I. Multinomial Logit Suppose we only have individual specific covariates. Then we can model the response probability as
|
|
- Madison Joseph
- 6 years ago
- Views:
Transcription
1 Econ 513, USC, Fall 2005 Lecture 15 Discrete Response Models: Multinomial, Conditional and Nested Logit Models Here we focus again on models for discrete choice with more than two outcomes We assume that the outcome of interest, the choice y takes on non-negative integer values between zero and J; y {0, 1,, J} Unlike the ordered case there is no particular meaning to the ordering Examples are travel modes (bus/train/car), employment status (employed/unemployed/out-of-the-laborforce), marital status (single/married/divorced/widowed) and many others We wish to model the distribution of y in terms of covariates In some cases we will distinguish between covariates x i that vary by units (individuals or firms), and covariates that vary by choice (and possibly individual), x ij Examples of the first type include individual characteristics such as age, or education An example of the second type is the cost associated with the choice, for example the cost of commuting by bus/train/car This distinction only arises from the economics (or general scientific) substance of the problem McFadden developed the interpretation of these models through utility maximizing choice behavior In that case we may be willing to put restrictions on the way covariates affect choices: costs of a particular choice affect the utility of that choice, but not the utilities of other choices The strategy is to develop a model for the conditional probability of choice j given the covariates Suppose the model is Pr(y j x) P j (x; θ) Then the log likelihood function is L(θ) N J 1{y i j} ln P j (x i ; θ) i1 j0 I Multinomial Logit Suppose we only have individual specific covariates Then we can model the response probability as Pr(y j x) for choices j 1,, J and exp(x β j ) 1 + J l1 exp(x β l ), Pr(y 0 x) J l1 exp(x β l ), for the first choice This is a direct extension of the binary response logit model It leads to a very well-behaved likelihood function and is easy to estimate More interestingly it can be viewed as a special case of the following conditional logit 1
2 II Conditional Logit Suppose all covariates vary by choice (and possibly also by individual, but that is not essential here) Then McFadden proposed the conditional logit model: Pr(y i j x i0,, x ij ) exp(x ijβ) J l0 exp(x il β), for j 0,, J The multinomial logit model can be viewed as a special case of this Suppose we have a vector of individual characteristics x i with dimension K Then define for each choice j the vector of covariates x ij as the vector of dimension K (J + 1), with all zeros other than the elements K j + 1 to K (j + 1) which are equal to x i : x i0 x 1 0 0, x ij III Link with Utility Maximization 0 x i 0, x ij McFadden motivates this model by extending the latent index model to multiple choices Suppose that the utility for individual i associated with choice j is 0 0 x i U ij x ijβ + ε ij (1) Furthermore, let individual i choose option j (that is y i j) if that provides the highest level of utility, or y i j if U ij U il for all l 0,, J, (ties have probability zero because of the continuity of the distribution for ε) Now suppose that the ε ij are independent accross choices and individuals and have type I extreme value distributions Then the choice y i follows the conditional logit model The type I extreme value distribution has cumulative distribution function F (ɛ) exp( exp( ɛ)), and probability density function f(ɛ) exp( ɛ) exp( exp( ɛ)) 2
3 This distribution has a unique mode at zero, a mean equal to 058, and a a second moment of 199 and a variance of 165 See Figure 1 for the probability density function and the comparison with the normal density Note the assymetry of the distribution Given the extreme value distribution the probability of choice 0 is Pr(y i 0 x i ) Pr(U i0 > U i1,, U i0 > U ij ) Pr(ε i0 + x i0β x i1β > ε i1,, ε i0 + x i0β x ijβ > ε ij ) εi0 +x i0 β x i1 β εi0 +x ij β x ij β f(ε i0 ) f(ε ij )dε i0, dε ij exp( ε 0i ) exp( exp( ε 0i ) exp( exp( ε i0 x i0β + x i1β)) exp( exp( ε i0 x i0β + x ijβ))dε i0 [ exp( ε 0i ) exp exp( ε 0i ) exp( ε i0 x i0β + x i1β)) ] exp( ε i0 x i0β + x ijβ) dε i0 exp(x i0β) J j0 exp(x i0 β) To see the different steps in this derivation note that c exp( ɛ) exp( exp( ɛ))dɛ F (c) exp( exp( c)), for the extreme value distribution Also, exp( ɛ) exp( exp( ɛ c))dɛ exp( η + c) exp( exp( η))dη 3
