External links
- Personal homepage on Berkeley website
- Kenneth E. Train publications indexed by Google Scholar
- "Kenneth Train". EconPapers.
- "Kenneth Train". JSTOR.
Kenneth E. Train | |
|---|---|
| Born | November 14, 1951 |
| Alma mater | Harvard University University of California, Berkeley |
| Scientific career | |
| Fields | Economics |
| Institutions | University of California, Berkeley |
Kenneth E. Train (born November 14, 1951) is an Adjunct Professor of Economics at the University of California, Berkeley, United States. He is also Vice President of NERA Economic Consulting, Inc. in San Francisco, California. He received a Bachelors in Economics at Harvard and PhD from UC Berkeley. He specializes in econometrics and regulation, with applications in energy, environmental studies, telecommunications and transportation.
Kenneth Train has published three books and more than sixty articles. His most recent book, Discrete Choice Methods with Simulation, deals with a new area in econometrics. His software for mixed logit estimation, which is distributed free on his university website, has been used by researchers worldwide, and several commercial statistical packages have recently added mixed logit routines that reach an even larger audience.
Professor Train has served as an expert witness in court cases and regulatory proceedings, on the editorial board of academic journals, and as chair of the Center for Regulatory Policy at UC Berkeley. He has received awards for his teaching and research, including Outstanding Teaching awards from both the undergraduate and graduate economics students' associations at UC Berkeley, an Outstanding Achievement award for his work on energy conservation program evaluation, and the Richard Stone prize in applied econometrics.
Econometrics is the application of statistical methods to economic data in order to give empirical content to economic relationships. More precisely, it is "the quantitative analysis of actual economic phenomena based on the concurrent development of theory and observation, related by appropriate methods of inference". An introductory economics textbook describes econometrics as allowing economists "to sift through mountains of data to extract simple relationships". The first known use of the term "econometrics" was by Polish economist Paweł Ciompa in 1910. Jan Tinbergen is considered by many to be one of the founding fathers of econometrics. Ragnar Frisch is credited with coining the term in the sense in which it is used today.
Daniel Little McFadden is an American econometrician who shared the 2000 Nobel Memorial Prize in Economic Sciences with James Heckman. McFadden's share of the prize was "for his development of theory and methods for analyzing discrete choice". He is the Presidential Professor of Health Economics at the University of Southern California and Professor of the Graduate School at University of California, Berkeley.
'Conjoint analysis' is a survey-based statistical technique used in market research that helps determine how people value different attributes that make up an individual product or service.
In statistics, a vector of random variables is heteroscedastic if the variability of the random disturbance is different across elements of the vector. Here, variability could be quantified by the variance or any other measure of statistical dispersion. Thus heteroscedasticity is the absence of homoscedasticity. A typical example is the set of observations of income in different cities.
Jerry Allen Hausman is the John and Jennie S. MacDonald Professor of Economics at the Massachusetts Institute of Technology and a notable econometrician. He has published numerous influential papers in microeconometrics. Hausman is the recipient of several prestigious awards including the John Bates Clark Medal in 1985 and the Frisch Medal in 1980.
Articles in economics journals are usually classified according to the JEL classification codes, a system originated by the Journal of Economic Literature. The JEL is published quarterly by the American Economic Association (AEA) and contains survey articles and information on recently published books and dissertations. The AEA maintains EconLit, a searchable data base of citations for articles, books, reviews, dissertations, and working papers classified by JEL codes for the years from 1969. A recent addition to EconLit is indexing of economics-journal articles from 1886 to 1968 parallel to the print series Index of Economic Articles.
In economics, discrete choice models, or qualitative choice models, describe, explain, and predict choices between two or more discrete alternatives, such as entering or not entering the labor market, or choosing between modes of transport. Such choices contrast with standard consumption models in which the quantity of each good consumed is assumed to be a continuous variable. In the continuous case, calculus methods can be used to determine the optimum amount chosen, and demand can be modeled empirically using regression analysis. On the other hand, discrete choice analysis examines situations in which the potential outcomes are discrete, such that the optimum is not characterized by standard first-order conditions. Thus, instead of examining “how much” as in problems with continuous choice variables, discrete choice analysis examines “which one.” However, discrete choice analysis can also be used to examine the chosen quantity when only a few distinct quantities must be chosen from, such as the number of vehicles a household chooses to own and the number of minutes of telecommunications service a customer decides to purchase. Techniques such as logistic regression and probit regression can be used for empirical analysis of discrete choice.
