Structural Econometric Modeling in Industrial Organization Handout 1

Save this PDF as:
 WORD  PNG  TXT  JPG

Size: px
Start display at page:

Download "Structural Econometric Modeling in Industrial Organization Handout 1"

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 1-4,

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 first-price sealed-bid auctions with Pareto-distributed 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 (sealed-bid versus open-outcry or first-price versus second-price), 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 Cross-section 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 Cobb-Douglas 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 Cobb-Douglas 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 Cobb-Douglas 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 market-clearing 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 Cross-section 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. First-order 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 first-order 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

Markups and Firm-Level Export Status: Appendix

Markups and Firm-Level Export Status: Appendix Markups and Firm-Level 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 information

ECON20310 LECTURE SYNOPSIS REAL BUSINESS CYCLE

ECON20310 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 information

problem arises when only a non-random sample is available differs from censored regression model in that x i is also unobserved

problem arises when only a non-random 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 non-random

More information

Financial Market Microstructure Theory

Financial 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 information

VI. Real Business Cycles Models

VI. 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 information

On the Efficiency of Competitive Stock Markets Where Traders Have Diverse Information

On 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 information

Private Equity Fund Valuation and Systematic Risk

Private Equity Fund Valuation and Systematic Risk An Equilibrium Approach and Empirical Evidence Axel Buchner 1, Christoph Kaserer 2, Niklas Wagner 3 Santa Clara University, March 3th 29 1 Munich University of Technology 2 Munich University of Technology

More information

Chapter 10: Basic Linear Unobserved Effects Panel Data. Models:

Chapter 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 information

MISSING DATA TECHNIQUES WITH SAS. IDRE Statistical Consulting Group

MISSING DATA TECHNIQUES WITH SAS. IDRE Statistical Consulting Group MISSING DATA TECHNIQUES WITH SAS IDRE Statistical Consulting Group ROAD MAP FOR TODAY To discuss: 1. Commonly used techniques for handling missing data, focusing on multiple imputation 2. Issues that could

More information

Topic 5: Stochastic Growth and Real Business Cycles

Topic 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 information

Centre for Central Banking Studies

Centre for Central Banking Studies Centre for Central Banking Studies Technical Handbook No. 4 Applied Bayesian econometrics for central bankers Andrew Blake and Haroon Mumtaz CCBS Technical Handbook No. 4 Applied Bayesian econometrics

More information

1 Sufficient statistics

1 Sufficient statistics 1 Sufficient statistics A statistic is a function T = rx 1, X 2,, X n of the random sample X 1, X 2,, X n. Examples are X n = 1 n s 2 = = X i, 1 n 1 the sample mean X i X n 2, the sample variance T 1 =

More information

The Effect of Housing on Portfolio Choice. July 2009

The Effect of Housing on Portfolio Choice. July 2009 The Effect of Housing on Portfolio Choice Raj Chetty Harvard Univ. Adam Szeidl UC-Berkeley July 2009 Introduction How does homeownership affect financial portfolios? Linkages between housing and financial

More information

Sharing Online Advertising Revenue with Consumers

Sharing 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@yahoo-inc.com 2 Harvard University.

More information

ESTIMATING AN ECONOMIC MODEL OF CRIME USING PANEL DATA FROM NORTH CAROLINA BADI H. BALTAGI*

ESTIMATING 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 information

One Period Binomial Model

One Period Binomial Model FIN-40008 FINANCIAL INSTRUMENTS SPRING 2008 One Period Binomial Model These notes consider the one period binomial model to exactly price an option. We will consider three different methods of pricing

More information

HOW EFFECTIVE IS TARGETED ADVERTISING?