4 exp(c) exp( η) exp( exp( η))dη exp(c), by change of variables, which we apply with c ln (1 + exp(x i1β x i0β) + + exp(x ijβ x i0β)) IV Independence of Irrelevant Alternatives The main problem with the conditional logit is the property of independence of irrelevant alternative (IIA) Consider the conditional probability of choosing j given that you choose either j or l, Pr(y j y {j, l}): Pr(y i j y i {j, l}) exp(x ijβ) exp(x ij β) + exp(x il β) This probability does not depend on the characteristics of alternatives other than j and l This is sometimes unattractive McFadden s famous blue bus/red bus example illustrates this Suppose there are three choices: commuting by car, by red bus or by blue bus A sensible model would be to think that people have a preference over cars versus buses, but are indifferent between red versus blue buses That would imply that the conditional probability of commuting by car given that one commutes by car or red bus would probably differ from the same conditional probability if there is no blue bus Presumably taking away the blue bus choice would lead all the current blue bus users to shift to the red bus, and not to cars The solution is to allow in some fashion for correlation between the errors in the latent utility representation (1) With choice set that contains multiple versions of essentially the same option, we should allow the latent utilities for these choices to be identical, and so the error terms would have to be perfectly correlated This can be done in a number of ways We analyze the first one in the following discussion III Nested Logit One way to induce correlation between the choices is through nesting them Suppose the set of choices {0, 1,, J} can be partitioned into S sets B 1,, B S, so that {0, 1,, J} S s1b s Let Z s be set specific variables (It may be that the set of set specific variables is just a vector of indicators, with Z s an S-vector of zeros with a one for the sth element) Now let the conditional probability of choice j given that y i B s be equal to Pr(y i j x i, y i B s ) exp(σs 1 x ijβ) l B s exp(σs 1 x il β) 4
5 In addition suppose the probability of set B s is Pr(y i B s x i ) exp(z sα) ( l B s exp(σs 1 x il β)) σ s S t1 exp(z tα) ( l B t exp(σt 1 x il β)) σ s If we fix σ s 1 for all s, then Pr(y i j x i ) exp(x ijβ + Z sα) S t1 l B t exp(x il β + Z tα), and we are back in the conditional logit model The extra coefficient σ s implicitly allows for correlation of the errors in (1) The joint distribution function of the ε ij is F (ε i0,, ε ij ) exp ( ( S exp(z sα) exp ( ) ) ) σs σs 1 ε ij j B s s1 Within the sets the correlation coefficient for the ε ij is equal to 1 σ Between the sets the ε ij are independent How do you estimate these models? One approach is to construct the log likelihood and directly maximize it That is complicated, especially since the log likelihood function is not concave, but it is not impossible An easier alternative is to directly use the nesting structure Within a nest we have a conditional logit model with coefficients β/σ s Hence we can directly estimate β/σ s using the concavity of the conditional logit model Denote these estimates of β/σ s by β/σ s Then the probability of a particular set B s can be used to estimate σ s and α through Pr(y i B s x i ) ( exp(z sα) l B exp(x s il β/σ s ) ( S t1 exp(z tα) l Bt exp(x β/σ ) σs exp(z sα + σ s Ŵ s ) S il t ) t1 exp(z tα + σ t Ŵ t ), ) σs where Ŵ s ln ( l B s exp(x il β/σ s ) ), known as the inclusive values Hence we have another conditional logit model back that is easily estimable These two-step estimators are not efficient The variance/covariance matrix is provided in McFadden (1981) 5
6 These models can be extended to many layers of nests See for an example of a complex set of nesting Goldberg (1995) It should be noted that both the order of the nests and the elements of each nest are very important References Goldberg, P, (1995), Product Differentiation and Oligopoly in International Markets: The Case of the Automobile Industry, Econometrica, 63, McFadden, D, (1981) Econometrica Models of Probabilistic Choice, in Structural Analysis of Discrete Data with Econometric Applications, Manski and McFadden (eds), , MIT Press, Cambridge, MA 6
7
8 04 extreme value distribution (solid) and normal distribution (dashed)
Imbens/Wooldridge, Lecture Notes 11, NBER, Summer 07 1
Imbens/Wooldridge, Lecture Notes 11, NBER, Summer 07 1 What s New in Econometrics NBER, Summer 2007 Lecture 11, Wednesday, Aug 1st, 9.00-10.30am Discrete Choice Models 1. Introduction In this lecture we
More informationh=1 exp (X : J h=1 Even the direction of the e ect is not determined by jk. A simpler interpretation of j is given by the odds-ratio
