge about the auction under study. For instance, information paradigm matters. 4
5 Structural versus Nonstructural Econometrics 5
6 Structural versus Nonstructural Econometrics 6
7 Structural versus Nonstructural Econometrics Structural approach: Use the structure of an auction model to say something about winning bids and the number of firms. For example, Paarsch (1992, j econometrics) shows that for firstprice sealedbid auctions with Paretodistributed private value bidders, the conditional density of winning bids given the number of firms f (y x) is f (y x, θ) = θ [ ] 2x θ1 θ 2 (x 1) θ2 x y θ, 2x+1 θ 2 (x 1) 1 so that the expected value of the winning bid given the number of bidder is [ ] θ1 θ 2 (x 1) θ 2 x E(y x, θ) = θ 2 (x 1) 1 θ 2 x 1. 7
8 Structural versus Nonstructural Econometrics Why use economic theory in this example? Helps us to clarify how institutional and economic conditions affect the relationship between x and y. Think of type of auction (sealedbid versus openoutcry or firstprice versus secondprice), bidder behavior (risk neutral versus risk averse), and information paradigm (common versus private values). Three general reasons for specifying and estimating a structural econometric model: 1 Estimate unobserved parameters that could not otherwise be inferred from the data (costs, elasticities, valuations). 2 Perform counterfactuals or policy experiments. 3 Compare the predictive performance of two competing theories. 8
9 Structural versus Nonstructural Econometrics Although using structural econometrics has many advantages, this does not always mean structural models should be favored over nonstructural models. Think of a situation where there little or no useful economic theory to guide the empirical work. Levitt (1997, am econ rev): using electoral cycles in police hiring to estimate the effect of police on crime. Studies the effect of police on reducing crime. Previous studies found little evidence, likely due to simultaneity problems. Levitt proposes a new instrument: timing of elections. Effects the size of the police force, but does not belong directly to the crime production function. 9
10 Constructing Structural Models Sources of structure 1 economics 2 statistics Since economic models are often deterministic we have to add statistical structure to rationalize why economic theory does not perfectly explain the data. 10
11 Constructing Structural Models Example Crosssection data on output, Q i, labor inputs, L i, and capital inputs K i. Estimate the regression ln Q i = θ 0 + θ 1 ln L i + θ 2 ln K i + ɛ i, by ordinary least squares (OLS). Error term ɛ i necessary because right hand side variables do not perfectly explain log output. Interpretation? Best Linear Predictor (BLP) of ln Q i given a constant, ln L i and ln K i : only statistical structure needed (sample second moments converge to their population counterparts). Estimation of CobbDouglas production function: structure needed from both economics and statistics. 11
12 Constructing Structural Models Only structure from economics not enough to estimate (logarithmic transformation) of CobbDouglas production function Q i = AL α i K β i : we have to add an error term as well: Q i = AL α i K β i exp ɛ i. Where does the error term come from? If ɛ i is measurement error distributed independently of the right hand side variables the estimated OLS parameters can be interpreted as the coefficient of the CobbDouglas production function. Moreover, firms should produce on their production function. Note that if the error includes unobserved differences in productivity, OLS fails to deliver consistent estimates of the production function parameters. 12
13 Constructing Structural Models Linear regression model y = α + xβ + ɛ. From a statistical perspective we can always regress y on x (or the other way around): the coefficients have statistical interpretations (Best Linear Predictor). However, we need economic arguments to make a case about causation. Moreover, without an economic model the OLS regression only gives (under certain conditions) consistent estimates of a best linear predictor function. 13
14 Constructing Structural Models Usually not possible to test a deterministic economic model by running a regression. Many descriptive studies treat the linear regression coefficient estimates as as if they were estimates of the derivative of E(y x) with respect to x, although β = BLP(y x)/ x is usually not equal to E(y x)/ x. 14
15 Constructing Structural Models Nonexperimental data raises significant modeling issues. Estimating the demand curve q d t = γ 0 + γ 1 p t + γ 2 x 1t + ɛ 1t by OLS only gives consistent estimates of the demand curve parameters if price p t and a demand shifter like income x 1t are uncorrelated with the error ɛ 1t. If we perform experiments where we randomly select prices and observe the quantity demanded this will work. Same for the supply curve q s t = β 0 + β 1 p t + β 2 x 2t + ɛ 2t, where x 2t is now a supply shifter like input prices. 15
