Small-Sample Properties of Tests for Heteroscedasticity in the Conditional Logit Model
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Economic Choices
ECONOMIC CHOICES Daniel McFadden* This Nobel lecture discusses the microeconometric analysis of choice behavior of consumers who face discrete economic alternatives. Before the 1960's, economists used consumer theory mostly as a logical tool, to explore conceptually the properties of alternative market organizations and economic policies. When the theory was applied empirically, it was to market-level or national-accounts-level data. In these applications, the theory was usually developed in terms of a representative agent, with market-level behavior given by the representative agent’s behavior writ large. When observations deviated from those implied by the representative agent theory, these differences were swept into an additive disturbance and attributed to data measurement errors, rather than to unobserved factors within or across individual agents. In statistical language, traditional consumer theory placed structural restrictions on mean behavior, but the distribution of responses about their mean was not tied to the theory. In the 1960's, rapidly increasing availability of survey data on individual behavior, and the advent of digital computers that could analyze these data, focused attention on the variations in demand across individuals. It became important to explain and model these variations as part of consumer theory, rather than as ad hoc disturbances. This was particularly obvious for discrete choices, such as transportation mode or occupation. The solution to this problem has led to the tools we have today for microeconometric analysis of choice behavior. I will first give a brief history of the development of this subject, and place my own contributions in context. After that, I will discuss in some detail more recent developments in the economic theory of choice, and modifications to this theory that are being forced by experimental evidence from cognitive psychology. -
Estimation of Discrete Choice Models Including Socio-Economic Explanatory Variables, Diskussionsbeiträge - Serie II, No
A Service of Leibniz-Informationszentrum econstor Wirtschaft Leibniz Information Centre Make Your Publications Visible. zbw for Economics Ronning, Gerd Working Paper Estimation of discrete choice models including socio- economic explanatory variables Diskussionsbeiträge - Serie II, No. 42 Provided in Cooperation with: Department of Economics, University of Konstanz Suggested Citation: Ronning, Gerd (1987) : Estimation of discrete choice models including socio-economic explanatory variables, Diskussionsbeiträge - Serie II, No. 42, Universität Konstanz, Sonderforschungsbereich 178 - Internationalisierung der Wirtschaft, Konstanz This Version is available at: http://hdl.handle.net/10419/101753 Standard-Nutzungsbedingungen: Terms of use: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Documents in EconStor may be saved and copied for your Zwecken und zum Privatgebrauch gespeichert und kopiert werden. personal and scholarly purposes. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle You are not to copy documents for public or commercial Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich purposes, to exhibit the documents publicly, to make them machen, vertreiben oder anderweitig nutzen. publicly available on the internet, or to distribute or otherwise use the documents in public. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, If the documents have been made available under an Open gelten abweichend von diesen Nutzungsbedingungen -
Discrete Choice Models As Structural Models of Demand: Some Economic Implications of Common Approaches∗
Discrete Choice Models as Structural Models of Demand: Some Economic Implications of Common Approaches∗ Patrick Bajari† C. Lanier Benkard‡ Stanford University and NBER Stanford University and NBER March 2003 Abstract We derive several properties of commonly used discrete choice models that are po- tentially undesirable if these models are to be used as structural models of demand. Specifically, we show that as the number of goods in the market becomes large in these models, i) no product has a perfect substitute, ii) Bertrand-Nash markups do not con- verge to zero, iii) the fraction of utility derived from the observed product characteristics tends toward zero, iv) if an