Multivariate Response Models The response variable is unordered and takes more than two values. The term unordered refers to the fact that response 3 is not more favored than response 2. One choice from
More informationProbabilistic Choice Models
Econ 3: James J. Heckman Probabilistic Choice Models This chapter examines different models commonly used to model probabilistic choice, such as eg the choice of one type of transportation from among many
More informationEconometrics Lecture 5: Limited Dependent Variable Models: Logit and Probit
Econometrics Lecture 5: Limited Dependent Variable Models: Logit and Probit R. G. Pierse 1 Introduction In lecture 5 of last semester s course, we looked at the reasons for including dichotomous variables
More informationIntroduction to Discrete Choice Models
Chapter 7 Introduction to Dcrete Choice Models 7.1 Introduction It has been mentioned that the conventional selection bias model requires estimation of two structural models, namely the selection model
More informationGoals. PSCI6000 Maximum Likelihood Estimation Multiple Response Model 1. Multinomial Dependent Variable. Random Utility Model
Goals PSCI6000 Maximum Likelihood Estimation Multiple Response Model 1 Tetsuya Matsubayashi University of North Texas November 2, 2010 Random utility model Multinomial logit model Conditional logit model
More informationLatent Variable Models for Binary Data. Suppose that for a given vector of explanatory variables x, the latent
Latent Variable Models for Binary Data Suppose that for a given vector of explanatory variables x, the latent variable, U, has a continuous cumulative distribution function F (u; x) and that the binary
More informationLecture 12: Application of Maximum Likelihood Estimation:Truncation, Censoring, and Corner Solutions
Econ 513, USC, Department of Economics Lecture 12: Application of Maximum Likelihood Estimation:Truncation, Censoring, and Corner Solutions I Introduction Here we look at a set of complications with the
More informationLimited Dependent Variable Models II
Limited Dependent Variable Models II Fall 2008 Environmental Econometrics (GR03) LDV Fall 2008 1 / 15 Models with Multiple Choices The binary response model was dealing with a decision problem with two
More informationChoice Theory. Matthieu de Lapparent
Choice Theory Matthieu de Lapparent matthieu.delapparent@epfl.ch Transport and Mobility Laboratory, School of Architecture, Civil and Environmental Engineering, Ecole Polytechnique Fédérale de Lausanne
More informationGoals. PSCI6000 Maximum Likelihood Estimation Multiple Response Model 2. Recap: MNL. Recap: MNL
Goals PSCI6000 Maximum Likelihood Estimation Multiple Response Model 2 Tetsuya Matsubayashi University of North Texas November 9, 2010 Learn multiple responses models that do not require the assumption
More informationLecture 6: Discrete Choice: Qualitative Response
Lecture 6: Instructor: Department of Economics Stanford University 2011 Types of Discrete Choice Models Univariate Models Binary: Linear; Probit; Logit; Arctan, etc. Multinomial: Logit; Nested Logit; GEV;
More informationPOLI 7050 Spring 2008 February 27, 2008 Unordered Response Models I
POLI 7050 Spring 2008 February 27, 2008 Unordered Response Models I Introduction For the next couple weeks we ll be talking about unordered, polychotomous dependent variables. Examples include: Voter choice
More informationECON 594: Lecture #6
ECON 594: Lecture #6 Thomas Lemieux Vancouver School of Economics, UBC May 2018 1 Limited dependent variables: introduction Up to now, we have been implicitly assuming that the dependent variable, y, was
More informationProbabilistic Choice Models
Probabilistic Choice Models James J. Heckman University of Chicago Econ 312 This draft, March 29, 2006 This chapter examines dierent models commonly used to model probabilistic choice, such as eg the choice
More informationEcon 673: Microeconometrics
Econ 673: Microeconometrics Chapter 4: Properties of Discrete Choice Models Fall 2008 Herriges (ISU) Chapter 4: Discrete Choice Models Fall 2008 1 / 29 Outline 1 2 Deriving Choice Probabilities 3 Identification
More informationLecture-20: Discrete Choice Modeling-I
Lecture-20: Discrete Choice Modeling-I 1 In Today s Class Introduction to discrete choice models General formulation Binary choice models Specification Model estimation Application Case Study 2 Discrete
More informationModeling Binary Outcomes: Logit and Probit Models
Modeling Binary Outcomes: Logit and Probit Models Eric Zivot December 5, 2009 Motivating Example: Women s labor force participation y i = 1 if married woman is in labor force = 0 otherwise x i k 1 = observed
More informationQuasi-Maximum Likelihood: Applications