HOW 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 information

Intermediate Macroeconomics: The Real Business Cycle Model

Intermediate Macroeconomics: The Real Business Cycle Model Intermediate Macroeconomics: The Real Business Cycle Model Eric Sims University of Notre Dame Fall 2012 1 Introduction Having developed an operational model of the economy, we want to ask ourselves the

More information

Chapter 25: Exchange in Insurance Markets

Chapter 25: Exchange in Insurance Markets Chapter 25: Exchange in Insurance Markets 25.1: Introduction In this chapter we use the techniques that we have been developing in the previous 2 chapters to discuss the trade of risk. Insurance markets

More information

II- Review of the Literature

II- 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 information

The Real Business Cycle Model

The Real Business Cycle Model The Real Business Cycle Model Ester Faia Goethe University Frankfurt Nov 2015 Ester Faia (Goethe University Frankfurt) RBC Nov 2015 1 / 27 Introduction The RBC model explains the co-movements in the uctuations

More information

Risk Preferences and Demand Drivers of Extended Warranties

Risk Preferences and Demand Drivers of Extended Warranties Risk Preferences and Demand Drivers of Extended Warranties Online Appendix Pranav Jindal Smeal College of Business Pennsylvania State University July 2014 A Calibration Exercise Details We use sales data

More information

6. Budget Deficits and Fiscal Policy

6. Budget Deficits and Fiscal Policy Prof. Dr. Thomas Steger Advanced Macroeconomics II Lecture SS 2012 6. Budget Deficits and Fiscal Policy Introduction Ricardian equivalence Distorting taxes Debt crises Introduction (1) Ricardian equivalence

More information

The Real Business Cycle model

The Real Business Cycle model The Real Business Cycle model Spring 2013 1 Historical introduction Modern business cycle theory really got started with Great Depression Keynes: The General Theory of Employment, Interest and Money Keynesian

More information

Standard errors of marginal effects in the heteroskedastic probit model

Standard errors of marginal effects in the heteroskedastic probit model Standard errors of marginal effects in the heteroskedastic probit model Thomas Cornelißen Discussion Paper No. 320 August 2005 ISSN: 0949 9962 Abstract In non-linear regression models, such as the heteroskedastic

More information

Market Analysis SES 0549500. Lecture 8 Rena 14-15. October 9 &11. Office Markets. Presented by: Raymond G. Torto. Global Research and Consulting

Market Analysis SES 0549500. Lecture 8 Rena 14-15. October 9 &11. Office Markets. Presented by: Raymond G. Torto. Global Research and Consulting Market Analysis SES 0549500 Lecture 8 Rena 14-15 October 9 &11 Office Markets Presented by: Raymond G. Torto Exercise 2 Review: Effect of Price Increase in Asset Market Asset Market: Valuation Rent $ Space

More information

M1 in Economics and Economics and Statistics Applied multivariate Analysis - Big data analytics Worksheet 1 - Bootstrap

M1 in Economics and Economics and Statistics Applied multivariate Analysis - Big data analytics Worksheet 1 - Bootstrap Nathalie Villa-Vialanei Année 2015/2016 M1 in Economics and Economics and Statistics Applied multivariate Analsis - Big data analtics Worksheet 1 - Bootstrap This worksheet illustrates the use of nonparametric

More information

Who Pays the Social Security Tax? *

Who Pays the Social Security Tax? * Who Pays the Social Security Tax? * Haizheng Li School of Economics Georgia Institute of Technology Atlanta, GA 30332-0615 Phone: (404) 894-3542 Fax: (404) 894-1890 Email: haizheng.li@econ.gatech.edu *

More information

Conditional guidance as a response to supply uncertainty

Conditional guidance as a response to supply uncertainty 1 Conditional guidance as a response to supply uncertainty Appendix to the speech given by Ben Broadbent, External Member of the Monetary Policy Committee, Bank of England At the London Business School,

More information

Lecture 3: Linear methods for classification

Lecture 3: Linear methods for classification Lecture 3: Linear methods for classification Rafael A. Irizarry and Hector Corrada Bravo February, 2010 Today we describe four specific algorithms useful for classification problems: linear regression,

More information

MA Advanced Macroeconomics: 7. The Real Business Cycle Model

MA Advanced Macroeconomics: 7. The Real Business Cycle Model MA Advanced Macroeconomics: 7. The Real Business Cycle Model Karl Whelan School of Economics, UCD Spring 2015 Karl Whelan (UCD) Real Business Cycles Spring 2015 1 / 38 Working Through A DSGE Model We have

