CDR: [psychohistory] Daniel L. McFadden - 2000 Economics Nobel Prize (fwd)

[Jim Choate]

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---------- Forwarded message ----------
Date: Tue, 14 Nov 2000 01:06:37 -0000
From: "Marco A. Argotte" <margotte at theplanet.net.au>
Reply-To: psychohistory at egroups.com
To: psychohistory at 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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