Chapter 10 Quasi-Maximum Likelihood: Applications 10.1 Binary Choice Models In many economic applications the dependent variables of interest may assume only finitely many integer values, each labeling
More informationMaximum Likelihood Methods
Maximum Likelihood Methods Some of the models used in econometrics specify the complete probability distribution of the outcomes of interest rather than just a regression function. Sometimes this is because
More informationParametric Identification of Multiplicative Exponential Heteroskedasticity
Parametric Identification of Multiplicative Exponential Heteroskedasticity Alyssa Carlson Department of Economics, Michigan State University East Lansing, MI 48824-1038, United States Dated: October 5,
More informationOrdered Response and Multinomial Logit Estimation
Ordered Response and Multinomial Logit Estimation Quantitative Microeconomics R. Mora Department of Economics Universidad Carlos III de Madrid Outline Introduction 1 Introduction 2 3 Introduction The Ordered
More informationParametric identification of multiplicative exponential heteroskedasticity ALYSSA CARLSON
Parametric identification of multiplicative exponential heteroskedasticity ALYSSA CARLSON Department of Economics, Michigan State University East Lansing, MI 48824-1038, United States (email: carls405@msu.edu)
More informationBinary Choice Models Probit & Logit. = 0 with Pr = 0 = 1. decision-making purchase of durable consumer products unemployment
BINARY CHOICE MODELS Y ( Y ) ( Y ) 1 with Pr = 1 = P = 0 with Pr = 0 = 1 P Examples: decision-making purchase of durable consumer products unemployment Estimation with OLS? Yi = Xiβ + εi Problems: nonsense
More informationBayesian Inference in GLMs. Frequentists typically base inferences on MLEs, asymptotic confidence
Bayesian Inference in GLMs Frequentists typically base inferences on MLEs, asymptotic confidence limits, and log-likelihood ratio tests Bayesians base inferences on the posterior distribution of the unknowns
More informationSimulation. Li Zhao, SJTU. Spring, Li Zhao Simulation 1 / 19
Simulation Li Zhao, SJTU Spring, 2017 Li Zhao Simulation 1 / 19 Introduction Simulation consists of drawing from a density, calculating a statistic for each draw, and averaging the results. Simulation
More informationStandard Errors & Confidence Intervals. N(0, I( β) 1 ), I( β) = [ 2 l(β, φ; y) β i β β= β j
Standard Errors & Confidence Intervals β β asy N(0, I( β) 1 ), where I( β) = [ 2 l(β, φ; y) ] β i β β= β j We can obtain asymptotic 100(1 α)% confidence intervals for β j using: β j ± Z 1 α/2 se( β j )
More informationUsing a Laplace Approximation to Estimate the Random Coefficients Logit Model by Non-linear Least Squares 1
Using a Laplace Approximation to Estimate the Random Coefficients Logit Model by Non-linear Least Squares 1 Matthew C. Harding 2 Jerry Hausman 3 September 13, 2006 1 We thank Ketan Patel for excellent
More informationcovariance between any two observations
1 Ordinary Least Squares (OLS) 1.1 Single Linear Regression Model assumptions of Classical Linear Regression Model (CLRM) (i) true relationship y i = α + βx i + ε i, i = 1,..., N where α, β = population
More informationNon-linear panel data modeling
Non-linear panel data modeling Laura Magazzini University of Verona laura.magazzini@univr.it http://dse.univr.it/magazzini May 2010 Laura Magazzini (@univr.it) Non-linear panel data modeling May 2010 1
More informationLecture 14 More on structural estimation
Lecture 14 More on structural estimation Economics 8379 George Washington University Instructor: Prof. Ben Williams traditional MLE and GMM MLE requires a full specification of a model for the distribution
More informationA Rank-Ordered Logit Model with Unobserved Heterogeneity in Ranking Capabilities
A Rank-Ordered Logit Model with Unobserved Heterogeneity in Ranking Capabilities Bram van Dijk Econometric Institute Tinbergen Institute Erasmus University Rotterdam Richard Paap Econometric Institute
More informationGeneralized Linear Models Introduction
Generalized Linear Models Introduction Statistics 135 Autumn 2005 Copyright c 2005 by Mark E. Irwin Generalized Linear Models For many problems, standard linear regression approaches don t work. Sometimes,
More informationA Note on Demand Estimation with Supply Information. in Non-Linear Models
A Note on Demand Estimation with Supply Information in Non-Linear Models Tongil TI Kim Emory University J. Miguel Villas-Boas University of California, Berkeley May, 2018 Keywords: demand estimation, limited
More informationSingle-level Models for Binary Responses