More information

A Basic Introduction to Missing Data

A Basic Introduction to Missing Data John Fox Sociology 740 Winter 2014 Outline Why Missing Data Arise Why Missing Data Arise Global or unit non-response. In a survey, certain respondents may be unreachable or may refuse to participate. Item

More information

Chapter 5 Estimating Demand Functions

Chapter 5 Estimating Demand Functions Chapter 5 Estimating Demand Functions 1 Why do you need statistics and regression analysis? Ability to read market research papers Analyze your own data in a simple way Assist you in pricing and marketing

More information

Graduate Macro Theory II: The Real Business Cycle Model

Graduate Macro Theory II: The Real Business Cycle Model Graduate Macro Theory II: The Real Business Cycle Model Eric Sims University of Notre Dame Spring 2011 1 Introduction This note describes the canonical real business cycle model. A couple of classic references

More information

Forecast. Forecast is the linear function with estimated coefficients. Compute with predict command

Forecast. Forecast is the linear function with estimated coefficients. Compute with predict command Forecast Forecast is the linear function with estimated coefficients T T + h = b0 + b1timet + h Compute with predict command Compute residuals Forecast Intervals eˆ t = = y y t+ h t+ h yˆ b t+ h 0 b Time

More information

Supplement to Call Centers with Delay Information: Models and Insights

Supplement to Call Centers with Delay Information: Models and Insights Supplement to Call Centers with Delay Information: Models and Insights Oualid Jouini 1 Zeynep Akşin 2 Yves Dallery 1 1 Laboratoire Genie Industriel, Ecole Centrale Paris, Grande Voie des Vignes, 92290

More information

Learning Objectives. Essential Concepts

Learning Objectives. Essential Concepts Learning Objectives After reading Chapter 7 and working the problems for Chapter 7 in the textbook and in this Workbook, you should be able to: Specify an empirical demand function both linear and nonlinear

More information

The RBC methodology also comes down to two principles:

The RBC methodology also comes down to two principles: Chapter 5 Real business cycles 5.1 Real business cycles The most well known paper in the Real Business Cycles (RBC) literature is Kydland and Prescott (1982). That paper introduces both a specific theory

More information

Predict the Popularity of YouTube Videos Using Early View Data

Predict the Popularity of YouTube Videos Using Early View Data 000 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 048 049 050

More information

Towards a Structuralist Interpretation of Saving, Investment and Current Account in Turkey

Towards a Structuralist Interpretation of Saving, Investment and Current Account in Turkey Towards a Structuralist Interpretation of Saving, Investment and Current Account in Turkey MURAT ÜNGÖR Central Bank of the Republic of Turkey http://www.muratungor.com/ April 2012 We live in the age of

More information

Analyzing Intervention Effects: Multilevel & Other Approaches. Simplest Intervention Design. Better Design: Have Pretest

Analyzing Intervention Effects: Multilevel & Other Approaches. Simplest Intervention Design. Better Design: Have Pretest Analyzing Intervention Effects: Multilevel & Other Approaches Joop Hox Methodology & Statistics, Utrecht Simplest Intervention Design R X Y E Random assignment Experimental + Control group Analysis: t

More information

FIXED EFFECTS AND RELATED ESTIMATORS FOR CORRELATED RANDOM COEFFICIENT AND TREATMENT EFFECT PANEL DATA MODELS

FIXED EFFECTS AND RELATED ESTIMATORS FOR CORRELATED RANDOM COEFFICIENT AND TREATMENT EFFECT PANEL DATA MODELS FIXED EFFECTS AND RELATED ESTIMATORS FOR CORRELATED RANDOM COEFFICIENT AND TREATMENT EFFECT PANEL DATA MODELS Jeffrey M. Wooldridge Department of Economics Michigan State University East Lansing, MI 48824-1038