Mixed logit is a fully general statistical model for examining discrete choices. The motivation for the mixed logit model arises from the limitations of the standard logit model. The standard logit model has three primary limitations, which mixed logit solves: "It obviates the three limitations of standard logit by allowing for random taste variation, unrestricted substitution patterns, and correlation in unobserved factors over time." Mixed logit can also utilize any distribution for the random coefficients, unlike probit which is limited to the normal distribution. It has been shown that a mixed logit model can approximate to any degree of accuracy any true random utility model of discrete choice, given an appropriate specification of variables and distribution of coefficients."
Choice modelling attempts to model the decision process of an individual or segment via revealed preferences or stated preferences made in a particular context or contexts. Typically, it attempts to use discrete choices in order to infer positions of the items on some relevant latent scale. Indeed many alternative models exist in econometrics, marketing, sociometrics and other fields, including utility maximization, optimization applied to consumer theory, and a plethora of other identification strategies which may be more or less accurate depending on the data, sample, hypothesis and the particular decision being modelled. In addition, choice modelling is regarded as the most suitable method for estimating consumers' willingness to pay for quality improvements in multiple dimensions.
Although the concept choice models is widely understood and practiced these days, it is often difficult to acquire hands-on knowledge in simulating choice models. While many stat packages provide useful tools to simulate, researchers attempting to test and simulate new choice models with data often encounter problems from as simple as scaling parameter to misspecification. This article goes beyond simply defining discrete choice models. Rather, it aims at providing a comprehensive overview of how to simulate such models in computer.
A limited dependent variable is a variable whose range of possible values is "restricted in some important way." In econometrics, the term is often used when estimation of the relationship between the limited dependent variable of interest and other variables requires methods that take this restriction into account. For example, this may arise when the variable of interest is constrained to lie between zero and one, as in the case of a probability, or is constrained to be positive, as in the case of wages or hours worked.
Frank Trevor Denton, is a Canadian economist. He is Professor Emeritus in the Department of Economics at McMaster University in Hamilton, Ontario, Canada. His published work includes contributions to applied econometrics, population economics, labour economics, health economics, energy economics, and demography.
The UC Irvine Institute of Transportation Studies (ITS), is a University of California organized research unit with sister branches at UC Davis, and UC Berkeley. ITS was established to foster research, education, and training in the field of transportation. UC Irvine ITS is located on the fourth floor of the Anteater Instruction and Research Building at University of California, Irvine's main Campus, and also houses the UC Irvine Transportation Science graduate studies program.
LIMDEP is an econometric and statistical software package with a variety of estimation tools. In addition to the core econometric tools for analysis of cross sections and time series, LIMDEP supports methods for panel data analysis, frontier and efficiency estimation and discrete choice modeling. The package also provides a programming language to allow the user to specify, estimate and analyze models that are not contained in the built in menus of model forms.
NLOGIT is an extension of the econometric and statistical software package LIMDEP. In addition to the estimation tools in LIMDEP, NLOGIT provides programs for estimation, model simulation and analysis of multinomial choice data, such as brand choice, transportation mode and for survey and market data in which consumers choose among a set of competing alternatives.
Michael Patrick Keane is an American/Australian economist who is currently professor of economics and Australian Research Council laureate fellow at the University of New South Wales. From 2012 to 2017 he was the Nuffield Professor of Economics at the University of Oxford and a professorial fellow of Nuffield College. He is considered one of the world's leading experts in the fields of Choice Modelling, structural modelling, simulation estimation, and panel data econometrics.
John Philip Rust is an American economist and econometrician. John Rust received his PhD from MIT in 1983 and taught at the University of Wisconsin, Yale University and University of Maryland before joining Georgetown University in 2012. John Rust was awarded Frisch Medal in 1992 and became the fellow of Econometric Society in 1993.
Control functions are statistical methods to correct for endogeneity problems by modelling the endogeneity in the error term. The approach thereby differs in important ways from other models that try to account for the same econometric problem. Instrumental variables, for example, attempt to model the endogenous variable X as an often invertible model with respect to a relevant and exogenous instrument Z. Panel analysis uses special data properties to difference out unobserved heterogeneity that is assumed to be fixed over time.
Juan de Dios Ortúzar is Emeritus Professor at the Pontifical Catholic University of Chile (PUC), Santiago, Chile. He specializes in discrete choice models, valuation of externalities, design and collection of mobility and preference surveys and transportation forecasting. He received a B.Sc. in mathematics and a civil engineering degree at PUC, and an M.Sc. and Ph.D. from the University of Leeds.
Peter Arcidiacono is an American economist and econometrician. He received his PhD from Wisconsin in 1999 and has taught at Duke University ever since. He became a fellow of the Econometric Society in 2018.