outside good is present, then the deterministic portion of utility from the inside goods must tend to negative infinity and v) there is always some consumer that is willing to pay an arbitrarily large sum for each good in the market. These results support further research on alternative demand models such as those of Berry and Pakes (2001), Ackerberg and Rysman (2001), or Bajari and Benkard (2003). ∗We thank Daniel Ackerberg, Steven Berry, Richard Blundell, Jonathon Levin, Peter Reiss, and Ed Vytlacil for many helpful comments on an earlier draft of this paper, as well as seminar participants at Carnegie Mellon, Northwestern, Stanford, UBC, UCL, UCLA, Wash. Univ. in St. Louis, Wisconsin, and Yale. We gratefully acknowledge financial support of NSF grant #SES-0112106. Any remaining errors are our own. †Dept. of Economics, Stanford University, Stanford, CA 94305, [email protected] ‡Graduate School of Business, Stanford University, Stanford, CA 94305-5015, [email protected] 1 Introduction. -
Multinomial Logistic Regression
26-Osborne (Best)-45409.qxd 10/9/2007 5:52 PM Page 390 26 MULTINOMIAL LOGISTIC REGRESSION CAROLYN J. ANDERSON LESLIE RUTKOWSKI hapter 24 presented logistic regression Many of the concepts used in binary logistic models for dichotomous response vari- regression, such as the interpretation of parame- C ables; however, many discrete response ters in terms of odds ratios and modeling prob- variables have three or more categories (e.g., abilities, carry over to multicategory logistic political view, candidate voted for in an elec- regression models; however, two major modifica- tion, preferred mode of transportation, or tions are needed to deal with multiple categories response options on survey items). Multicate- of the response variable. One difference is that gory response variables are found in a wide with three or more levels of the response variable, range of experiments and studies in a variety of there are multiple ways to dichotomize the different fields. A detailed example presented in response variable. If J equals the number of cate- this chapter uses data from 600 students from gories of the response variable, then J(J – 1)/2 dif- the High School and Beyond study (Tatsuoka & ferent ways exist to dichotomize the categories. Lohnes, 1988) to look at the differences among In the High School and Beyond study, the three high school students who attended academic, program types can be dichotomized into pairs general, or vocational programs. The students’ of programs (i.e., academic and general, voca- socioeconomic status (ordinal), achievement tional and general, and academic and vocational). test scores (numerical), and type of school How the response variable is dichotomized (nominal) are all examined as possible explana- depends, in part, on the nature of the variable. -
Logit and Ordered Logit Regression (Ver
Getting Started in Logit and Ordered Logit Regression (ver. 3.1 beta) Oscar Torres-Reyna Data Consultant [email protected] http://dss.princeton.edu/training/ PU/DSS/OTR Logit model • Use logit models whenever your dependent variable is binary (also called dummy) which takes values 0 or 1. • Logit regression is a nonlinear regression model that forces the output (predicted values) to be either 0 or 1. • Logit models estimate the probability of your dependent variable to be 1 (Y=1). This is the probability that some event happens. PU/DSS/OTR Logit odelm From Stock & Watson, key concept 9.3. The logit model is: Pr(YXXXFXX 1 | 1= , 2 ,...=k β ) +0 β ( 1 +2 β 1 +βKKX 2 + ... ) 1 Pr(YXXX 1= | 1 , 2k = ,... ) 1−+(eβ0 + βXX 1 1 + β 2 2 + ...βKKX + ) 1 Pr(YXXX 1= | 1 , 2= ,... ) k ⎛ 1 ⎞ 1+ ⎜ ⎟ (⎝ eβ+0 βXX 1 1 + β 2 2 + ...βKK +X ⎠ ) Logit nd probita models are basically the same, the difference is in the distribution: • Logit – Cumulative standard logistic distribution (F) • Probit – Cumulative standard normal distribution (Φ) Both models provide similar results. PU/DSS/OTR It tests whether the combined effect, of all the variables in the model, is different from zero. If, for example, < 0.05 then the model have some relevant explanatory power, which does not mean it is well specified or at all correct. Logit: predicted probabilities After running the model: logit y_bin x1 x2 x3 x4 x5 x6 x7 Type predict y_bin_hat /*These are the predicted probabilities of Y=1 */ Here are the estimations for the first five cases, type: 1 x2 x3 x4 x5 x6 x7 y_bin_hatbrowse y_bin x Predicted probabilities To estimate the probability of Y=1 for the first row, replace the values of X into the logit regression equation. -