16 Constructing Structural Models In the experiments the quantity supplied will in general not be equal to the quantity demanded. However, no problem since we observe the quantity demand and supplied directly for each randomly generated price. Prices around us are nonexperimental. OLS no longer possible because of correlations between explanatory variables and error term. But if we use economics and impose the marketclearing equation q s t = q d t, we could apply instrumental variable techniques to get consistent estimates of the simultaneous equation model. 16
17 Constructing Structural Models Simultaneous equations models When dealing with endogeneity it is important to think about a complete simultaneous equations model. Example Researcher estimates: p i = POP i θ 1 + COMP i θ 2 + ɛ i, where p i is the price in market i, POP i is population size, and COMP i is a dummy for whether the firm faces competition. Has this equation a structural meaning? Could be: θ 2 measures effect of competition on prices. 17
18 Constructing Structural Models Simultaneous equations models Problem: COMP i is likely to depend on p i : COMP i = POP i γ 1 + p i γ 2 + η i. Therefore COMP i will be correlated with ɛ i, so OLS will give inconsistent estimates of θ 2. Possible solution: use average income Y i as instrument for COMP i, since one can argue Y i is correlated with COMP i but not with ɛ i. Statistical rationale. 18
19 Constructing Structural Models Simultaneous equations models To be completely convincing two things need to be done: 1 explain why Y i is not part of p i. 2 make the case that Y i is part of COMP i. Therefore, specify the complete system: p i = POP i θ 1 + COMP i θ 2 + ɛ i ; COMP i = POP i γ 1 + p i γ 2 + Y i γ 3 + η i. This requires the researcher to think carefully about the economic model underlying the simultaneous system of equations. 19
20 Framework for Structural Econometrics Models in IO A structural model has two main components: 1 economic model; 2 stochastic model. The economic model should have the following components: description of economic environment (market, actors, information available); list of primitives (technologies, preferences, endowments); exogenous variables (variables outside the model); decision variables and objective functions (utility/profit maximization); equilibrium concept (nash equilibrium) 20
21 Framework for Structural Econometrics Models in IO The stochastic model transforms the (usually) deterministic economic model into an econometric model. Main difference between the two is inclusion of unobservables. Major stochastic specifications: unobserved heterogeneity agent uncertainty optimization errors measurement error Different forms can have dramatically different implications for identification and estimation! 21
22 Framework for Structural Econometrics Models in IO Unobserved heterogeneity Situation where agents decisions depend on something the economist does not observe. Agent uncertainty Situation where agents decisions depend on something the agent does not (fully) observe. Note that in both cases the econometrician is ignorant. Still, they can have different implications. 22
23 Framework for Structural Econometrics Models in IO Example Crosssection data on firms consisting of output Q, total costs TC, and input prices p K and p L. Goal is to estimate α and β in Q i = A i L α i K β i. Suppose a regulator chooses a price pi r and that firms have different A i, the latter being observed by the firm and regulator but not by the econometrician. Assume inelastic demand. Firm chooses inputs to maximize π(k i, L i ) = p r i A i L α i K β i p Ki K i p Li L i. 23
24 Framework for Structural Econometrics Models in IO Firms produce in a cost minimizing way, so This means i K β i MP L = A iαl α 1 i MP K A i βl α i K β 1 = α β K i = p Li p Ki β α L i. K i L i = p Li p Ki. Substituting this into the production function gives [ ] pli β β [ ] Q i = A i p Ki α L i L α pli β β i = A i L α+β i, p Ki α and solving for L i gives L i = Q 1 α+β i A ( ) β 1 α+β pli i p Ki α+β ( β α ) β α+β 24
25 Framework for Structural Econometrics Models in IO The total labor cost p Li L i is then given by p Li L i = C L p γ Ki p1 γ Li Qi δ A δ i, where δ = 1/(α + β), γ = β/(α + β), and C L = (α/β) γ. Similarly, the total capital cost p Ki K i is given by where C K = (α/β) γ 1. p Ki K i = C K p γ Ki p1 γ Li Qi δ A δ i, The total cost function is therefore where C 0 = C L + C K. TC i = C 0 p γ Ki p1 γ Li Qi δ A δ i, 25
26 Framework for Structural Econometrics Models in IO Transforming this equation using natural logarithms gives ln TC i = ln C 0 + γ ln p Ki + (1 γ) ln p Li + δ ln Q i δ ln A i, which holds exactly. The efficiency differences are assumed to be i.i.d. positive random variables, so subtracting E[ln A i ] from the error term and adding it to the constant gives ln TC i = ln C 1 + γ ln p Ki + (1 γ) ln p Li + δ ln Q i δ ln u i, where ln C 1 = ln C 0 + E[ln A i ] and ln u i = ln A i E[ln A i ]. This equation can finally be taken to the data using OLS. 26
27 Framework for Structural Econometrics Models in IO Now suppose the firms (and the regulator) do not know the efficiency parameters A i either. Firms now choose inputs to maximize E[π(K i, L i )] = p r i E[A i L α i K β i ] p Ki K i p Li L i. Firstorder condition for expected profit maximization imply [ ] α p Ki L i = K i. β p Li Observed total costs are and do not depend on A i. TC i = α + β β p KiK i = α + β α p LiL i, 27
