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- Articles and reports: 82-003-X201601214686Description:
This study examines birth and death registration data and census data to compare rates of preterm birth, small-for-gestational-age birth, stillbirth and infant mortality, based on the presence or absence of paternal data on the birth registration and in census results, while controlling for maternal characteristics.
Release date: 2016-12-21 - Articles and reports: 11F0019M2016386Description:
This paper asks whether research and development (R&D) drives the level of competitiveness required to successfully enter export markets and whether, in turn, participation in export markets increases R&D expenditures. Canadian non-exporters that subsequently entered export markets in the first decade of the 2000s are found to be not only larger and more productive, as has been reported for previous decades, but also more likely to have invested in R&D. Both extramural R&D expenditures (purchased from domestic and foreign suppliers) and intramural R&D expenditures (performed in-house) increase the ability of firms to penetrate export markets. Exporting also has a significant impact on subsequent R&D expenditures; exporters are more likely to start investing in R&D. Firms that began exporting increased the intensity of extramural R&D expenditures in the year in which exporting occurred.
Release date: 2016-11-28 - Articles and reports: 12-001-X201600114540Description:
In this paper, we compare the EBLUP and pseudo-EBLUP estimators for small area estimation under the nested error regression model and three area level model-based estimators using the Fay-Herriot model. We conduct a design-based simulation study to compare the model-based estimators for unit level and area level models under informative and non-informative sampling. In particular, we are interested in the confidence interval coverage rate of the unit level and area level estimators. We also compare the estimators if the model has been misspecified. Our simulation results show that estimators based on the unit level model perform better than those based on the area level. The pseudo-EBLUP estimator is the best among unit level and area level estimators.
Release date: 2016-06-22 - Articles and reports: 12-001-X201600114542Description:
The restricted maximum likelihood (REML) method is generally used to estimate the variance of the random area effect under the Fay-Herriot model (Fay and Herriot 1979) to obtain the empirical best linear unbiased (EBLUP) estimator of a small area mean. When the REML estimate is zero, the weight of the direct sample estimator is zero and the EBLUP becomes a synthetic estimator. This is not often desirable. As a solution to this problem, Li and Lahiri (2011) and Yoshimori and Lahiri (2014) developed adjusted maximum likelihood (ADM) consistent variance estimators which always yield positive variance estimates. Some of the ADM estimators always yield positive estimates but they have a large bias and this affects the estimation of the mean squared error (MSE) of the EBLUP. We propose to use a MIX variance estimator, defined as a combination of the REML and ADM methods. We show that it is unbiased up to the second order and it always yields a positive variance estimate. Furthermore, we propose an MSE estimator under the MIX method and show via a model-based simulation that in many situations, it performs better than other ‘Taylor linearization’ MSE estimators proposed recently.
Release date: 2016-06-22 - 5. The 2006 Canadian Birth-Census Cohort ArchivedArticles and reports: 82-003-X201600114306Description:
This article is an overview of the creation, content, and quality of the 2006 Canadian Birth-Census Cohort Database.
Release date: 2016-01-20
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Articles and reports (5)
Articles and reports (5) ((5 results))
- Articles and reports: 82-003-X201601214686Description:
This study examines birth and death registration data and census data to compare rates of preterm birth, small-for-gestational-age birth, stillbirth and infant mortality, based on the presence or absence of paternal data on the birth registration and in census results, while controlling for maternal characteristics.
Release date: 2016-12-21 - Articles and reports: 11F0019M2016386Description:
This paper asks whether research and development (R&D) drives the level of competitiveness required to successfully enter export markets and whether, in turn, participation in export markets increases R&D expenditures. Canadian non-exporters that subsequently entered export markets in the first decade of the 2000s are found to be not only larger and more productive, as has been reported for previous decades, but also more likely to have invested in R&D. Both extramural R&D expenditures (purchased from domestic and foreign suppliers) and intramural R&D expenditures (performed in-house) increase the ability of firms to penetrate export markets. Exporting also has a significant impact on subsequent R&D expenditures; exporters are more likely to start investing in R&D. Firms that began exporting increased the intensity of extramural R&D expenditures in the year in which exporting occurred.
Release date: 2016-11-28 - Articles and reports: 12-001-X201600114540Description:
In this paper, we compare the EBLUP and pseudo-EBLUP estimators for small area estimation under the nested error regression model and three area level model-based estimators using the Fay-Herriot model. We conduct a design-based simulation study to compare the model-based estimators for unit level and area level models under informative and non-informative sampling. In particular, we are interested in the confidence interval coverage rate of the unit level and area level estimators. We also compare the estimators if the model has been misspecified. Our simulation results show that estimators based on the unit level model perform better than those based on the area level. The pseudo-EBLUP estimator is the best among unit level and area level estimators.
Release date: 2016-06-22 - Articles and reports: 12-001-X201600114542Description:
The restricted maximum likelihood (REML) method is generally used to estimate the variance of the random area effect under the Fay-Herriot model (Fay and Herriot 1979) to obtain the empirical best linear unbiased (EBLUP) estimator of a small area mean. When the REML estimate is zero, the weight of the direct sample estimator is zero and the EBLUP becomes a synthetic estimator. This is not often desirable. As a solution to this problem, Li and Lahiri (2011) and Yoshimori and Lahiri (2014) developed adjusted maximum likelihood (ADM) consistent variance estimators which always yield positive variance estimates. Some of the ADM estimators always yield positive estimates but they have a large bias and this affects the estimation of the mean squared error (MSE) of the EBLUP. We propose to use a MIX variance estimator, defined as a combination of the REML and ADM methods. We show that it is unbiased up to the second order and it always yields a positive variance estimate. Furthermore, we propose an MSE estimator under the MIX method and show via a model-based simulation that in many situations, it performs better than other ‘Taylor linearization’ MSE estimators proposed recently.
Release date: 2016-06-22 - 5. The 2006 Canadian Birth-Census Cohort ArchivedArticles and reports: 82-003-X201600114306Description:
This article is an overview of the creation, content, and quality of the 2006 Canadian Birth-Census Cohort Database.
Release date: 2016-01-20
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