Discrete Choice Models with Multiple Unobserved ∗ Choice Characteristics
INTERNATIONAL ECONOMIC REVIEW Vol. 48, No. 4, November 2007 DISCRETE CHOICE MODELS WITH MULTIPLE UNOBSERVED ∗ CHOICE CHARACTERISTICS BY SUSAN ATHEY AND GUIDO W. I MBENS1 Harvard University, U.S.A. Since the pioneering work by Daniel McFadden, utility-maximization-based multinomial response models have become important tools of empirical re- searchers. Various generalizations of these models have been developed to allow for unobserved heterogeneity in taste parameters and choice characteristics. Here we investigate how rich a specification of the unobserved components is needed to rationalize arbitrary choice patterns in settings with many individual decision makers, multiple markets, and large choice sets. We find that if one restricts the utility function to be monotone in the unobserved choice characteristics, then up to two unobserved choice characteristics may be needed to rationalize the choices. 1. INTRODUCTION Since the pioneering work by Daniel McFadden in the 1970s and 1980s (1973, 1981, 1982, 1984; Hausman and McFadden, 1984) discrete (multinomial) response models have become an important tool of empirical researchers. McFadden’s early work focused on the application of logit-based choice models to transportation choices. Since then these models have been applied in many areas of economics, including labor economics, public finance, development, finance, and others. Cur- rently, one of the most active areas of application of these methods is to demand analysis for differentiated products in industrial organization. A common feature of these applications is the presence of many choices. The application of McFadden’s methods to industrial organization has inspired numerous extensions and generalizations of the basic multinomial logit model. As pointed out by McFadden, multinomial logit models have the Independence of Irrelevant Alternatives (IIA) property, so that, for example, an increase in the price for one good implies a redistribution of part of the demand for that good to the other goods in proportions equal to their original market shares. -
Chapter 3: Discrete Choice
Chapter 3: Discrete Choice Joan Llull Microeconometrics IDEA PhD Program I. Binary Outcome Models A. Introduction In this chapter we analyze several models to deal with discrete outcome vari- ables. These models that predict in which of m mutually exclusive categories the outcome of interest falls. In this section m = 2, this is, we consider binary or dichotomic variables (e.g. whether to participate or not in the labor market, whether to have a child or not, whether to buy a good or not, whether to take our kid to a private or to a public school,...). In the next section we generalize the results to multiple outcomes. It is convenient to assume that the outcome y takes the value of 1 for cat- egory A, and 0 for category B. This is very convenient because, as a result, −1 PN N i=1 yi = Pr[c A is selected]. As a consequence of this property, the coeffi- cients of a linear regression model can be interpreted as marginal effects of the regressors on the probability of choosing alternative A. And, in the non-linear models, it allows us to write the likelihood function in a very compact way. B. The Linear Probability Model A simple approach to estimate the effect of x on the probability of choosing alternative A is the linear regression model. The OLS regression of y on x provides consistent estimates of the sample-average marginal effects of regressors x on the probability of choosing alternative A. As a result, the linear model is very useful for exploratory purposes. -
Lecture 9: Logit/Probit Prof