Single-level Models for Binary Responses Distribution of Binary Data y i response for individual i (i = 1,..., n), coded 0 or 1 Denote by r the number in the sample with y = 1 Mean and variance E(y) =
More informationINTRODUCTION TO TRANSPORTATION SYSTEMS
INTRODUCTION TO TRANSPORTATION SYSTEMS Lectures 5/6: Modeling/Equilibrium/Demand 1 OUTLINE 1. Conceptual view of TSA 2. Models: different roles and different types 3. Equilibrium 4. Demand Modeling References:
More informationVALUATION USING HOUSEHOLD PRODUCTION LECTURE PLAN 15: APRIL 14, 2011 Hunt Allcott
VALUATION USING HOUSEHOLD PRODUCTION 14.42 LECTURE PLAN 15: APRIL 14, 2011 Hunt Allcott PASTURE 1: ONE SITE Introduce intuition via PowerPoint slides Draw demand curve with nearby and far away towns. Question:
More informationAn Overview of Choice Models
An Overview of Choice Models Dilan Görür Gatsby Computational Neuroscience Unit University College London May 08, 2009 Machine Learning II 1 / 31 Outline 1 Overview Terminology and Notation Economic vs
More informationThe Generalized Roy Model and Treatment Effects
The Generalized Roy Model and Treatment Effects Christopher Taber University of Wisconsin November 10, 2016 Introduction From Imbens and Angrist we showed that if one runs IV, we get estimates of the Local
More informationA short introduc-on to discrete choice models
A short introduc-on to discrete choice models BART Kenneth Train, Discrete Choice Models with Simula-on, Chapter 3. Ques-ons Impact of cost, commu-ng -me, walk -me, transfer -me, number of transfers, distance
More information16/018. Efficiency Gains in Rank-ordered Multinomial Logit Models. June 13, 2016
16/018 Efficiency Gains in Rank-ordered Multinomial Logit Models Arie Beresteanu and Federico Zincenko June 13, 2016 Efficiency Gains in Rank-ordered Multinomial Logit Models Arie Beresteanu and Federico
More informationECO 2901 EMPIRICAL INDUSTRIAL ORGANIZATION
ECO 2901 EMPIRICAL INDUSTRIAL ORGANIZATION Lecture 7 & 8: Models of Competition in Prices & Quantities Victor Aguirregabiria (University of Toronto) Toronto. Winter 2018 Victor Aguirregabiria () Empirical
More informationSyllabus. By Joan Llull. Microeconometrics. IDEA PhD Program. Fall Chapter 1: Introduction and a Brief Review of Relevant Tools
Syllabus By Joan Llull Microeconometrics. IDEA PhD Program. Fall 2017 Chapter 1: Introduction and a Brief Review of Relevant Tools I. Overview II. Maximum Likelihood A. The Likelihood Principle B. The
More informationHypothesis Testing. Econ 690. Purdue University. Justin L. Tobias (Purdue) Testing 1 / 33
Hypothesis Testing Econ 690 Purdue University Justin L. Tobias (Purdue) Testing 1 / 33 Outline 1 Basic Testing Framework 2 Testing with HPD intervals 3 Example 4 Savage Dickey Density Ratio 5 Bartlett
More informationSpecification Test on Mixed Logit Models
Specification est on Mixed Logit Models Jinyong Hahn UCLA Jerry Hausman MI December 1, 217 Josh Lustig CRA Abstract his paper proposes a specification test of the mixed logit models, by generalizing Hausman
More informationLecture notes: Rust (1987) Economics : M. Shum 1
Economics 180.672: M. Shum 1 Estimate the parameters of a dynamic optimization problem: when to replace engine of a bus? This is a another practical example of an optimal stopping problem, which is easily
More informationComments on: Panel Data Analysis Advantages and Challenges. Manuel Arellano CEMFI, Madrid November 2006
Comments on: Panel Data Analysis Advantages and Challenges Manuel Arellano CEMFI, Madrid November 2006 This paper provides an impressive, yet compact and easily accessible review of the econometric literature
More informationMunich Lecture Series 2 Non-linear panel data models: Binary response and ordered choice models and bias-corrected fixed effects models
Munich Lecture Series 2 Non-linear panel data models: Binary response and ordered choice models and bias-corrected fixed effects models Stefanie Schurer stefanie.schurer@rmit.edu.au RMIT University School
More information14.13 Lecture 8. Xavier Gabaix. March 2, 2004
14.13 Lecture 8 Xavier Gabaix March 2, 2004 1 Bounded Rationality Three reasons to study: Hope that it will generate a unified framework for behavioral economics Some phenomena should be captured: difficult-easy
More informationChapter 11. Regression with a Binary Dependent Variable
Chapter 11 Regression with a Binary Dependent Variable 2 Regression with a Binary Dependent Variable (SW Chapter 11) So far the dependent variable (Y) has been continuous: district-wide average test score
More informationMaximum Likelihood and. Limited Dependent Variable Models
