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on Discrete Choice Models |
| By: | Christian Pihl; Ulrich Pötter |
| Abstract: | Demands for care are met not only by professional care services but as well by family members and friends, especially by partners and children. These personal resources are not equally available to everyone. Moreover, there may be differences in preferences when choosing between alternative care arrangements. In particular, the often intimate situations arising in nursing care will lead many to prefer close relatives or friends over professional care services. We consider preference orderings between potentially supporting persons or professional care services. The orderings were obtained by asking respondents to name the most preferred groups of persons they would ask for support in a hypothetical case of being in the need of care. This question is part of the standard panel questionnaire program of the Socio-Economic Panel Study (SOEP) and is thus regularly posed. Similar questions to state preferences among a given set of potential alternatives can be found in many social surveys. The analysis of such data requires suitable methods that take their special features into account. We present a method to visualize partial rankings. The method is then used to present rankings over partial preferences, where the set of alternatives depends on the availability of networks of relatives, partners and friends. The method visualizes rank data using the skeleton of polytopes, by which the complexity of given preferences can faithfully be represented . Using the method for responses to the question about nursing care clearly demonstrates a shortcoming of this type of questions in general survey questionnaires. |
| Keywords: | social networks, rank data, polytope, social support |
| Date: | 2010 |
| URL: | http://d.repec.org/n?u=RePEc:diw:diwsop:diw_sp303&r=dcm |
| By: | Abidoye, Babatunde; Herriges, Joseph A.; Tobias, Justin |
| Abstract: | Random Utility Maximization (RUM) models of recreation demand are typically plagued by limited information on environmental and other attributes characterizing the available sites in the choice set. To the extent that these unobserved site attributes are correlated with the observed characteristics and/or the key travel cost variable, the resulting parameter estimates and subsequent welfare calculations are likely to be biased. In this paper we develop a Bayesian approach to estimating a RUM model that incorporates a full set of alternative specific constants, insulating the key travel cost parameter from the influence of the unobserved site attributes. In contrast to estimation procedures recently outlined in Murdock (2006), the posterior simulator we propose (combining data augmentation and Gibbs sampling techniques) can be used in the more general mixed logit framework in which some parameters of the conditional utility function are random. Following a series of generated data experiments to illustrate the performance of the simulator, we apply the estimation procedures to data from the Iowa Lakes Project. In contrast to an earlier study using the same data (Egan \textit{et al.} \cite{eganetal}), we find that, with the addition of a full set of alternative specific constants, water quality attributes no longer appear to influence the choice of where to recreate. |
| Keywords: | nonmarket valuation; water quality; discrete choice |
| JEL: | C25 Q25 Q51 |
| Date: | 2010–05–31 |
| URL: | http://d.repec.org/n?u=RePEc:isu:genres:31559&r=dcm |
| By: | DHAENE, Geert (K.U.Leuven, Department of Economics, B-3000 Leuven, Belgium); JOCHMANS, Koen (Center for Operations Research and Econometrics (CORE), UniversitŽ catholique de Louvain (UCL), Louvain la Neuve, Belgium) |
| Abstract: | We propose a jackknife for reducing the order of the bias of maximum likelihood estimates of nonlinear dynamic fixed-effect panel models. In its simplest form, the half-panel jackknife, the estimatorisjust 2 ö 1/2,where ö!istheMLEfromthefullpaneland 1/2 istheaverageofthe two half-panel MLEs, each using T/2 time periods and all N cross-sectional units. This estimatoreliminates the first-order bias of ö . The order of the bias is further reduced if two partitions of the panel are used, for example, two half-panels and three 1/3-panels, and the corresponding MLEs.On further partitioning the panel, any order of bias reduction can be achieved. The split-panel jackknife estimators are asymptotically normal, centered at the true value, with variance equal to that of the MLE under asymptotics where T is allowed to grow slowly with N. In analogous fashion, the split-panel jackknife reduces the bias of the profile likelihood and the bias of marginal-effect estimates. Simulations in fixed-effect dynamic discrete-choice models with small T show that the split-panel jackknife effectively reduces the bias and mean squared error of the MLE, and yields confidence intervals with much better coverage. |
| Keywords: | jackknife, asymptotic bias correction, dynamic panel data, fixed effects |
| JEL: | C13 C14 C22 C23 |
| Date: | 2010–01–01 |
| URL: | http://d.repec.org/n?u=RePEc:cor:louvco:2010003&r=dcm |
| By: | Beckert, Walter |
| Abstract: | This paper formalizes an empirically implementable framework for the definition of local antitrust markets in retail markets. This framework rests on a demand model that captures the trade-off between distance and pecuniary cost across alternative shopping destinations within local markets. The paper develops, and presents estimation results for, an empirical demand model at the store level for groceries in the UK. -- |
| Keywords: | Geographic antitrust market definition,discrete choice |
| JEL: | L11 L13 L41 L81 C35 C73 |
| Date: | 2010 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:ifwedp:201016&r=dcm |