28 Framework for Structural Econometrics Models in IO This means L i = α β+α TC i/p Li and K i = β β+α TC i/p Ki, so Q a i = D 0 TC α+β i p β Ki p α Li A i. Final output produced Qi a logarithms gives does depend on A i.taking natural ln Q a i = ln D 0 + (α + β) ln TC i β ln p Ki α ln p Li + ln A i, which holds exactly. Researcher does not observe A i, so treat as random and move unconditional expectation again to the constant: ln Q a i = D 1 + (α + β) ln TC i β ln p Ki α ln p Li + η i, where η i = ln A i E[ln A i ] and D 1 = ln D 0 + E[ln A i ]. 28
29 Framework for Structural Econometrics Models in IO Optimization errors Failure of agents decisions to satisfy exactly firstorder necessary conditions for optimal decisions. Measurement errors Occurs when the variable the research observes are different from those the agents observe. Straightforward way of converting a deterministic model into a statistical model. 29
30 Framework for Structural Econometrics Models in IO Steps left 1 selection of functional forms; 2 selection of distributional assumptions; 3 selection of an estimation technique; and 4 selection of specification test. 30
STRUCTURAL ECONOMETRIC MODELING: RATIONALES AND EXAMPLES FROM INDUSTRIAL ORGANIZATION
Chapter 64 STRUCTURAL ECONOMETRIC MODELING: RATIONALES AND EXAMPLES FROM INDUSTRIAL ORGANIZATION PETER C. REISS Graduate School of Business, Stanford University, Stanford, CA 943055015, USA email: preiss@optimum.stanford.edu
More informationStructural Econometric Modeling: Rationales and Examples from Industrial Organization
Structural Econometric Modeling: Rationales and Examples from Industrial Organization by Peter C. Reiss Frank A. Wolak Graduate School of Business Department of Economics Stanford University Stanford University
More informationStructural Econometric Modeling: Rationales and Examples from Industrial Organization
Structural Econometric Modeling: Rationales and Examples from Industrial Organization by Peter C. Reiss Frank A. Wolak Graduate School of Business Department of Economics Stanford University Stanford University
More informationSimultaneous Equation Models As discussed last week, one important form of endogeneity is simultaneity. This arises when one or more of the
Simultaneous Equation Models As discussed last week, one important form of endogeneity is simultaneity. This arises when one or more of the explanatory variables is jointly determined with the dependent
More informationSimultaneous Equations
Simultaneous Equations y 1 = α 1 y 2 + β 1 z 1 + u 1 y 2 = α 2 y 1 + β 2 z 2 + u 2 Economics 20  Prof. Schuetze 1 Simultaneity Simultaneity is a specific type of endogeneity problem Here, the explanatory
More information1 Why demand analysis/estimation?
Lecture notes: Demand estimation introduction 1 1 Why demand analysis/estimation? Estimation of demand functions is an important empirical endeavor. Why? Fundamental empircial question: how much market
More information1 Demand Estimation. Empirical Problem Set # 2 Graduate Industrial Organization, Fall 2005 Glenn Ellison and Stephen Ryan
Empirical Problem Set # 2 Graduate Industrial Organization, Fall 2005 Glenn Ellison and Stephen Ryan 1 Demand Estimation The intent of this problem set is to get you familiar with Stata, if you are not
More information4 Alternative Gravity Model Estimators. 4 Alternative Gravity Model Estimators
4 Alternative Gravity Model Estimators 49 4 Alternative Gravity Model Estimators The previous section primarily used OLS as the estimation methodology for a variety of gravity models, both intuitive and
More informationMarkups and FirmLevel Export Status: Appendix
Markups and FirmLevel Export Status: Appendix De Loecker Jan  Warzynski Frederic Princeton University, NBER and CEPR  Aarhus School of Business Forthcoming American Economic Review Abstract This is
More informationHandout 7: Business Cycles
University of British Columbia Department of Economics, Macroeconomics (Econ 502) Prof. Amartya Lahiri Handout 7: Business Cycles We now use the methods that we have introduced to study modern business
More informationEconometrics. Week 9. Fall Institute of Economic Studies Faculty of Social Sciences Charles University in Prague
Econometrics Week 9 Institute of Economic Studies Faculty of Social Sciences Charles University in Prague Fall 2012 1 / 21 Recommended Reading For the today Simultaneous Equations Models Chapter 16 (pp.
More informationFinancial Market Microstructure Theory
The Microstructure of Financial Markets, de Jong and Rindi (2009) Financial Market Microstructure Theory Based on de Jong and Rindi, Chapters 2 5 Frank de Jong Tilburg University 1 Determinants of the
More informationproblem arises when only a nonrandom sample is available differs from censored regression model in that x i is also unobserved
4 Data Issues 4.1 Truncated Regression population model y i = x i β + ε i, ε i N(0, σ 2 ) given a random sample, {y i, x i } N i=1, then OLS is consistent and efficient problem arises when only a nonrandom
More informationECON20310 LECTURE SYNOPSIS REAL BUSINESS CYCLE
ECON20310 LECTURE SYNOPSIS REAL BUSINESS CYCLE YUAN TIAN This synopsis is designed merely for keep a record of the materials covered in lectures. Please refer to your own lecture notes for all proofs.