Maximum Likelihood and Limited Dependent Variable Models Michele Pellizzari IGIER-Bocconi, IZA and frdb May 24, 2010 These notes are largely based on the textbook by Jeffrey M. Wooldridge. 2002. Econometric
More informationA dynamic model for binary panel data with unobserved heterogeneity admitting a n-consistent conditional estimator
A dynamic model for binary panel data with unobserved heterogeneity admitting a n-consistent conditional estimator Francesco Bartolucci and Valentina Nigro Abstract A model for binary panel data is introduced
More informationLecture 1. Behavioral Models Multinomial Logit: Power and limitations. Cinzia Cirillo
Lecture 1 Behavioral Models Multinomial Logit: Power and limitations Cinzia Cirillo 1 Overview 1. Choice Probabilities 2. Power and Limitations of Logit 1. Taste variation 2. Substitution patterns 3. Repeated
More informationChapter 3 Choice Models
Chapter 3 Choice Models 3.1 Introduction This chapter describes the characteristics of random utility choice model in a general setting, specific elements related to the conjoint choice context are given
More informationThe 17 th Behavior Modeling Summer School
The 17 th Behavior Modeling Summer School September 14-16, 2017 Introduction to Discrete Choice Models Giancarlos Troncoso Parady Assistant Professor Urban Transportation Research Unit Department of Urban
More information-redprob- A Stata program for the Heckman estimator of the random effects dynamic probit model
-redprob- A Stata program for the Heckman estimator of the random effects dynamic probit model Mark B. Stewart University of Warwick January 2006 1 The model The latent equation for the random effects
More informationGibbs Sampling in Latent Variable Models #1
Gibbs Sampling in Latent Variable Models #1 Econ 690 Purdue University Outline 1 Data augmentation 2 Probit Model Probit Application A Panel Probit Panel Probit 3 The Tobit Model Example: Female Labor
More informationEstimating Single-Agent Dynamic Models
Estimating Single-Agent Dynamic Models Paul T. Scott Empirical IO Fall, 2013 1 / 49 Why are dynamics important? The motivation for using dynamics is usually external validity: we want to simulate counterfactuals
More informationLecture 22 Survival Analysis: An Introduction
University of Illinois Department of Economics Spring 2017 Econ 574 Roger Koenker Lecture 22 Survival Analysis: An Introduction There is considerable interest among economists in models of durations, which
More informationNinth ARTNeT Capacity Building Workshop for Trade Research "Trade Flows and Trade Policy Analysis"
Ninth ARTNeT Capacity Building Workshop for Trade Research "Trade Flows and Trade Policy Analysis" June 2013 Bangkok, Thailand Cosimo Beverelli and Rainer Lanz (World Trade Organization) 1 Selected econometric
More informationParameter Estimation
Parameter Estimation Consider a sample of observations on a random variable Y. his generates random variables: (y 1, y 2,, y ). A random sample is a sample (y 1, y 2,, y ) where the random variables y
More informationTruncation and Censoring
Truncation and Censoring Laura Magazzini laura.magazzini@univr.it Laura Magazzini (@univr.it) Truncation and Censoring 1 / 35 Truncation and censoring Truncation: sample data are drawn from a subset of
More informationDeveloping Confidence in Your Net-to-Gross Ratio Estimates
Developing Confidence in Your Net-to-Gross Ratio Estimates Bruce Mast and Patrice Ignelzi, Pacific Consulting Services Some authors have identified the lack of an accepted methodology for calculating the
More informationMax. Likelihood Estimation. Outline. Econometrics II. Ricardo Mora. Notes. Notes
Maximum Likelihood Estimation Econometrics II Department of Economics Universidad Carlos III de Madrid Máster Universitario en Desarrollo y Crecimiento Económico Outline 1 3 4 General Approaches to Parameter
More informationBinary choice. Michel Bierlaire
Binary choice Michel Bierlaire Transport and Mobility Laboratory School of Architecture, Civil and Environmental Engineering Ecole Polytechnique Fédérale de Lausanne M. Bierlaire (TRANSP-OR ENAC EPFL)
More informationLecture 16 Deep Neural Generative Models
Lecture 16 Deep Neural Generative Models CMSC 35246: Deep Learning Shubhendu Trivedi & Risi Kondor University of Chicago May 22, 2017 Approach so far: We have considered simple models and then constructed
More informationSTA216: Generalized Linear Models. Lecture 1. Review and Introduction
STA216: Generalized Linear Models Lecture 1. Review and Introduction Let y 1,..., y n denote n independent observations on a response Treat y i as a realization of a random variable Y i In the general
More informationWhat can we learn about correlations from multinomial probit estimates?
