How revealing is revealed preference?

This lecture address the following two key criticisms of the empirical application of revealed preference theory: When the RP conditions do not reject, they do not provide precise predictions; and when they do reject, they do not help characterize the nature of irrationality or the degree/direction of changing tastes. Recent developments in the application of RP theory are shown to have rendered these criticisms unfounded. A powerful test of rationality is available that also provides a natural characterization of changing tastes. Tight bounds on demand responses and on the welfare costs of relative price and tax changes are also available and are shown to work well in practice

Hybrid Choice Models: Progress and Challenges

We discuss the development of predictive choice models that go beyond the random utility model in its narrowest formulation. Such approaches incorporate several elements of cognitive process that have been identified as important to the choice process, including strong dependence on history and context, perception formation, and latent constraints. A flexible and practical hybrid choice model is presented that integrates many types of discrete choice modeling methods, draws on different types of data, and allows for flexible disturbances and explicit modeling of latent psychological explanatory variables, heterogeneity, and latent segmentation. Both progress and challenges related to the development of the hybrid choice model are presented.

A kernel-based framework for learning graded relations from data

Driven by a large number of potential applications in areas like bioinformatics, information retrieval and social network analysis, the problem setting of inferring relations between pairs of data objects has recently been investigated quite intensively in the machine learning community. To this end, current approaches typically consider datasets containing crisp relations, so that standard classification methods can be adopted. However, relations between objects like similarities and preferences are often expressed in a graded manner in real-world applications. A general kernel-based framework for learning relations from data is introduced here. It extends existing approaches because both crisp and graded relations are considered, and it unifies existing approaches because different types of graded relations can be modeled, including symmetric and reciprocal relations. This framework establishes important links between recent developments in fuzzy set theory and machine learning. Its usefulness is demonstrated through various experiments on synthetic and real-world data.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Pooling stated and revealed preference data in the presence of RP endogeneity

Pooled discrete choice models combine revealed preference (RP) data and stated preference (SP) data to exploit advantages of each. SP data is often treated with suspicion because consumers may respond differently in a hypothetical survey context than they do in the marketplace. However, models built on RP data can suffer from endogeneity bias when attributes that drive consumer choices are unobserved by the modeler and correlated with observed variables. Using a synthetic data experiment, we test the performance of pooled RP–SP models in recovering the preference parameters that generated the market data under conditions that choice modelers are likely to face, including (1) when there is potential for endogeneity problems in the RP data, such as omitted variable bias, and (2) when consumer willingness to pay for attributes may differ from the survey context to the market context. We identify situations where pooling RP and SP data does and does not mitigate each data source’s respective weaknesses. We also show that the likelihood ratio test, which has been widely used to determine whether pooling is statistically justifiable, (1) can fail to identify the case where SP context preference differences and RP endogeneity bias shift the parameter estimates of both models in the same direction and magnitude and (2) is unreliable when the product attributes are fixed within a small number of choice sets, which is typical of automotive RP data. Our findings offer new insights into when pooling data sources may or may not be advisable for accurately estimating market preference parameters, including consideration of the conditions and context under which the data were generated as well as the relative balance of information between data sources.This work was supported in part by a grant from the Link Foundation, a grant from the National Science Foundation # 1064241 , and a grant from Ford Motor Company. The opinions expressed are those of the authors and not necessarily those of the sponsors.Accepted manuscrip

Implicit prices of indigenous cattle traits in central Ethiopia: Application of revealed and stated preference approaches

The diversity of animal genetic resources has a quasi-public good nature that makes market prices inadequate indicator of its economic worth. Applying the characteristics theory of value, this research estimated the relative economic worth of the attributes of cattle genetic resources in central Ethiopia. Transaction level data were collected over four seasons in a year and choice experiment survey was done in five markets to generate data on both revealed and stated preferences of cattle buyers. Heteroscedasticity efficient estimation and random parameters logit were employed to analyse the data. The results essentially show that attributes related to the subsistence functions of cattle are more valued than attributes that directly influence marketable products of the animals. The findings imply the strong need to invest on improvement of attributes of cattle in the study area that enhance the subsistence functions of cattle that their owners accord higher priority to support their livelihoods than they do to tradable products

A revealed preference analysis of the rational addiction model.

We provide a revealed preference analysis of the rational addiction model. The revealed preference approach avoids the need to impose an, a priori unverifiable, functional form on the underlying utility function. Our results extend the previously established revealed preference characterizations for the life cycle model and the one-lag habits model. We show that our characterization is easily testable by means of linear programming methods and we demonstrate its practical usefulness by means of an application to Spanish household consumption data.

A revealed preference analysis of the rational addiction model

We provide a revealed preference analysis of the rational addiction model. The revealed preference approach avoids the need to impose an, a priori unverifiable, functional form on the underlying utility function. Our results extend the previously established revealed preference characterizations for the life cycle model and the one-lag habits model. We show that our characterization is easily testable by means of linear programming methods and we demonstrate its practical usefulness by means of an application to Spanish household consumption data.

Comparison of two sampling protocols and four home-range estimators using radio-tracking data from urban badgers Meles meles

Radio-telemetry is often the method of choice for studies of species whose behaviour is difficult to observe directly. However, considerable debate has ensued about the best way of deriving home-range estimates. In recent years, kernel estimators have become the most widely used method, together with the oldest and simplest method, the minimum convex polygon (MCP). More recently, it has been suggested that the local convex hull (LCH) might be more appropriate than kernel methods in cases where an animal’s home range includes a priori inaccessible areas. Yet another method, the Brownian bridge (BB), explicitly uses autocorrelated data to determine movement paths and, ultimately, home ranges or migration routes of animals. Whereas several studies have used simulation techniques to compare these different methods, few have used data from real animals. We used radio-telemetric data from urban badgers Meles meles to compare two sampling protocols (10-minute vs at least 30-minute inter-fix intervals) and four home-range estimators (MCP, fixed kernels (FK), LCH and BB). We used a multi-response permutation procedure and randomisation tests to compare overall patterns of fixes and degree of overlap of home ranges estimated using data from different sampling protocols, and a general linear model to compare the influence of sampling protocols and home-range estimator on the size of habitat patches. The shape of the estimated home ranges was influenced by sampling protocol in some cases. By contrast, the sizes and proportions of different habitats within home ranges were influenced by estimator type but not by sampling protocol. LCH performed consistently better than FK, and is especially appropriate for patchy study areas containing frequent no-go zones. However, we recommend using LCH in combination with other methods to estimate total range size, because LCH tended to produce smaller estimates than any other method. Results relating to BB are preliminary but suggest that this method is unsuitable for species in which range size is small compared to average travel speed.Marie-Curie Intra-European Fellowship (BSSUB - 24007); Defra WSC contract WM0304; Wildlife Biology granted the permit to upload the article to this repositor