More information1. Suppose demand for a monopolist s product is given by P = 300 6Q
Solution for June, Micro Part A Each of the following questions is worth 5 marks. 1. Suppose demand for a monopolist s product is given by P = 300 6Q while the monopolist s marginal cost is given by MC
More informationINDIRECT INFERENCE (prepared for: The New Palgrave Dictionary of Economics, Second Edition)
INDIRECT INFERENCE (prepared for: The New Palgrave Dictionary of Economics, Second Edition) Abstract Indirect inference is a simulationbased method for estimating the parameters of economic models. Its
More informationVI. Real Business Cycles Models
VI. Real Business Cycles Models Introduction Business cycle research studies the causes and consequences of the recurrent expansions and contractions in aggregate economic activity that occur in most industrialized
More informationThe CobbDouglas Production Function
171 10 The CobbDouglas Production Function This chapter describes in detail the most famous of all production functions used to represent production processes both in and out of agriculture. First used
More informationControl Functions and Simultaneous Equations Methods
Control Functions and Simultaneous Equations Methods By RICHARD BLUNDELL, DENNIS KRISTENSEN AND ROSA L. MATZKIN Economic models of agent s optimization problems or of interactions among agents often exhibit
More information1. The Classical Linear Regression Model: The Bivariate Case
Business School, Brunel University MSc. EC5501/5509 Modelling Financial Decisions and Markets/Introduction to Quantitative Methods Prof. Menelaos Karanasos (Room SS69, Tel. 018956584) Lecture Notes 3 1.
More informationSOLUTIONS Problem Set 1: BLP Demand Estimation
SOLUTIONS Problem Set 1: BLP Demand Estimation Matt Grennan November 15, 2007 These are my attempt at the first problem set for the second year Ph.D. IO course at NYU with Heski BarIsaac and Allan CollardWexler
More informationEstimates of Individual Dairy Farm Supply Elasticities
Estimates of Individual Dairy Farm Supply Elasticities Loren W. Tauer This is Working Paper, WP9808, of the Department of Agriculture, Resource, and Managerial Economics, Cornell University, July 1998.
More informationEconometrics: Models with Endogenous Explanatory Variables
Econometrics: Models with Endogenous Explanatory Variables Burcu Eke UC3M Endogeneity Given the following linear regression model: Y = β 0 + β 1 X 1 + β 2 X 2 +... + β k X k + ε If E [ε X 1, X 2,... X
More informationDepartment of Economics California State University, Fullerton Econ 315, Sample Midterm 2
Department of Economics California State University, Fullerton Econ 315, Sample Midterm 2 Please answer all the questions. 1. If the t ratio for the slope of a simple linear regression equation is equal
More informationWelcome! Lecture 1: Introduction. Course structure. Examination. Econometrics, 7.5hp. Textbook. Chapter 1: The Nature and Scope of Econometrics
Basic Econometrics Lecture 1: Introduction Welcome! This is the first lecture on the course Econometrics, 7.5hp STGA02 & NEGB22 Iris wang iris.wang@kau.se Textbook Gujarati, D. N. (2003) Basic Econometrics
More informationEconometrics Simple Linear Regression
Econometrics Simple Linear Regression Burcu Eke UC3M Linear equations with one variable Recall what a linear equation is: y = b 0 + b 1 x is a linear equation with one variable, or equivalently, a straight
More informationAn Introduction to Time Series Regression
An Introduction to Time Series Regression Henry Thompson Auburn University An economic model suggests examining the effect of exogenous x t on endogenous y t with an exogenous control variable z t. In
More informationNOTES AND PROBLEMS IDENTIFICATION IN TRIANGULAR SYSTEMS USING CONTROL FUNCTIONS
Econometric Theory, 2011, Page 1 of 9. doi:10.1017/s0266466610000460 NOTES AND PROBLEMS IDENTIFICATION IN TRIANGULAR SYSTEMS USING CONTROL FUNCTIONS MAXIMILIAN KASY University of California, Berkeley This
More information. This specification assumes individual specific means with Ey ( ) = µ. We know from Nickell (1981) that OLS estimates of (1.1) are biased for S S
UNIT ROOT TESTS WITH PANEL DATA. Consider the AR model y = αy ( ), it it + α µ + ε i it i =,... N, t =,..., T. (.) where the ε 2 IN(0, σ ) it. This specification assumes individual specific means with
More informationChapter 10: Basic Linear Unobserved Effects Panel Data. Models:
Chapter 10: Basic Linear Unobserved Effects Panel Data Models: Microeconomic Econometrics I Spring 2010 10.1 Motivation: The Omitted Variables Problem We are interested in the partial effects of the observable
More informationNew Developments in Econometrics Lecture 6: Nonlinear Panel Data Models
New Developments in Econometrics Lecture 6: Nonlinear Panel Data Models Jeff Wooldridge Cemmap Lectures, UCL, June 2009 1. Introduction 2. General Setup and Quantities of Interest 3. Exogeneity Assumptions
More informationModels for Count Data With Overdispersion
