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Variable Set (variable + set)

Distribution by Scientific Domains
Business, Economics, Finance and Accounting80%

Selected Abstracts

Panel Data Discrete Choice Models with Lagged Dependent Variables

ECONOMETRICA, Issue 4 2000
Bo E. Honoré
In this paper, we consider identification and estimation in panel data discrete choice models when the explanatory variable set includes strictly exogenous variables, lags of the endogenous dependent variable as well as unobservable individual-specific effects. For the binary logit model with the dependent variable lagged only once, Chamberlain (1993) gave conditions under which the model is not identified. We present a stronger set of conditions under which the parameters of the model are identified. The identification result suggests estimators of the model, and we show that these are consistent and asymptotically normal, although their rate of convergence is slower than the inverse of the square root of the sample size. We also consider identification in the semiparametric case where the logit assumption is relaxed. We propose an estimator in the spirit of the conditional maximum score estimator (Manski (1987)) and we show that it is consistent. In addition, we discuss an extension of the identification result to multinomial discrete choice models, and to the case where the dependent variable is lagged twice. Finally, we present some Monte Carlo evidence on the small sample performance of the proposed estimators for the binary response model. [source]

A Comparison of Neural Network, Statistical Methods, and Variable Choice for Life Insurers' Financial Distress Prediction

JOURNAL OF RISK AND INSURANCE, Issue 3 2006
Patrick L. Brockett
This study examines the effect of the statistical/mathematical model selected and the variable set considered on the ability to identify financially troubled life insurers. Models considered are two artificial neural network methods (back-propagation and learning vector quantization (LVQ)) and two more standard statistical methods (multiple discriminant analysis and logistic regression analysis). The variable sets considered are the insurance regulatory information system (IRIS) variables, the financial analysis solvency tracking (FAST) variables, and Texas early warning information system (EWIS) variables, and a data set consisting of twenty-two variables selected by us in conjunction with the research staff at TDI and a review of the insolvency prediction literature. The results show that the back-propagation (BP) and LVQ outperform the traditional statistical approaches for all four variable sets with a consistent superiority across the two different evaluation criteria (total misclassification cost and resubstitution risk criteria), and that the twenty-two variables and the Texas EWIS variable sets are more efficient than the IRIS and the FAST variable sets for identification of financially troubled life insurers in most comparisons. [source]

Database Comparison of the Adult-to-Adult Living Donor Liver Transplantation Cohort Study (A2ALL) and the SRTR U.S. Transplant Registry,

AMERICAN JOURNAL OF TRANSPLANTATION, Issue 7 2010
B. W. Gillespie
Data submitted by transplant programs to the Organ Procurement and Transplantation Network (OPTN) are used by the Scientific Registry of Transplant Recipients (SRTR) for policy development, performance evaluation and research. This study compared OPTN/SRTR data with data extracted from medical records by research coordinators from the nine-center A2ALL study. A2ALL data were collected independently of OPTN data submission (48 data elements among 785 liver transplant candidates/recipients; 12 data elements among 386 donors). At least 90% agreement occurred between OPTN/SRTR and A2ALL for 11/29 baseline recipient elements, 4/19 recipient transplant or follow-up elements and 6/12 donor elements. For the remaining recipient and donor elements, >10% of values were missing in OPTN/SRTR but present in A2ALL, confirming that missing data were largely avoidable. Other than variables required for allocation, the percentage missing varied widely by center. These findings support an expanded focus on data quality control by OPTN/SRTR for a broader variable set than those used for allocation. Center-specific monitoring of missing values could substantially improve the data. [source]

A Comparison of Neural Network, Statistical Methods, and Variable Choice for Life Insurers' Financial Distress Prediction

JOURNAL OF RISK AND INSURANCE, Issue 3 2006
Patrick L. Brockett
This study examines the effect of the statistical/mathematical model selected and the variable set considered on the ability to identify financially troubled life insurers. Models considered are two artificial neural network methods (back-propagation and learning vector quantization (LVQ)) and two more standard statistical methods (multiple discriminant analysis and logistic regression analysis). The variable sets considered are the insurance regulatory information system (IRIS) variables, the financial analysis solvency tracking (FAST) variables, and Texas early warning information system (EWIS) variables, and a data set consisting of twenty-two variables selected by us in conjunction with the research staff at TDI and a review of the insolvency prediction literature. The results show that the back-propagation (BP) and LVQ outperform the traditional statistical approaches for all four variable sets with a consistent superiority across the two different evaluation criteria (total misclassification cost and resubstitution risk criteria), and that the twenty-two variables and the Texas EWIS variable sets are more efficient than the IRIS and the FAST variable sets for identification of financially troubled life insurers in most comparisons. [source]