More information

Chapter 7. Sealed-bid Auctions

Chapter 7. Sealed-bid Auctions Chapter 7 Sealed-bid Auctions An auction is a procedure used for selling and buying items by offering them up for bid. Auctions are often used to sell objects that have a variable price (for example oil)

More information

Hedonic prices for crude oil

Hedonic prices for crude oil Applied Economics Letters, 2003, 10, 857 861 Hedonic prices for crude oil Z. WANG Department of Economics, Monash University, PO Box 197, Caulfield East, Victoria 3145, Australia Email: Zhongmin.Wang@BusEco.monash.edu.au

More information

Modelling and Big Data. Leslie Smith ITNPBD4, October 10 2015. Updated 9 October 2015

Modelling and Big Data. Leslie Smith ITNPBD4, October 10 2015. Updated 9 October 2015 Modelling and Big Data Leslie Smith ITNPBD4, October 10 2015. Updated 9 October 2015 Big data and Models: content What is a model in this context (and why the context matters) Explicit models Mathematical

More information

The Probit Link Function in Generalized Linear Models for Data Mining Applications

The Probit Link Function in Generalized Linear Models for Data Mining Applications Journal of Modern Applied Statistical Methods Copyright 2013 JMASM, Inc. May 2013, Vol. 12, No. 1, 164-169 1538 9472/13/$95.00 The Probit Link Function in Generalized Linear Models for Data Mining Applications

More information

Asset Prices And Asset Quantities

Asset Prices And Asset Quantities Asset Prices And Asset Quantities Monika Piazzesi University of Chicago, CEPR and NBER Martin Schneider NYU and FRB Minneapolis October 2006 Abstract We propose an organizing framework that determines

More information

11. Analysis of Case-control Studies Logistic Regression

11. Analysis of Case-control Studies Logistic Regression Research methods II 113 11. Analysis of Case-control Studies Logistic Regression This chapter builds upon and further develops the concepts and strategies described in Ch.6 of Mother and Child Health:

More information

Session 9 Case 3: Utilizing Available Software Statistical Analysis

Session 9 Case 3: Utilizing Available Software Statistical Analysis Session 9 Case 3: Utilizing Available Software Statistical Analysis Michelle Phillips Economist, PURC michelle.phillips@warrington.ufl.edu With material from Ted Kury Session Overview With Data from Cases

More information

Interaction between quantitative predictors

Interaction between quantitative predictors Interaction between quantitative predictors In a first-order model like the ones we have discussed, the association between E(y) and a predictor x j does not depend on the value of the other predictors

More information

Note 2 to Computer class: Standard mis-specification tests

Note 2 to Computer class: Standard mis-specification tests Note 2 to Computer class: Standard mis-specification tests Ragnar Nymoen September 2, 2013 1 Why mis-specification testing of econometric models? As econometricians we must relate to the fact that the

More information

Nominal and ordinal logistic regression

Nominal and ordinal logistic regression Nominal and ordinal logistic regression April 26 Nominal and ordinal logistic regression Our goal for today is to briefly go over ways to extend the logistic regression model to the case where the outcome

More information

Overview Classes. 12-3 Logistic regression (5) 19-3 Building and applying logistic regression (6) 26-3 Generalizations of logistic regression (7)

Overview Classes. 12-3 Logistic regression (5) 19-3 Building and applying logistic regression (6) 26-3 Generalizations of logistic regression (7) Overview Classes 12-3 Logistic regression (5) 19-3 Building and applying logistic regression (6) 26-3 Generalizations of logistic regression (7) 2-4 Loglinear models (8) 5-4 15-17 hrs; 5B02 Building and

More information

UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS

UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS Exam: ECON4310 Intertemporal macroeconomics Date of exam: Thursday, November 27, 2008 Grades are given: December 19, 2008 Time for exam: 09:00 a.m. 12:00 noon

More information

2. Real Business Cycle Theory (June 25, 2013)

2. Real Business Cycle Theory (June 25, 2013) Prof. Dr. Thomas Steger Advanced Macroeconomics II Lecture SS 13 2. Real Business Cycle Theory (June 25, 2013) Introduction Simplistic RBC Model Simple stochastic growth model Baseline RBC model Introduction