Models for Count Data With Overdispersion Germán Rodríguez November 6, 2013 Abstract This addendum to the WWS 509 notes covers extrapoisson variation and the negative binomial model, with brief appearances
More informationECON 497: Lecture Notes PD Page 1 of 9
ECON 497: Lecture Notes PD Page 1 of 9 Metropolitan State University ECON 497: Research and Forecasting Lecture Notes PD Note: This lecture is based on material found in two books. The first is Understanding
More informationIMPACT EVALUATION: INSTRUMENTAL VARIABLE METHOD
REPUBLIC OF SOUTH AFRICA GOVERNMENTWIDE MONITORING & IMPACT EVALUATION SEMINAR IMPACT EVALUATION: INSTRUMENTAL VARIABLE METHOD SHAHID KHANDKER World Bank June 2006 ORGANIZED BY THE WORLD BANK AFRICA IMPACT
More informationAdvanced International Trade Problem Set
Advanced International Trade Problem Set Question 1: Consider an economy which is inhabited by a representative consumer who offers inelastically L units of labor and derives utility from two consumption
More informationIntroduction to Labour Economics
Chapter 1 Introduction to Labour Economics McGrawHill/Irwin Labor Economics, 4 th edition Copyright 2008 The McGrawHill Companies, Inc. All rights reserved. 12 Some General Course Information Discussion
More informationThe Classical Linear Model and OLS Estimation
Econ 507. Econometric Analysis. Spring 2009 January 19, 2009 The Classical Linear Model Social sciences: nonexact relationships. Starting point: a model for the nonexact relationship between y (explained
More informationTrade Flows and Trade Policy Analysis. October 2013 Dhaka, Bangladesh
Trade Flows and Trade Policy Analysis October 2013 Dhaka, Bangladesh Witada Anukoonwattaka (ESCAP) Cosimo Beverelli (WTO) 1 Selected econometric methodologies and STATA applications 2 Content a) Classical
More informationInstrumental Variables Regression. Instrumental Variables (IV) estimation is used when the model has endogenous s.
Instrumental Variables Regression Instrumental Variables (IV) estimation is used when the model has endogenous s. IV can thus be used to address the following important threats to internal validity: Omitted
More informationECON 5110 Class Notes Overview of New Keynesian Economics
ECON 5110 Class Notes Overview of New Keynesian Economics 1 Introduction The primary distinction between Keynesian and classical macroeconomics is the flexibility of prices and wages. In classical models
More informationPartial Equilibrium: Positive Analysis
Partial Equilibrium: Positive Analysis This Version: November 28, 2009 First Version: December 1, 2008. In this Chapter we consider consider the interaction between different agents and firms, and solve
More informationECO 2901 EMPIRICAL INDUSTRIAL ORGANIZATION
ECO 2901 EMPIRICAL INDUSTRIAL ORGANIZATION Lecture 1: Introduction to the Course Victor Aguirregabiria (University of Toronto) Toronto. Winter 2016 Victor Aguirregabiria () Empirical IO Toronto. Winter
More informationLOGIT AND PROBIT ANALYSIS
LOGIT AND PROBIT ANALYSIS A.K. Vasisht I.A.S.R.I., Library Avenue, New Delhi 110 012 amitvasisht@iasri.res.in In dummy regression variable models, it is assumed implicitly that the dependent variable Y
More informationInstrumental Variables & 2SLS
Instrumental Variables & 2SLS y = β 0 + β 1 x 1 + β 2 x 2 +... β k x k + u x 1 = π 0 + π 1 z + π 2 x 2 +... π k x k + v Why Use Instrumental Variables? Instrumental Variables (IV) estimation is used when
More informationRegression III: Advanced Methods
Lecture 5: Linear leastsquares Regression III: Advanced Methods William G. Jacoby Department of Political Science Michigan State University http://polisci.msu.edu/jacoby/icpsr/regress3 Simple Linear Regression
More informationECONOMETRICS TOPICS. Chapter 1: An Overview of Regression Analysis
ECONOMETRICS TOPICS Chapter 1: An Overview of Regression Analysis What is econometrics? Econometrics: economic measurement Uses of econometrics: 1. Describing economic reality 2. Testing hypothesis about
More informationNext Tuesday: Amit Gandhi guest lecture on empirical work on auctions Next Wednesday: first problem set due
Econ 805 Advanced Micro Theory I Dan Quint Fall 2007 Lecture 6 Sept 25 2007 Next Tuesday: Amit Gandhi guest lecture on empirical work on auctions Next Wednesday: first problem set due Today: the pricediscriminating
More informationEconometrics Final Exam Solutions Universidad Carlos III de Madrid May 26th, 2015
Econometrics Final Exam Solutions Universidad Carlos III de Madrid May 26th, 2015 Answer all questions in two hours and a half. QUESTION 1 (33 marks: A researcher is considering two regression specifications
More informationSimultaneous Equations
Simultaneous Equations 1 Motivation and Examples Now we relax the assumption that EX u 0 his wil require new techniques: 1 Instrumental variables 2 2 and 3stage least squares 3 Limited LIML and full
More informationIntroduction to Auctions
Introduction to Auctions Lots of good theory and empirical work. game is simple with wellspecified rules actions are observed directly payoffs can sometimes be inferred Also, lots of data. government
More informationHOW EFFECTIVE IS TARGETED ADVERTISING?