What can we learn about correlations from multinomial probit estimates? Chiara Monfardini J.M.C. Santos Silva February 2006 Abstract It is well known that, in a multinomial probit, only the covariance
More informationdisc choice5.tex; April 11, ffl See: King - Unifying Political Methodology ffl See: King/Tomz/Wittenberg (1998, APSA Meeting). ffl See: Alvarez
disc choice5.tex; April 11, 2001 1 Lecture Notes on Discrete Choice Models Copyright, April 11, 2001 Jonathan Nagler 1 Topics 1. Review the Latent Varible Setup For Binary Choice ffl Logit ffl Likelihood
More informationLECTURE 10: NEYMAN-PEARSON LEMMA AND ASYMPTOTIC TESTING. The last equality is provided so this can look like a more familiar parametric test.
Economics 52 Econometrics Professor N.M. Kiefer LECTURE 1: NEYMAN-PEARSON LEMMA AND ASYMPTOTIC TESTING NEYMAN-PEARSON LEMMA: Lesson: Good tests are based on the likelihood ratio. The proof is easy in the
More informationECON 4160, Autumn term Lecture 1
ECON 4160, Autumn term 2017. Lecture 1 a) Maximum Likelihood based inference. b) The bivariate normal model Ragnar Nymoen University of Oslo 24 August 2017 1 / 54 Principles of inference I Ordinary least
More informationECON Introductory Econometrics. Lecture 11: Binary dependent variables
ECON4150 - Introductory Econometrics Lecture 11: Binary dependent variables Monique de Haan (moniqued@econ.uio.no) Stock and Watson Chapter 11 Lecture Outline 2 The linear probability model Nonlinear probability
More informationLinear Regression Models P8111
Linear Regression Models P8111 Lecture 25 Jeff Goldsmith April 26, 2016 1 of 37 Today s Lecture Logistic regression / GLMs Model framework Interpretation Estimation 2 of 37 Linear regression Course started
More informationDynamic Models Part 1
Dynamic Models Part 1 Christopher Taber University of Wisconsin December 5, 2016 Survival analysis This is especially useful for variables of interest measured in lengths of time: Length of life after
More informationNevo on Random-Coefficient Logit
Nevo on Random-Coefficient Logit March 28, 2003 Eric Rasmusen Abstract Overheads for logit demand estimation for G604. These accompany the Nevo JEMS article. Indiana University Foundation Professor, Department
More informationECON 5350 Class Notes Functional Form and Structural Change
ECON 5350 Class Notes Functional Form and Structural Change 1 Introduction Although OLS is considered a linear estimator, it does not mean that the relationship between Y and X needs to be linear. In this
More informationLogistic regression: Why we often can do what we think we can do. Maarten Buis 19 th UK Stata Users Group meeting, 10 Sept. 2015
Logistic regression: Why we often can do what we think we can do Maarten Buis 19 th UK Stata Users Group meeting, 10 Sept. 2015 1 Introduction Introduction - In 2010 Carina Mood published an overview article
More informationWeek 7: Binary Outcomes (Scott Long Chapter 3 Part 2)
Week 7: (Scott Long Chapter 3 Part 2) Tsun-Feng Chiang* *School of Economics, Henan University, Kaifeng, China April 29, 2014 1 / 38 ML Estimation for Probit and Logit ML Estimation for Probit and Logit
More informationSOLUTIONS Problem Set 2: Static Entry Games
SOLUTIONS Problem Set 2: Static Entry Games Matt Grennan January 29, 2008 These are my attempt at the second problem set for the second year Ph.D. IO course at NYU with Heski Bar-Isaac and Allan Collard-Wexler
More informationLecture Notes: Estimation of dynamic discrete choice models
Lecture Notes: Estimation of dynamic discrete choice models Jean-François Houde Cornell University November 7, 2016 These lectures notes incorporate material from Victor Agguirregabiria s graduate IO slides
More information8 Nominal and Ordinal Logistic Regression
8 Nominal and Ordinal Logistic Regression 8.1 Introduction If the response variable is categorical, with more then two categories, then there are two options for generalized linear models. One relies on
More informationW-BASED VS LATENT VARIABLES SPATIAL AUTOREGRESSIVE MODELS: EVIDENCE FROM MONTE CARLO SIMULATIONS
1 W-BASED VS LATENT VARIABLES SPATIAL AUTOREGRESSIVE MODELS: EVIDENCE FROM MONTE CARLO SIMULATIONS An Liu University of Groningen Henk Folmer University of Groningen Wageningen University Han Oud Radboud
More information1 Differentiated Products: Motivation
1 Differentiated Products: Motivation Let us generalise the problem of differentiated products. Let there now be N firms producing one differentiated product each. If we start with the usual demand function
More informationDiscrete Choice Modeling
[Part 6] 1/55 0 Introduction 1 Summary 2 Binary Choice 3 Panel Data 4 Bivariate Probit 5 Ordered Choice 6 7 Multinomial Choice 8 Nested Logit 9 Heterogeneity 10 Latent Class 11 Mixed Logit 12 Stated Preference
More informationMultinomial Discrete Choice Models
hapter 2 Multinomial Discrete hoice Models 2.1 Introduction We present some discrete choice models that are applied to estimate parameters of demand for products that are purchased in discrete quantities.
