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on Discrete Choice Models |
| By: | Roberto Gutierrez (StataCorp) |
| Abstract: | Mixed-effects models contain both fixed and random effects. The fixed effects are analogous to standard regression coefficients and are estimated directly. The random effects are not directly estimated but instead are summarized according to their estimated variances and covariances, known as variance components. Random effects take the form of either random intercepts or random coefficients, and the grouping structure of the data may consist of multiple levels of nested groups. In Stata, one can fit mixed models with continuous (Gaussian) responses by using xtmixed and, in Stata 10, fit mixed models with binary and count responses by using xtmelogit and xtmepoisson, respectively. All three commands have a common multiequation syntax and output, and postestimation tasks such as the prediction of random effects and likelihood-ratio comparisons of nested models also take a common form. This presentation will cover many models that one can fit using these three commands. Among these are simple random intercept models, random-coefficient models, growth curve models, and crossed-effects models. |
| Date: | 2007–08–31 |
| URL: | http://d.repec.org/n?u=RePEc:boc:asug07:12&r=dcm |
| By: | Yulia Marchenko (StataCorp) |
| Abstract: | Power analysis and sample-size determination are important components of a study design. In survival analysis, the power is directly related to the number of events observed in the study. The required sample size is therefore determined by the observed number of events. Survival data are commonly analyzed using the log-rank test or the Cox proportional hazards model. Stata 10’s new stpower command provides sample-size and power calculations for survival studies that use the log-rank test, the Cox proportional hazards model, and the parametric test comparing exponential hazard rates. It reports the number of events that must be observed in the study and accommodates unequal subject allocation between groups, nonuniform subject entry, and exponential losses to follow-up. This talk will demonstrate power, sample-size, and effect-size computations for different methods used to analyze survival data and for designs with recruitment periods and random censoring (administrative and loss to follow-up). It will also discuss building customized tables and producing graphs of power curves. |
| Date: | 2007–08–31 |
| URL: | http://d.repec.org/n?u=RePEc:boc:asug07:13&r=dcm |
| By: | Ben Dwamena (Division of Nuclear Medicine, Department of Radiology, University of Michigan Health System, Ann Ar) |
| Abstract: | This presentation will demonstrate how to perform diagnostic meta-analysis using midas , a user-written macro. midas is is comprehensive program of statistical and graphical routines for undertaking meta-analysis of diagnostic test performance in Stata. Primary data synthesis is performed within the bivariate generalized linear mixed modeling framework. Model specification, estimation and prediction are carried out with gllamm (Rabe-Hesketh et.al, spherical adaptive quadrature). Using the estimated coefficients and variance-covariance matrices, midas calculates the summary operating sensitivity and specificity (with confidence and prediction ellipses) in SROC space. Summary likelihood and odds ratios with relevant heterogeneity statistics are provided. midas facilitates extensive statistical and graphical data synthesis and exploratory analyses of unobserved heterogeneity, covariate effects, publication bias and subgroup analyses. Bayes' nomograms, likelihood ratio matrices and conditional probability plots may be obtained and used to guide clinical decision-making. |
| Date: | 2007–08–30 |
| URL: | http://d.repec.org/n?u=RePEc:boc:asug07:4&r=dcm |
| By: | Jörg Franke |
| Abstract: | In this paper a contest game with heterogeneous players is analyzed in which heterogeneity could be the consequence of past discrimination. Based on the normative perception of the heterogeneity there are two policy options to tackle this heterogeneity: either it is ignored and the contestants are treated equally, or affirmative action is implemented which compensates discriminated players. The consequences of these two policy options are analyzed for a simple two-person contest game and it is shown that the frequently criticized trade-off between affirmative action and total effort does not exist: Instead, affirmative action fosters effort incentives. A generalization to the n-person case and to a case with a partially informed contest designer yields the same result if the participation level is similar under each policy. |
| Keywords: | Asymmetric contest; affirmative action; discrimination |
| JEL: | C72 D63 I38 J78 |
| Date: | 2007–07–31 |
| URL: | http://d.repec.org/n?u=RePEc:aub:autbar:711.07&r=dcm |
| By: | Ehud Lehrer; Eilon Solan |
| Date: | 2007–08–31 |
| URL: | http://d.repec.org/n?u=RePEc:cla:levarc:122247000000001436&r=dcm |
| By: | Degryse, Hans; Laeven, Luc; Ongena, Steven |
| Abstract: | Recent theoretical models argue that a bank’s organizational structure reflects its lending technology. A hierarchically organized bank will employ mainly hard information, whereas a decentralized bank will rely more on soft information. We investigate theoretically and empirically how bank organization shapes banking competition. Our theoretical model illustrates how a lending bank’s geographical reach and loan pricing strategy is determined not only by its own organizational structure but also by organizational choices made by its rivals. We take our model to the data by estimating the impact of the lending and rival banks’ organization on the geographical reach and loan pricing of a singular, large bank in Belgium. We employ detailed contract information from more than 15,000 bank loans granted to small firms, comprising the entire loan portfolio of this large bank, and information on the organizational structure of all rival banks located in the vicinity of the borrower. We find that the organizational structures of both the rival banks and the lending bank matter for branch reach and loan pricing. The geographical footprint of the lending bank is smaller when rival banks are large and hierarchically organized. Such rival banks may rely more on hard information. Geographical reach increases when rival banks have inferior communication technology, have a wider span of organization, and are further removed from a decision unit with lending authority. Rival banks’ size and the number of layers to a decision unit also soften spatial pricing. We conclude that the organizational structure and technology of rival banks in the vicinity influence local banking competition. |
| Keywords: | authority; banking sector; competition; hierarchies; technology |
| JEL: | G21 L11 L14 |
| Date: | 2007–08 |
| URL: | http://d.repec.org/n?u=RePEc:cpr:ceprdp:6412&r=dcm |