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- Articles and reports: 11F0027M2005036Geography: CanadaDescription:
Burkart and Ellingsen (2004) develop a model of trade credit and bank credit rationing which predicts that trade credit will be used by medium-wealth and low-wealth firms to help ease bank credit rationing. This paper tests this and other predictions of the Burkart and Ellingsen model using a large sample of more than 28,000 Canadian firms. The author uses an endogenous method to divide the firms into the appropriate wealth categories rather than arbitrarily selecting firms likely to be credit-rationed. The data support the main predictions of the model quite well. The author finds that medium-wealth firms substitute trade credit for bank credit consistent with using it to alleviate bank credit rationing. The low-wealth firms use trade credit but it is positively linked to bank credit, suggesting those firms are constrained in both bank credit and trade credit markets, and so cannot use trade credit to adjust as much to negative shocks. The findings also suggest that there are very few unconstrained, high-wealth Canadian firms. The author also finds low-wealth, declining and distressed firms supply proportionally more trade credit than firms with healthier balance sheets.
Release date: 2005-11-04 - Articles and reports: 12-001-X20050018083Description:
The advent of computerized record linkage methodology has facilitated the conduct of cohort mortality studies in which exposure data in one database are electronically linked with mortality data from another database. This, however, introduces linkage errors due to mismatching an individual from one database with a different individual from the other database. In this article, the impact of linkage errors on estimates of epidemiological indicators of risk such as standardized mortality ratios and relative risk regression model parameters is explored. It is shown that the observed and expected number of deaths are affected in opposite direction and, as a result, these indicators can be subject to bias and additional variability in the presence of linkage errors.
Release date: 2005-07-21 - Articles and reports: 12-001-X20050018089Description:
We use hierarchical Bayesian models to analyze body mass index (BMI) data of children and adolescents with nonignorable nonresponse from the Third National Health and Nutrition Examination Survey (NHANES III). Our objective is to predict the finite population mean BMI and the proportion of respondents for domains formed by age, race and sex (covariates in the regression models) in each of thirty five large counties, accounting for the nonrespondents. Markov chain Monte Carlo methods are used to fit the models (two selection and two pattern mixture) to the NHANES III BMI data. Using a deviance measure and a cross-validation study, we show that the nonignorable selection model is the best among the four models. We also show that inference about BMI is not too sensitive to the model choice. An improvement is obtained by including a spline regression into the selection model to reflect changes in the relationship between BMI and age.
Release date: 2005-07-21 - Articles and reports: 12-001-X20050018091Description:
Procedures for constructing vectors of nonnegative regression weights are considered. A vector of regression weights in which initial weights are the inverse of the approximate conditional inclusion probabilities is introduced. Through a simulation study, the weighted regression weights, quadratic programming weights, raking ratio weights, weights from logit procedure, and weights of a likelihood-type are compared.
Release date: 2005-07-21 - Articles and reports: 12-001-X20050018092Description:
When there is auxiliary information in survey sampling, the design based "optimal (regression) estimator" of a finite population total/mean is known to be (at least asymptotically) more efficient than the corresponding GREG estimator. We will illustrate this by some simulations with stratified sampling from skewed populations. The GREG estimator was originally constructed using an assisting linear superpopulation model. It may also be seen as a calibration estimator; i.e., as a weighted linear estimator, where the weights obey the calibration equation and, with that restriction, are as close as possible to the original "Horvitz-Thompson weights" (according to a suitable distance). We show that the optimal estimator can also be seen as a calibration estimator in this respect, with a quadratic distance measure closely related to the one generating the GREG estimator. Simple examples will also be given, revealing that this new measure is not always easily obtained.
Release date: 2005-07-21 - Articles and reports: 12-001-X20050018094Description:
Nested error regression models are frequently used in small-area estimation and related problems. Standard regression model selection criterion, when applied to nested error regression models, may result in inefficient model selection methods. We illustrate this point by examining the performance of the C_P statistic through a Monte Carlo simulation study. The inefficiency of the C_P statistic may, however, be rectified by a suitable transformation of the data.
Release date: 2005-07-21 - 7. Robust generalized regression estimation ArchivedArticles and reports: 12-001-X20040027752Description:
The Best Linear Unbiased (BLU) estimator (or predictor) of a population total is based on the following two assumptions: i) the estimation model underlying the BLU estimator is correctly specified and ii) the sampling design is ignorable with respect to the estimation model. In this context, an estimator is robust if it stays close to the BLU estimator when both assumptions hold and if it keeps good properties when one or both assumptions are not fully satisfied. Robustness with respect to deviations from assumption (i) is called model robustness while robustness with respect to deviations from assumption (ii) is called design robustness. The Generalized Regression (GREG) estimator is often viewed as being robust since its property of being Asymptotically Design Unbiased (ADU) is not dependent on assumptions (i) and (ii). However, if both assumptions hold, the GREG estimator may be far less efficient than the BLU estimator and, in that sense, it is not robust. The relative inefficiency of the GREG estimator as compared to the BLU estimator is caused by widely dispersed design weights. To obtain a design-robust estimator, we thus propose a compromise between the GREG and the BLU estimators. This compromise also provides some protection against deviations from assumption (i). However, it does not offer any protection against outliers, which can be viewed as a consequence of a model misspecification. To deal with outliers, we use the weighted generalized M-estimation technique to reduce the influence of units with large weighted population residuals. We propose two practical ways of implementing M-estimators for multipurpose surveys; either the weights of influential units are modified and a calibration approach is used to obtain a single set of robust estimation weights or the values of influential units are modified. Some properties of the proposed approach are evaluated in a simulation study using a skewed finite population created from real survey data.