More informationLecture Notes 1: Decisions and Data. In these notes, I describe some basic ideas in decision theory. theory is constructed from
Topics in Data Analysis Steven N. Durlauf University of Wisconsin Lecture Notes : Decisions and Data In these notes, I describe some basic ideas in decision theory. theory is constructed from The Data:
More informationSIMULATION-BASED SENSITIVITY ANALYSIS FOR MATCHING ESTIMATORS
SIMULATION-BASED SENSITIVITY ANALYSIS FOR MATCHING ESTIMATORS TOMMASO NANNICINI universidad carlos iii de madrid UK Stata Users Group Meeting London, September 10, 2007 CONTENT Presentation of a Stata
More informationBresnahan, JIE 87: Competition and Collusion in the American Automobile Industry: 1955 Price War
Bresnahan, JIE 87: Competition and Collusion in the American Automobile Industry: 1955 Price War Spring 009 Main question: In 1955 quantities of autos sold were higher while prices were lower, relative
More informationIntroduction to choice models Michel Bierlaire Virginie Lurkin
Introduction to choice models Michel Bierlaire Virginie Lurkin This document gathers the material used in the course, that is the slides of the videos as well as the text files. It makes it easier to search
More informationEconometrics III: Problem Set # 2 Single Agent Dynamic Discrete Choice
Holger Sieg Carnegie Mellon University Econometrics III: Problem Set # 2 Single Agent Dynamic Discrete Choice INSTRUCTIONS: This problem set was originally created by Ron Goettler. The objective of this
More informationLimited Dependent Variables and Panel Data
and Panel Data June 24 th, 2009 Structure 1 2 Many economic questions involve the explanation of binary variables, e.g.: explaining the participation of women in the labor market explaining retirement
More informationECONOMETRICS II (ECO 2401S) University of Toronto. Department of Economics. Spring 2013 Instructor: Victor Aguirregabiria
ECONOMETRICS II (ECO 2401S) University of Toronto. Department of Economics. Spring 2013 Instructor: Victor Aguirregabiria SOLUTION TO FINAL EXAM Friday, April 12, 2013. From 9:00-12:00 (3 hours) INSTRUCTIONS:
More information,..., θ(2),..., θ(n)
Likelihoods for Multivariate Binary Data Log-Linear Model We have 2 n 1 distinct probabilities, but we wish to consider formulations that allow more parsimonious descriptions as a function of covariates.
More informationSTAT 518 Intro Student Presentation
STAT 518 Intro Student Presentation Wen Wei Loh April 11, 2013 Title of paper Radford M. Neal [1999] Bayesian Statistics, 6: 475-501, 1999 What the paper is about Regression and Classification Flexible
More informationStat 642, Lecture notes for 04/12/05 96
Stat 642, Lecture notes for 04/12/05 96 Hosmer-Lemeshow Statistic The Hosmer-Lemeshow Statistic is another measure of lack of fit. Hosmer and Lemeshow recommend partitioning the observations into 10 equal
More informationDuration Analysis. Joan Llull
Duration Analysis Joan Llull Panel Data and Duration Models Barcelona GSE joan.llull [at] movebarcelona [dot] eu Introduction Duration Analysis 2 Duration analysis Duration data: how long has an individual
More informationSpatial Discrete Choice Models
Spatial Discrete Choice Models Professor William Greene Stern School of Business, New York University SPATIAL ECONOMETRICS ADVANCED INSTITUTE University of Rome May 23, 2011 Spatial Correlation Spatially
More information6.867 Machine Learning
6.867 Machine Learning Problem set 1 Solutions Thursday, September 19 What and how to turn in? Turn in short written answers to the questions explicitly stated, and when requested to explain or prove.
More informationDiscrete Time Duration Models with Group level Heterogeneity
This work is distributed as a Discussion Paper by the STANFORD INSTITUTE FOR ECONOMIC POLICY RESEARCH SIEPR Discussion Paper No. 05-08 Discrete Time Duration Models with Group level Heterogeneity By Anders
More informationOptimal Designs for 2 k Experiments with Binary Response
1 / 57 Optimal Designs for 2 k Experiments with Binary Response Dibyen Majumdar Mathematics, Statistics, and Computer Science College of Liberal Arts and Sciences University of Illinois at Chicago Joint
More information