HOW EFFECTIVE IS TARGETED ADVERTISING? Ayman Farahat and Michael Bailey Marketplace Architect Yahoo! July 28, 2011 Thanks Randall Lewis, Yahoo! Research Agenda An Introduction to Measuring Effectiveness
More informationEconometric Analysis of Cross Section and Panel Data Second Edition. Jeffrey M. Wooldridge. The MIT Press Cambridge, Massachusetts London, England
Econometric Analysis of Cross Section and Panel Data Second Edition Jeffrey M. Wooldridge The MIT Press Cambridge, Massachusetts London, England Preface Acknowledgments xxi xxix I INTRODUCTION AND BACKGROUND
More informationMediation.  Mediation can refer to a single mediator variable or multiple mediator variables.
Fundamentals  refers to the transmission of the effect of an independent variable on a dependent variable through one or more other variables. These variables are termed mediator or intervening variables.
More informationABSOLUTE RISK AVERSION
(short notes from Wiki complied by Ekta Grover :) Measures of risk aversion ABSOLUTE RISK AVERSION The higher the curvature of u(c), the higher the risk aversion. However, since expected utility functions
More information1 The Problem: Endogeneity There are two kinds of variables in our models: exogenous variables and endogenous variables. Endogenous Variables: These a
Notes on Simultaneous Equations and Two Stage Least Squares Estimates Copyright  Jonathan Nagler; April 19, 1999 1. Basic Description of 2SLS ffl The endogeneity problem, and the bias of OLS. ffl The
More informationStochastic Structural Dynamics Prof. Dr. C. S. Manohar Department of Civil Engineering Indian Institute of Science, Bangalore
Stochastic Structural Dynamics Prof. Dr. C. S. Manohar Department of Civil Engineering Indian Institute of Science, Bangalore (Refer Slide Time: 00:23) Lecture No. # 3 Scalar random variables2 Will begin
More informationOn the Efficiency of Competitive Stock Markets Where Traders Have Diverse Information
Finance 400 A. Penati  G. Pennacchi Notes on On the Efficiency of Competitive Stock Markets Where Traders Have Diverse Information by Sanford Grossman This model shows how the heterogeneous information
More informationOn Marginal Effects in Semiparametric Censored Regression Models
On Marginal Effects in Semiparametric Censored Regression Models Bo E. Honoré September 3, 2008 Introduction It is often argued that estimation of semiparametric censored regression models such as the
More informationEstimating the random coefficients logit model of demand using aggregate data
Estimating the random coefficients logit model of demand using aggregate data David Vincent Deloitte Economic Consulting London, UK davivincent@deloitte.co.uk September 14, 2012 Introduction Estimation
More informationYou are to answer any combination of questions that sum to exactly 200 points.
Instructions The questions below are arranged into two groups according to the estimated time and difficulty. Group A are relatively short, should require 5 10 minute each, and are worth 25 points each.
More informationECON 459 Game Theory. Lecture Notes Auctions. Luca Anderlini Spring 2015
ECON 459 Game Theory Lecture Notes Auctions Luca Anderlini Spring 2015 These notes have been used before. If you can still spot any errors or have any suggestions for improvement, please let me know. 1
More information241B Lecture Application: Returns to Scale in Electricity Markets
241B Lecture Application: Returns to Scale in Electricity Markets We work from Nerlove (1963): a classic study of returns to scale in a regulated industry. The Electricity Supply Industry In 1963, the
More informationConditional Logit Models
Conditional Logit Models James J. Heckman University of Chicago Econ 312 This draft, March 22, 2006 1 The Weibull Distribution Suppose is i.i.d. Weibull. Then the CDF of is given as Pr( ) () exp( exp (
More information2. If the explained sum of squares is 35 and the total sum of squares is 49, what is the residual sum of squares? a. 10 b. 12 c. 18 d.