Release date: 2005-02-03 - Articles and reports: 12-001-X20040027758Description:
In this article, we study the use of Bayesian neural networks in finite population estimation.We propose estimators for finite population mean and the associated mean squared error. We also propose to use the student t-distribution to model the disturbances in order to accommodate extreme observations that are often present in the data from social sample surveys. Numerical results show that Bayesian neural networks have made a significant improvement in finite population estimation over linear regression based methods
Release date: 2005-02-03 - Articles and reports: 75F0002M2005001Description:
Comparative analysis of poverty dynamics incidence - transitions, and persistence - can yield important insights about the nature of poverty and the effectiveness of alternative policy responses. This manuscript compares poverty dynamics in four advanced industrial countries (Canada, unified Germany, Great Britain, and the United States) for overlapping six-year periods in the 1990s. The data indicate that poverty persistence is higher in North America than in Europe; for example, despite high incidence, poverty in Great Britain is relatively transitory. Most poverty transitions, and the prevalence of chronic poverty, are associated with employment instability and family dissolution in all four countries. The results also suggest that differences in social policy are crucial for the observed differences in poverty incidence and persistence between Europe and North America.
Release date: 2005-01-31 - 10. Tracing and non-response adjustment for the Longitudinal Survey of Immigrants to Canada ArchivedArticles and reports: 11-522-X20030017595Geography: CanadaDescription:
This paper discusses challenges faced in locating recent immigrants and implementing strategies to increase response rates for the Longitudinal Survey of Immigrants to Canada (LSIC). It also presents a model-assisted technique for adjusting for non-response, based on the approach proposed by Eltinge-Yanseneh to define adjustment classes.
Release date: 2005-01-26
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Analysis (16)
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- Articles and reports: 11F0027M2005036Geography: CanadaDescription:
Burkart and Ellingsen (2004) develop a model of trade credit and bank credit rationing which predicts that trade credit will be used by medium-wealth and low-wealth firms to help ease bank credit rationing. This paper tests this and other predictions of the Burkart and Ellingsen model using a large sample of more than 28,000 Canadian firms. The author uses an endogenous method to divide the firms into the appropriate wealth categories rather than arbitrarily selecting firms likely to be credit-rationed. The data support the main predictions of the model quite well. The author finds that medium-wealth firms substitute trade credit for bank credit consistent with using it to alleviate bank credit rationing. The low-wealth firms use trade credit but it is positively linked to bank credit, suggesting those firms are constrained in both bank credit and trade credit markets, and so cannot use trade credit to adjust as much to negative shocks. The findings also suggest that there are very few unconstrained, high-wealth Canadian firms. The author also finds low-wealth, declining and distressed firms supply proportionally more trade credit than firms with healthier balance sheets.
Release date: 2005-11-04 - Articles and reports: 12-001-X20050018083Description:
The advent of computerized record linkage methodology has facilitated the conduct of cohort mortality studies in which exposure data in one database are electronically linked with mortality data from another database. This, however, introduces linkage errors due to mismatching an individual from one database with a different individual from the other database. In this article, the impact of linkage errors on estimates of epidemiological indicators of risk such as standardized mortality ratios and relative risk regression model parameters is explored. It is shown that the observed and expected number of deaths are affected in opposite direction and, as a result, these indicators can be subject to bias and additional variability in the presence of linkage errors.
Release date: 2005-07-21 - Articles and reports: 12-001-X20050018089Description:
We use hierarchical Bayesian models to analyze body mass index (BMI) data of children and adolescents with nonignorable nonresponse from the Third National Health and Nutrition Examination Survey (NHANES III). Our objective is to predict the finite population mean BMI and the proportion of respondents for domains formed by age, race and sex (covariates in the regression models) in each of thirty five large counties, accounting for the nonrespondents. Markov chain Monte Carlo methods are used to fit the models (two selection and two pattern mixture) to the NHANES III BMI data. Using a deviance measure and a cross-validation study, we show that the nonignorable selection model is the best among the four models. We also show that inference about BMI is not too sensitive to the model choice. An improvement is obtained by including a spline regression into the selection model to reflect changes in the relationship between BMI and age.
