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---------- Forwarded message ----------
Date: Tue, 14 Nov 2000 01:06:37 -0000
From: "Marco A. Argotte"
Reply-To: psychohistory@egroups.com
To: psychohistory@egroups.com
Subject: [psychohistory] Daniel L. McFadden - 2000 Economics Nobel Prize
Hi everyone
Glad to see that we are still alive and kicking.
For those that might be interested, a brief extract from the
this webpage:
http://nobel.sdsc.edu/announcement/2000/ecoinfoen.html
{ Opening material on another laureate omitted }
Daniel L. McFadden
Daniel McFadden's most significant contribution is his development
of the economic theory and econometric methodology for analysis of
discrete choice, i.e., choice among a finite set of decision
alternatives. A recurring theme in McFadden's research is his ability
to combine economic theory, statistical methods and empirical
applications, where his ultimate goal has often been a desire to
resolve social problems.
Discrete Choice Analysis
Microdata often reflect discrete choices. In a database, information
about individuals' occupation, place of residence, or travel mode
reflects the choices they have made among a limited number of
alternatives. In economic theory, traditional demand analysis
presupposes that individual choice be represented by a continuous
variable, thereby rendering it inappropriate for studying discrete
choice behavior. Prior to McFadden's prizewinning achievements,
empirical studies of such choices lacked a foundation in economic
theory.
McFadden's Contributions
McFadden's theory of discrete choice emanates from microeconomic
theory, according to which each individual chooses a specific
alternative that maximizes his utility. However, as the researcher
cannot observe all the factors affecting individual choices, he
perceives a random variation across individuals with the same observed
characteristics. On the basis of his new theory, McFadden developed
microeconometric models that can be used, for example, to predict the
share of a population that will choose different alternatives.
McFadden's seminal contribution is his development of so-called
conditional logit analysis in 1974. In order to describe this model,
suppose that each individual in a population faces a number (say, J)
of alternatives. Let X denote the characteristics associated
with each alternative and Z the characteristics of the individuals
that the researcher can observe in his data. In a study of the choice
of travel mode, for instance, where the alternatives may be car, bus
or subway, X would then include information about time and costs,
while Z might cover data on age, income and education. But differences
among individuals and alternatives other than X and Z, although
unobservable to the researcher, also determine an individual's
utility-maximizing choice. Such characteristics are represented by
random "error terms". McFadden assumed that these random errors have a
specific statistical distribution (termed an extreme value
distribution) in the population. Under these conditions (plus some
technical assumptions), he demonstrated that the probability that
individual i will choose alternative j can be written as:
{ Formula omitted }
In this so-called multinomial logit model, e is the base of the
natural logarithm, while ... and ... are (vectors of) parameters. In
his database, the researcher can observe the variables X and Z, as
well as the alternative the individual in fact chooses. As a result,
he is able to estimate the parameters and using well-known
statistical methods. Even though logit models had been around for some
time, McFadden's derivation of the model was entirely new and was
immediately recognized as a fundamental breakthrough.
Such models are highly useful and are routinely applied in studies
of urban travel demand. They can thus be used in traffic planning to
examine the effects of policy measures as well as other social and/or
environmental changes. For example, these models can explain how
changes in price, improved accessibility or shifts in the demographic
composition of the population affect the shares of travel using
alternative means of transportation. The models are also relevant in
numerous other areas, such as in studies of the choice of dwelling,
place of residence, and education. McFadden has applied his own
methods to analyze a number of social issues, such as the demand for
residential energy, telephone services and housing for the elderly.
Methodological Elaboration
Conditional logit models have the peculiar property that the
relative probabilities of choosing between two alternatives, say,
travel by bus or car, are independent of the price and quality of
other transportation options. This property - called independence of
irrelevant alternatives (IIA) - is unrealistic in certain
applications. McFadden not only devised statistical tests to ascertain
whether IIA is satisfied, but also introduced more general models,
such as the so-called nested logit model. Here, it is assumed that
individuals' choices can be ordered in a specific sequence. For
instance, when studying decisions regarding place of residence and
type of housing, an individual is assumed to begin by choosing the
location and then the type of dwelling.
Even with these generalizations, the models are sensitive to the
specific assumptions about the distribution of unobserved
characteristics in the population. Over the last decade, McFadden has
elaborated on simulation models (the method of simulated moments)
for statistical estimation of discrete choice models allowing much
more general assumptions.
Increasingly powerful computers have enhanced the practical
applicability of these numerical methods. As a result, individuals'
discrete choices can now be portrayed with greater realism and their
decisions predicted more accurately.
{ Ending bit omitted }
****
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