Chapter 3 A. Multiple Choice Questions 1. Consider the following regression equation: y β β x β x u. What does β 1 imply? a. β measures the ceteris paribus effect of x on x. b. β measures the ceteris paribus
More informationECON 8010 (Spring 2012) Exam 1
ECON 81 (Spring 212) Exam 1 Name A. Key Multiple Choice Questions: (4 points each) 1. Since August 211, there has been a 5% decrease in price and a 8% increase in quantity traded of Good X. Which of the
More informationII Review of the Literature
A Model for Estimating the Value Added of the Life Insurance Market in Egypt: An Empirical Study Dr. N. M. Habib Associate Professor University of Maryland Eastern Shore Abstract The paper is an attempt
More informationESTIMATING AN ECONOMIC MODEL OF CRIME USING PANEL DATA FROM NORTH CAROLINA BADI H. BALTAGI*
JOURNAL OF APPLIED ECONOMETRICS J. Appl. Econ. 21: 543 547 (2006) Published online in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/jae.861 ESTIMATING AN ECONOMIC MODEL OF CRIME USING PANEL
More informationA new test for monopoly with limited cost data
Department of Economics Working Paper A new test for monopoly with limited cost data Charles C. Moul Miami University August 2011 Working Paper #  201103 A new test for monopoly with limited cost data
More informationEconometrics II. Lecture Notes 2
Econometrics II. Lecture Notes 2 SIMULTANEOUS LINEAR EQUATIONS SYSTEMS 1. Identification 2. Estimation 3. Identification with crossequation 4. Identification with covariance restrictions 5. Models nonlinear
More informationCHAPTER 6. SIMULTANEOUS EQUATIONS
Economics 24B Daniel McFadden 1999 1. INTRODUCTION CHAPTER 6. SIMULTANEOUS EQUATIONS Economic systems are usually described in terms of the behavior of various economic agents, and the equilibrium that
More informationEquilibrium is the Intersection of Supply and Demand Curves. Partial Equilibrium Analysis. Price Setting. Supply and Demand Elasticities
rof. Jay Bhattacharya Econ 11Lecture 16 artial Analysis We have now analyzed the intricate workings of market supply and demand curves from the bottom up. Today, we will put together these curves to
More informationInstrumental Variables (IV) Instrumental Variables (IV) is a method of estimation that is widely used
Instrumental Variables (IV) Instrumental Variables (IV) is a method of estimation that is widely used in many economic applications when correlation between the explanatory variables and the error term
More informationNotes 10: An Equation Based Model of the Macroeconomy
Notes 10: An Equation Based Model of the Macroeconomy In this note, I am going to provide a simple mathematical framework for 8 of the 9 major curves in our class (excluding only the labor supply curve).
More informationSimultaneous Equations Models. Sanjaya DeSilva
Simultaneous Equations Models Sanjaya DeSilva 1 Reduced Form and Structural Models We will begin with definitions; 1. Exogenous variables are variables that are determined outside of the model. For the
More informationECON 5010 Class Notes Basic Model, Calibration, Solution and Simulation
RBC Theory: ECON 500 Class Notes Basic Model, Calibration, Solution and Simulation Introduction In this section, I present the details of a basic real business cycle (RBC) model. I rely heavily on Prescott
More informationProductioin OVERVIEW. WSG5 7/7/03 4:35 PM Page 63. Copyright 2003 by Academic Press. All rights of reproduction in any form reserved.
WSG5 7/7/03 4:35 PM Page 63 5 Productioin OVERVIEW This chapter reviews the general problem of transforming productive resources in goods and services for sale in the market. A production function is the
More informationChapter 6. Econometrics. 6.1 Introduction. 6.2 Univariate techniques Transforming data
Chapter 6 Econometrics 6.1 Introduction We re going to use a few tools to characterize the time series properties of macro variables. Today, we will take a relatively atheoretical approach to this task,
More informationShould Instrumental Variables be Used as Matching Variables?
Should Instrumental Variables be Used as Matching Variables? Jeffrey M. Wooldridge Michigan State University September 2006 This version: July 2009 Abstract: I show that for estimating a constant treatment
More informationA Guide to Modern Econometrics / 2ed. Answers to selected exercises  Chapter 5
A Guide to Modern Econometrics / 2ed Answers to selected exercises  Chapter 5 Exercise 5.1 a. The essential conditions for b to be unbiased for are that Ef" i g = 0 and that f" 1 ; :::; " N g is independent
More information17 The Economics of Revenue Sharing: Theory and Measurement
17 The Economics of Revenue Sharing: Theory and Measurement The accompanying case, The Home Video Industry, discusses the evolution of the home video industry. One important event in the industry s history
More informationL1: Dealing with Reverse Causation: Simultaneous Equation Modelling
L: Dealing with Reverse Causation: Simultaneous Equation Modelling Prof Gwilym Pryce AQIM Training June 2006 Introduction Social Science Statistics I & II: We have assumed only one dependent variable,
More informationSolution to Selected Questions: CHAPTER 12 MONOPOLISTIC COMPETITION AND OLIGOPOLY
Chulalongkorn University: BBA International Program, Faculty of Commerce and Accountancy 900 (Section ) Chairat Aemkulwat Economics I: Microeconomics Spring 05 Solution to Selected Questions: CHAPTER MONOPOLISTIC
More informationMultivariable Calculus and Optimization
Multivariable Calculus and Optimization Dudley Cooke Trinity College Dublin Dudley Cooke (Trinity College Dublin) Multivariable Calculus and Optimization 1 / 51 EC2040 Topic 3  Multivariable Calculus
More information14.461: Technological Change, Lecture 2 Knowledge Spillovers and Diffusion
14.461: Technological Change, Lecture 2 Knowledge Spillovers and Diffusion Daron Acemoglu MIT September 10, 2013. Daron Acemoglu (MIT) Knowledge Spillovers and Diffusion September 10, 2013. 1 / 36 Introduction
More informationDiscussion of AradillasLopez and Tamer
Discussion of AradillasLopez and Tamer Allan CollardWexler New York University February 8, 2008 1 A simple AradillasLopez and Tamer estimator To illustrate the power and ease of AradillasLopez and
More informationSharing Online Advertising Revenue with Consumers
Sharing Online Advertising Revenue with Consumers Yiling Chen 2,, Arpita Ghosh 1, Preston McAfee 1, and David Pennock 1 1 Yahoo! Research. Email: arpita, mcafee, pennockd@yahooinc.com 2 Harvard University.