More information

Valuation of the Surrender Option Embedded in Equity-Linked Life Insurance. Brennan Schwartz (1976,1979) Brennan Schwartz

Valuation of the Surrender Option Embedded in Equity-Linked Life Insurance. Brennan Schwartz (1976,1979) Brennan Schwartz Valuation of the Surrender Option Embedded in Equity-Linked Life Insurance Brennan Schwartz (976,979) Brennan Schwartz 04 2005 6. Introduction Compared to traditional insurance products, one distinguishing

More information

statistical learning; Bayesian learning; stochastic optimization; dynamic programming

statistical learning; Bayesian learning; stochastic optimization; dynamic programming INFORMS 2008 c 2008 INFORMS isbn 978-1-877640-23-0 doi 10.1287/educ.1080.0039 Optimal Learning Warren B. Powell and Peter Frazier Department of Operations Research and Financial Engineering, Princeton

More information

Cournot s model of oligopoly

Cournot s model of oligopoly Cournot s model of oligopoly Single good produced by n firms Cost to firm i of producing q i units: C i (q i ), where C i is nonnegative and increasing If firms total output is Q then market price is P(Q),

More information

Volatility, Productivity Correlations and Measures of. International Consumption Risk Sharing.

Volatility, Productivity Correlations and Measures of. International Consumption Risk Sharing. Volatility, Productivity Correlations and Measures of International Consumption Risk Sharing. Ergys Islamaj June 2014 Abstract This paper investigates how output volatility and productivity correlations

More information

Money and Capital in an OLG Model

Money and Capital in an OLG Model Money and Capital in an OLG Model D. Andolfatto June 2011 Environment Time is discrete and the horizon is infinite ( =1 2 ) At the beginning of time, there is an initial old population that lives (participates)

More information

The Loss in Efficiency from Using Grouped Data to Estimate Coefficients of Group Level Variables. Kathleen M. Lang* Boston College.

The Loss in Efficiency from Using Grouped Data to Estimate Coefficients of Group Level Variables. Kathleen M. Lang* Boston College. The Loss in Efficiency from Using Grouped Data to Estimate Coefficients of Group Level Variables Kathleen M. Lang* Boston College and Peter Gottschalk Boston College Abstract We derive the efficiency loss

More information

3 The Standard Real Business Cycle (RBC) Model. Optimal growth model + Labor decisions

3 The Standard Real Business Cycle (RBC) Model. Optimal growth model + Labor decisions Franck Portier TSE Macro II 29-21 Chapter 3 Real Business Cycles 36 3 The Standard Real Business Cycle (RBC) Model Perfectly competitive economy Optimal growth model + Labor decisions 2 types of agents

More information

DOES GOVERNMENT R & D POLICY MAINLY BENEFIT SCIENTISTS AND ENGINEERS?

DOES GOVERNMENT R & D POLICY MAINLY BENEFIT SCIENTISTS AND ENGINEERS? DOES GOVERNMENT R & D POLICY MAINLY BENEFIT SCIENTISTS AND ENGINEERS? Austan Goolsbee University of Chicago, GSB, American Bar Foundation, and National Bureau of Economic Research Presented at the A.E.A.

More information

Sectoral and regional analysis of industrial electricity demand in Russia *

Sectoral and regional analysis of industrial electricity demand in Russia * Sectoral and regional analysis of industrial electricity demand in Russia * Svetlana Egorova (NES) Natalya Volchkova (CEFIR) 1. Introduction Over last year electricity reforms in Russia are widely discussed

More information

Web-based Supplementary Materials for Bayesian Effect Estimation. Accounting for Adjustment Uncertainty by Chi Wang, Giovanni

Web-based Supplementary Materials for Bayesian Effect Estimation. Accounting for Adjustment Uncertainty by Chi Wang, Giovanni 1 Web-based Supplementary Materials for Bayesian Effect Estimation Accounting for Adjustment Uncertainty by Chi Wang, Giovanni Parmigiani, and Francesca Dominici In Web Appendix A, we provide detailed