Lecture 9: Logit/Probit Prof. Sharyn O’Halloran Sustainable Development U9611 Econometrics II Review of Linear Estimation So far, we know how to handle linear estimation models of the type: Y = β0 + β1*X1 + β2*X2 + … + ε ≡ Xβ+ ε Sometimes we had to transform or add variables to get the equation to be linear: Taking logs of Y and/or the X’s Adding squared terms Adding interactions Then we can run our estimation, do model checking, visualize results, etc. Nonlinear Estimation In all these models Y, the dependent variable, was continuous. Independent variables could be dichotomous (dummy variables), but not the dependent var. This week we’ll start our exploration of non- linear estimation with dichotomous Y vars. These arise in many social science problems Legislator Votes: Aye/Nay Regime Type: Autocratic/Democratic Involved in an Armed Conflict: Yes/No Link Functions Before plunging in, let’s introduce the concept of a link function This is a function linking the actual Y to the estimated Y in an econometric model We have one example of this already: logs Start with Y = Xβ+ ε Then change to log(Y) ≡ Y′ = Xβ+ ε Run this like a regular OLS equation Then you have to “back out” the results Link Functions Before plunging in, let’s introduce the concept of a link function This is a function linking the actual Y to the estimated Y in an econometric model We have one example of this already: logs Start with Y = Xβ+ ε Different β’s here Then change to log(Y) ≡ Y′ = Xβ + ε Run this like a regular OLS equation Then you have to “back out” the results Link Functions If the coefficient on some particular X is β, then a 1 unit ∆X Æ β⋅∆(Y′) = β⋅∆[log(Y))] = eβ ⋅∆(Y) Since for small values of β, eβ ≈ 1+β , this is almost the same as saying a β% increase in Y (This is why you should use natural log transformations rather than base-10 logs) In general, a link function is some F(⋅) s.t. -
Generalized Linear Models
CHAPTER 6 Generalized linear models 6.1 Introduction Generalized linear modeling is a framework for statistical analysis that includes linear and logistic regression as special cases. Linear regression directly predicts continuous data y from a linear predictor Xβ = β0 + X1β1 + + Xkβk.Logistic regression predicts Pr(y =1)forbinarydatafromalinearpredictorwithaninverse-··· logit transformation. A generalized linear model involves: 1. A data vector y =(y1,...,yn) 2. Predictors X and coefficients β,formingalinearpredictorXβ 1 3. A link function g,yieldingavectoroftransformeddataˆy = g− (Xβ)thatare used to model the data 4. A data distribution, p(y yˆ) | 5. Possibly other parameters, such as variances, overdispersions, and cutpoints, involved in the predictors, link function, and data distribution. The options in a generalized linear model are the transformation g and the data distribution p. In linear regression,thetransformationistheidentity(thatis,g(u) u)and • the data distribution is normal, with standard deviation σ estimated from≡ data. 1 1 In logistic regression,thetransformationistheinverse-logit,g− (u)=logit− (u) • (see Figure 5.2a on page 80) and the data distribution is defined by the proba- bility for binary data: Pr(y =1)=y ˆ. This chapter discusses several other classes of generalized linear model, which we list here for convenience: The Poisson model (Section 6.2) is used for count data; that is, where each • data point yi can equal 0, 1, 2, ....Theusualtransformationg used here is the logarithmic, so that g(u)=exp(u)transformsacontinuouslinearpredictorXiβ to a positivey ˆi.ThedatadistributionisPoisson. It is usually a good idea to add a parameter to this model to capture overdis- persion,thatis,variationinthedatabeyondwhatwouldbepredictedfromthe Poisson distribution alone. -
Discrete Choice Models and Methods.2
Greene-2140242 book November 25, 2010 22:10 17 DISCRETEQ CHOICE 17.1 INTRODUCTION This is the first of three chapters that will survey models used in microeconometrics. The analysis of individual choice that is the focus of this field is fundamentally about modeling discrete outcomes such as purchase decisions, for example whether or not to buy insurance, voting behavior, choice among a set of alternative brands, travel modes or places to live, and responses to survey questions about the strength of preferences or about self-assessed health or well-being. In these and any number of other cases, the “dependent variable” is not a quantitative measure of some economic outcome, but rather an indicator of whether or not some outcome occurred. It follows that the regression methods we have used up to this point are largely inappropriate. We turn, instead, to modeling probabilities and using econometric tools to make probabilistic statements about the occurrence of these events. We will also examine models for counts of occurrences. These are closer to familiar regression models, but are, once again, about discrete outcomes of behavioral choices. As such, in this setting as well, we will be modeling probabilities of events, rather than conditional mean functions. The models that are analyzed in this and the next chapter are built on a platform of preferences of decision makers. We take a random utility view of the choices that are observed. The decision maker is faced with a situation or set of alternatives and reveals something about their underlying preferences by the choice that he or she makes. -