Release date: 2005-07-21 - Articles and reports: 12-001-X20050018091Description:
Procedures for constructing vectors of nonnegative regression weights are considered. A vector of regression weights in which initial weights are the inverse of the approximate conditional inclusion probabilities is introduced. Through a simulation study, the weighted regression weights, quadratic programming weights, raking ratio weights, weights from logit procedure, and weights of a likelihood-type are compared.
Release date: 2005-07-21 - Articles and reports: 12-001-X20050018092Description:
When there is auxiliary information in survey sampling, the design based "optimal (regression) estimator" of a finite population total/mean is known to be (at least asymptotically) more efficient than the corresponding GREG estimator. We will illustrate this by some simulations with stratified sampling from skewed populations. The GREG estimator was originally constructed using an assisting linear superpopulation model. It may also be seen as a calibration estimator; i.e., as a weighted linear estimator, where the weights obey the calibration equation and, with that restriction, are as close as possible to the original "Horvitz-Thompson weights" (according to a suitable distance). We show that the optimal estimator can also be seen as a calibration estimator in this respect, with a quadratic distance measure closely related to the one generating the GREG estimator. Simple examples will also be given, revealing that this new measure is not always easily obtained.
Release date: 2005-07-21 - Articles and reports: 12-001-X20050018094Description:
Nested error regression models are frequently used in small-area estimation and related problems. Standard regression model selection criterion, when applied to nested error regression models, may result in inefficient model selection methods. We illustrate this point by examining the performance of the C_P statistic through a Monte Carlo simulation study. The inefficiency of the C_P statistic may, however, be rectified by a suitable transformation of the data.
Release date: 2005-07-21 - 7. Robust generalized regression estimation ArchivedArticles and reports: 12-001-X20040027752Description:
The Best Linear Unbiased (BLU) estimator (or predictor) of a population total is based on the following two assumptions: i) the estimation model underlying the BLU estimator is correctly specified and ii) the sampling design is ignorable with respect to the estimation model. In this context, an estimator is robust if it stays close to the BLU estimator when both assumptions hold and if it keeps good properties when one or both assumptions are not fully satisfied. Robustness with respect to deviations from assumption (i) is called model robustness while robustness with respect to deviations from assumption (ii) is called design robustness. The Generalized Regression (GREG) estimator is often viewed as being robust since its property of being Asymptotically Design Unbiased (ADU) is not dependent on assumptions (i) and (ii). However, if both assumptions hold, the GREG estimator may be far less efficient than the BLU estimator and, in that sense, it is not robust. The relative inefficiency of the GREG estimator as compared to the BLU estimator is caused by widely dispersed design weights. To obtain a design-robust estimator, we thus propose a compromise between the GREG and the BLU estimators. This compromise also provides some protection against deviations from assumption (i). However, it does not offer any protection against outliers, which can be viewed as a consequence of a model misspecification. To deal with outliers, we use the weighted generalized M-estimation technique to reduce the influence of units with large weighted population residuals. We propose two practical ways of implementing M-estimators for multipurpose surveys; either the weights of influential units are modified and a calibration approach is used to obtain a single set of robust estimation weights or the values of influential units are modified. Some properties of the proposed approach are evaluated in a simulation study using a skewed finite population created from real survey data.
Release date: 2005-02-03 - Articles and reports: 12-001-X20040027758Description:
In this article, we study the use of Bayesian neural networks in finite population estimation.We propose estimators for finite population mean and the associated mean squared error. We also propose to use the student t-distribution to model the disturbances in order to accommodate extreme observations that are often present in the data from social sample surveys. Numerical results show that Bayesian neural networks have made a significant improvement in finite population estimation over linear regression based methods
Release date: 2005-02-03 - Articles and reports: 75F0002M2005001Description:
Comparative analysis of poverty dynamics incidence - transitions, and persistence - can yield important insights about the nature of poverty and the effectiveness of alternative policy responses. This manuscript compares poverty dynamics in four advanced industrial countries (Canada, unified Germany, Great Britain, and the United States) for overlapping six-year periods in the 1990s. The data indicate that poverty persistence is higher in North America than in Europe; for example, despite high incidence, poverty in Great Britain is relatively transitory. Most poverty transitions, and the prevalence of chronic poverty, are associated with employment instability and family dissolution in all four countries. The results also suggest that differences in social policy are crucial for the observed differences in poverty incidence and persistence between Europe and North America.
Release date: 2005-01-31 - 10. Tracing and non-response adjustment for the Longitudinal Survey of Immigrants to Canada ArchivedArticles and reports: 11-522-X20030017595Geography: CanadaDescription:
This paper discusses challenges faced in locating recent immigrants and implementing strategies to increase response rates for the Longitudinal Survey of Immigrants to Canada (LSIC). It also presents a model-assisted technique for adjusting for non-response, based on the approach proposed by Eltinge-Yanseneh to define adjustment classes.
Release date: 2005-01-26
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