More information

A LONGITUDINAL AND SURVIVAL MODEL WITH HEALTH CARE USAGE FOR INSURED ELDERLY. Workshop

A LONGITUDINAL AND SURVIVAL MODEL WITH HEALTH CARE USAGE FOR INSURED ELDERLY. Workshop A LONGITUDINAL AND SURVIVAL MODEL WITH HEALTH CARE USAGE FOR INSURED ELDERLY Ramon Alemany Montserrat Guillén Xavier Piulachs Lozada Riskcenter - IREA Universitat de Barcelona http://www.ub.edu/riskcenter

More information

Cost implications of no-fault automobile insurance. By: Joseph E. Johnson, George B. Flanigan, and Daniel T. Winkler

Cost implications of no-fault automobile insurance. By: Joseph E. Johnson, George B. Flanigan, and Daniel T. Winkler Cost implications of no-fault automobile insurance By: Joseph E. Johnson, George B. Flanigan, and Daniel T. Winkler Johnson, J. E., G. B. Flanigan, and D. T. Winkler. "Cost Implications of No-Fault Automobile

More information

Back to the past: option pricing using realized volatility

Back to the past: option pricing using realized volatility Back to the past: option pricing using realized volatility F. Corsi N. Fusari D. La Vecchia Swiss Finance Institute and University of Siena Swiss Finance Institute, University of Lugano University of Lugano

More information

Panel Data Econometrics

Panel Data Econometrics Panel Data Econometrics Master of Science in Economics - University of Geneva Christophe Hurlin, Université d Orléans University of Orléans January 2010 De nition A longitudinal, or panel, data set is

More information

Sharing Online Advertising Revenue with Consumers

Sharing 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@yahoo-inc.com 2 Harvard University.

More information

Asymmetric Information

Asymmetric Information Chapter 12 Asymmetric Information CHAPTER SUMMARY In situations of asymmetric information, the allocation of resources will not be economically efficient. The asymmetry can be resolved directly through

More information

Integrated Resource Plan

Integrated Resource Plan Integrated Resource Plan March 19, 2004 PREPARED FOR KAUA I ISLAND UTILITY COOPERATIVE LCG Consulting 4962 El Camino Real, Suite 112 Los Altos, CA 94022 650-962-9670 1 IRP 1 ELECTRIC LOAD FORECASTING 1.1

More information

How Important Is the Stock Market Effect on Consumption?

How Important Is the Stock Market Effect on Consumption? How Important Is the Stock Market Effect on Consumption? Sydney Ludvigson and Charles Steindel The second half of the 1990s has seen substantial changes in the wealth of American households, primarily

More information

A Panel Data Analysis of Corporate Attributes and Stock Prices for Indian Manufacturing Sector

A Panel Data Analysis of Corporate Attributes and Stock Prices for Indian Manufacturing Sector Journal of Modern Accounting and Auditing, ISSN 1548-6583 November 2013, Vol. 9, No. 11, 1519-1525 D DAVID PUBLISHING A Panel Data Analysis of Corporate Attributes and Stock Prices for Indian Manufacturing

More information

Forecasting Framework for Inventory and Sales of Short Life Span Products

Forecasting Framework for Inventory and Sales of Short Life Span Products Forecasting Framework for Inventory and Sales of Short Life Span Products Master Thesis Graduate student: Astrid Suryapranata Graduation committee: Professor: Prof. dr. ir. M.P.C. Weijnen Supervisors:

More information

Real Business Cycle Models

Real Business Cycle Models Phd Macro, 2007 (Karl Whelan) 1 Real Business Cycle Models The Real Business Cycle (RBC) model introduced in a famous 1982 paper by Finn Kydland and Edward Prescott is the original DSGE model. 1 The early

More information

Specification: Choosing the Independent Variables

Specification: Choosing the Independent Variables CHAPTER 6 Specification: Choosing the Independent Variables 6.1 Omitted Variables 6.2 Irrelevant Variables 6.3 An Illustration of the Misuse of Specification Criteria 6.4 Specification Searches 6.5 Lagged