Specification of the Utility Function in Discrete Choice Experiments
VALUE IN HEALTH 17 (2014) 297– 301 Available online at www.sciencedirect.com journal homepage: www.elsevier.com/locate/jval Specification of the Utility Function in Discrete Choice Experiments Marjon van der Pol, PhD, MA1,*, Gillian Currie, PhD, MPhil, MA, BComm2, Seija Kromm, MA, BA3, Mandy Ryan, PhD, MSc, BA1 1Health Economics Research Unit, University of Aberdeen, Abedeen, UK; 2University of Calgary, Calgary, AB, Canada; 3University of Toronto, Toronto, ON, Canada ABSTRACT Background: The specification of the utility function has received across the three different specifications of the utility function. Assum- limited attention within the discrete choice experiment (DCE) liter- ing a linear utility function led to much higher estimates of marginal ature. This lack of investigation is surprising given that evidence from rates of substitution (WTWs) than with nonlinear specifications. The the contingent valuation literature suggests that welfare estimates are goodness-of-fit measures indicated that nonlinear specifications were sensitive to different specifications of the utility function. Objective: superior. This study investigates the effect of different specifications of the Keywords: discrete choice experiments, specification of utility utility function on results within a DCE. Methods: The DCE elicited the function. public’s preferences for waiting time for hip and knee replacement Copyright & 2014, International Society for Pharmacoeconomics and and estimated willingness to wait (WTW). Results: The results showed Outcomes Research (ISPOR). Published by Elsevier Inc. that the WTW for the different patient profiles varied considerably Introduction nonlinear results in relative biases ranging up to 63%. Herriges and Kling [7] and Shonkwiler and Shaw [8] analyzed DCE data to Discrete choice experiments (DCEs) are increasingly used to elicit explore the sensitivity of WTP estimates to alternative utility preferences for health and health care [1]. -
A Generalized Gaussian Process Model for Computer Experiments with Binary Time Series
A Generalized Gaussian Process Model for Computer Experiments with Binary Time Series Chih-Li Sunga 1, Ying Hungb 1, William Rittasec, Cheng Zhuc, C. F. J. Wua 2 aSchool of Industrial and Systems Engineering, Georgia Institute of Technology bDepartment of Statistics, Rutgers, the State University of New Jersey cDepartment of Biomedical Engineering, Georgia Institute of Technology Abstract Non-Gaussian observations such as binary responses are common in some computer experiments. Motivated by the analysis of a class of cell adhesion experiments, we introduce a generalized Gaussian process model for binary responses, which shares some common features with standard GP models. In addition, the proposed model incorporates a flexible mean function that can capture different types of time series structures. Asymptotic properties of the estimators are derived, and an optimal predictor as well as its predictive distribution are constructed. Their performance is examined via two simulation studies. The methodology is applied to study computer simulations for cell adhesion experiments. The fitted model reveals important biological information in repeated cell bindings, which is not directly observable in lab experiments. Keywords: Computer experiment, Gaussian process model, Single molecule experiment, Uncertainty quantification arXiv:1705.02511v4 [stat.ME] 24 Sep 2018 1Joint first authors. 2Corresponding author. 1 1 Introduction Cell adhesion plays an important role in many physiological and pathological processes. This research is motivated by the analysis of a class of cell adhesion experiments called micropipette adhesion frequency assays, which is a method for measuring the kinetic rates between molecules in their native membrane environment. In a micropipette adhesion frequency assay, a red blood coated in a specific ligand is brought into contact with cell containing the native receptor for a predetermined duration, then retracted.