More informationTopic 5: Stochastic Growth and Real Business Cycles
Topic 5: Stochastic Growth and Real Business Cycles Yulei Luo SEF of HKU October 1, 2015 Luo, Y. (SEF of HKU) Macro Theory October 1, 2015 1 / 45 Lag Operators The lag operator (L) is de ned as Similar
More informationTopic 1  Introduction to Labour Economics. Professor H.J. Schuetze Economics 370. What is Labour Economics?
Topic 1  Introduction to Labour Economics Professor H.J. Schuetze Economics 370 What is Labour Economics? Let s begin by looking at what economics is in general Study of interactions between decision
More information1 Logit & Probit Models for Binary Response
ECON 370: Limited Dependent Variable 1 Limited Dependent Variable Econometric Methods, ECON 370 We had previously discussed the possibility of running regressions even when the dependent variable is dichotomous
More informationLecture 2 Dynamic Equilibrium Models : Finite Periods
Lecture 2 Dynamic Equilibrium Models : Finite Periods 1. Introduction In macroeconomics, we study the behavior of economywide aggregates e.g. GDP, savings, investment, employment and so on  and their
More informationSOCIAL AND NONMARKET BENEFITS FROM EDUCATION IN AN ADVANCED ECONOMY
Discussion SOCIAL AND NONMARKET BENEFITS FROM EDUCATION IN AN ADVANCED ECONOMY T. Paul Schultz* The objective of the paper by Barbara Wolfe and Robert Haveman is to assign a monetary value to the welfare
More informationTopic 1 Simultaneous Equations I: Introduction
ECONOMETRICS II Topic 1 Simultaneous Equations I: Introduction These slides are copyright 2010 by Tavis Barr. This work is licensed under a Creative Commons Attribution ShareAlike 3.0 Unported License.
More informationECONOMETRIC THEORY. MODULE I Lecture  1 Introduction to Econometrics
ECONOMETRIC THEORY MODULE I Lecture  1 Introduction to Econometrics Dr. Shalabh Department of Mathematics and Statistics Indian Institute of Technology Kanpur 2 Econometrics deals with the measurement
More informationAggregate Uncertainty: Krusell and Smith
Aggregate Uncertainty: Krusell and Smith Econ720 Prof. Lutz Hendricks November 22, 2016 1 / 31 A Bewley Model of the Wealth Distribution We study Krusell and Smith (1998 JPE). The problem: In models with
More informationUNIVERSITY OF WAIKATO. Hamilton New Zealand
UNIVERSITY OF WAIKATO Hamilton New Zealand Can We Trust ClusterCorrected Standard Errors? An Application of Spatial Autocorrelation with Exact Locations Known John Gibson University of Waikato Bonggeun
More informationCUBS MSc Finance ASSET MANAGEMENT: LECTURE 4. Dr. D.N. Tambakis ARBITRAGE PRICING THEORY (APT)
CUBS MSc Finance 19992000 ASSET MANAGEMENT: LECTURE 4 Dr. D.N. Tambakis ARBITRAGE PRICING THEORY (APT) Textbook: Readings: EG16, BKM10, GT6 Shanken, J. The Arbitrage Pricing Theory: Is it Testable?, The
More informationMgmt 469. Maximum Likelihood Estimation
Mgmt 469 Maximum Likelihood Estimation In regression analysis, you estimate the parameters of an equation linking predictor variables X to a dependent variable Y. Your goal is the find the parameters the
More informationLabor Demand. Labor Economics VSE Praha March 2009
Labor Demand Labor Economics VSE Praha March 2009 Labor Economics: Outline Labor Supply Labor Demand Equilibrium in Labor Market et cetera Labor Demand Model: Firms Firm s role in: Labor Market consumes
More informationSOLUTIONS EC302  INTERMEDIATE MICROECONOMICS Loyola University Fall Problem Set 4
1 SOLUTIONS EC302  INTERMEDIATE MICROECONOMICS Loyola University Fall 2016 Problem Set 4 1. A firm produces output q in a competitive industry that is in long run equilibrium. Now, suppose that an output
More information3 Estimating the Gravity Model. 3 Estimating the Gravity Model
3 Estimating the Gravity Model 25 3 Estimating the Gravity Model This section addresses some of the basic econometric issues that arise when estimating gravity models in practice. It first uses the intuitive
More information