More information

SOLUTIONS TO EXERCISES FOR. MATHEMATICS 205A Part 3. Spaces with special properties

SOLUTIONS TO EXERCISES FOR. MATHEMATICS 205A Part 3. Spaces with special properties SOLUTIONS TO EXERCISES FOR MATHEMATICS 205A Part 3 Fall 2008 III. Spaces with special properties III.1 : Compact spaces I Problems from Munkres, 26, pp. 170 172 3. Show that a finite union of compact subspaces

More information

USING ANALYTICS TO MEASURE THE VALUE OF EMPLOYEE REFERRAL PROGRAMS

USING ANALYTICS TO MEASURE THE VALUE OF EMPLOYEE REFERRAL PROGRAMS USING ANALYTICS TO MEASURE THE VALUE OF EMPLOYEE REFERRAL PROGRAMS Evolv Study: The benefits of an employee referral program significantly outweigh the costs Using Analytics To Measure The Value Of Employee

More information

Sharing Online Advertising Revenue with Consumers

Sharing 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@yahoo-inc.com 2 Harvard University.

More information

Financial Markets. Itay Goldstein. Wharton School, University of Pennsylvania

Financial Markets. Itay Goldstein. Wharton School, University of Pennsylvania Financial Markets Itay Goldstein Wharton School, University of Pennsylvania 1 Trading and Price Formation This line of the literature analyzes the formation of prices in financial markets in a setting

More information

Sales forecasting # 1

Sales forecasting # 1 Sales forecasting # 1 Arthur Charpentier arthur.charpentier@univ-rennes1.fr 1 Agenda Qualitative and quantitative methods, a very general introduction Series decomposition Short versus long term forecasting

More information

Empirical Methods in Applied Economics

Empirical Methods in Applied Economics Empirical Methods in Applied Economics Jörn-Ste en Pischke LSE October 2005 1 Observational Studies and Regression 1.1 Conditional Randomization Again When we discussed experiments, we discussed already

More information

Clustering in the Linear Model

Clustering in the Linear Model Short Guides to Microeconometrics Fall 2014 Kurt Schmidheiny Universität Basel Clustering in the Linear Model 2 1 Introduction Clustering in the Linear Model This handout extends the handout on The Multiple

More information

Randomization Based Confidence Intervals For Cross Over and Replicate Designs and for the Analysis of Covariance

Randomization Based Confidence Intervals For Cross Over and Replicate Designs and for the Analysis of Covariance Randomization Based Confidence Intervals For Cross Over and Replicate Designs and for the Analysis of Covariance Winston Richards Schering-Plough Research Institute JSM, Aug, 2002 Abstract Randomization

More information

An Empirical Analysis of Insider Rates vs. Outsider Rates in Bank Lending

An Empirical Analysis of Insider Rates vs. Outsider Rates in Bank Lending An Empirical Analysis of Insider Rates vs. Outsider Rates in Bank Lending Lamont Black* Indiana University Federal Reserve Board of Governors November 2006 ABSTRACT: This paper analyzes empirically the

More information

Time Series Forecasting Techniques

Time Series Forecasting Techniques 03-Mentzer (Sales).qxd 11/2/2004 11:33 AM Page 73 3 Time Series Forecasting Techniques Back in the 1970s, we were working with a company in the major home appliance industry. In an interview, the person

More information

List of Ph.D. Courses

List of Ph.D. Courses Research Methods Courses (5 courses/15 hours) List of Ph.D. Courses The research methods set consists of five courses (15 hours) that discuss the process of research and key methodological issues encountered

More information

The VAR models discussed so fare are appropriate for modeling I(0) data, like asset returns or growth rates of macroeconomic time series.

The VAR models discussed so fare are appropriate for modeling I(0) data, like asset returns or growth rates of macroeconomic time series. Cointegration The VAR models discussed so fare are appropriate for modeling I(0) data, like asset returns or growth rates of macroeconomic time series. Economic theory, however, often implies equilibrium

More information

Market Maker Protection Tools. TOM MTF Derivatives

Market Maker Protection Tools. TOM MTF Derivatives Market Maker Protection Tools TOM MTF Derivatives version: November 2011 1 Introduction This manual describes the Market Maker Protection functionality for trading of Derivatives on the TOM MTF Derivatives

More information

Economic Growth: Theory and Empirics (2012) Problem set I

Economic Growth: Theory and Empirics (2012) Problem set I Economic Growth: Theory and Empirics (2012) Problem set I Due date: April 27, 2012 Problem 1 Consider a Solow model with given saving/investment rate s. Assume: Y t = K α t (A tl t ) 1 α 2) a constant

More information

Missing Data: Part 1 What to Do? Carol B. Thompson Johns Hopkins Biostatistics Center SON Brown Bag 3/20/13

Missing Data: Part 1 What to Do? Carol B. Thompson Johns Hopkins Biostatistics Center SON Brown Bag 3/20/13 Missing Data: Part 1 What to Do? Carol B. Thompson Johns Hopkins Biostatistics Center SON Brown Bag 3/20/13 Overview Missingness and impact on statistical analysis Missing data assumptions/mechanisms Conventional

More information

Real Business Cycle Theory

Real Business Cycle Theory Chapter 4 Real Business Cycle Theory This section of the textbook focuses on explaining the behavior of the business cycle. The terms business cycle, short-run macroeconomics, and economic fluctuations

More information

Finance 400 A. Penati - G. Pennacchi Market Micro-Structure: Notes on the Kyle Model

Finance 400 A. Penati - G. Pennacchi Market Micro-Structure: Notes on the Kyle Model Finance 400 A. Penati - G. Pennacchi Market Micro-Structure: Notes on the Kyle Model These notes consider the single-period model in Kyle (1985) Continuous Auctions and Insider Trading, Econometrica 15,

More information

Technical Efficiency Accounting for Environmental Influence in the Japanese Gas Market

Technical Efficiency Accounting for Environmental Influence in the Japanese Gas Market Technical Efficiency Accounting for Environmental Influence in the Japanese Gas Market Sumiko Asai Otsuma Women s University 2-7-1, Karakida, Tama City, Tokyo, 26-854, Japan asai@otsuma.ac.jp Abstract:

More information

A Review of the Literature of Real Business Cycle theory. By Student E XXXXXXX

A Review of the Literature of Real Business Cycle theory. By Student E XXXXXXX A Review of the Literature of Real Business Cycle theory By Student E XXXXXXX Abstract: The following paper reviews five articles concerning Real Business Cycle theory. First, the review compares the various

More information

14.581 MIT PhD International Trade Lecture 9: Increasing Returns to Scale and Monopolistic Competition (Theory)

14.581 MIT PhD International Trade Lecture 9: Increasing Returns to Scale and Monopolistic Competition (Theory) 14.581 MIT PhD International Trade Lecture 9: Increasing Returns to Scale and Monopolistic Competition (Theory) Dave Donaldson Spring 2011 Today s Plan 1 Introduction to New Trade Theory 2 Monopolistically

More information

Chapter 13 Introduction to Nonlinear Regression( 非 線 性 迴 歸 )

Chapter 13 Introduction to Nonlinear Regression( 非 線 性 迴 歸 ) Chapter 13 Introduction to Nonlinear Regression( 非 線 性 迴 歸 ) and Neural Networks( 類 神 經 網 路 ) 許 湘 伶 Applied Linear Regression Models (Kutner, Nachtsheim, Neter, Li) hsuhl (NUK) LR Chap 10 1 / 35 13 Examples

More information

This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and

This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution

More information

Analyzing Structural Equation Models With Missing Data

Analyzing Structural Equation Models With Missing Data Analyzing Structural Equation Models With Missing Data Craig Enders* Arizona State University cenders@asu.edu based on Enders, C. K. (006). Analyzing structural equation models with missing data. In G.

More information