Analysis

In 1991, Statistics Canada for the first time adjusted the Population Estimates Program for undercoverage in the 1991 Census. The Census coverage studies provided reliable estimates of undercoverage at the provincial level and for national estimates of large age - sex domains. However, the population series required estimates of undercoverage for age - sex domains within each province and territory. Since the direct survey estimates for some of these small domains had large standard errors due to the small sample size in the domain, small area modelling techniques were needed. In order to incorporate the varying degrees of reliability of the direct survey estimates, a regression model utilizing an Empirical Bayes methodology was used to estimate the undercoverage in small domains. A raking ratio procedure was then applied to the undercoverage estimates to preserve consistency with the marginal direct survey estimates. The results of this modelling process are shown along with the estimated reduction in standard errors.

A commonly used model for the analysis of time series models is the seasonal ARIMA model. However, the survey errors of the input data are usually ignored in the analysis. We show, through the use of state-space models with partially improper initial conditions, how to estimate the unknown parameters of this model using maximum likelihood methods. As well, the survey estimates can be smoothed using an empirical Bayes framework and model validation can be performed. We apply these techniques to an unemployment series from the Labour Force Survey.

Estimation of the means of a characteristic for a population at different points in time, based on a series of repeated surveys, is briefly reviewed. By imposing a stochastic parametric model on these means, it is possible to estimate the parameters of the model and to obtain alternative estimators of the means themselves. We describe the case where the population means follow an autoregressive-moving average (ARMA) process and the survey errors can also be formulated as an ARMA process. An example using data from the Canadian Travel Survey is presented.

In 1991, Statistics Canada for the first time adjusted the Population Estimates Program for undercoverage in the 1991 Census. The Census coverage studies provided reliable estimates of undercoverage at the provincial level and for national estimates of large age - sex domains. However, the population series required estimates of undercoverage for age - sex domains within each province and territory. Since the direct survey estimates for some of these small domains had large standard errors due to the small sample size in the domain, small area modelling techniques were needed. In order to incorporate the varying degrees of reliability of the direct survey estimates, a regression model utilizing an Empirical Bayes methodology was used to estimate the undercoverage in small domains. A raking ratio procedure was then applied to the undercoverage estimates to preserve consistency with the marginal direct survey estimates. The results of this modelling process are shown along with the estimated reduction in standard errors.

A commonly used model for the analysis of time series models is the seasonal ARIMA model. However, the survey errors of the input data are usually ignored in the analysis. We show, through the use of state-space models with partially improper initial conditions, how to estimate the unknown parameters of this model using maximum likelihood methods. As well, the survey estimates can be smoothed using an empirical Bayes framework and model validation can be performed. We apply these techniques to an unemployment series from the Labour Force Survey.

Estimation of the means of a characteristic for a population at different points in time, based on a series of repeated surveys, is briefly reviewed. By imposing a stochastic parametric model on these means, it is possible to estimate the parameters of the model and to obtain alternative estimators of the means themselves. We describe the case where the population means follow an autoregressive-moving average (ARMA) process and the survey errors can also be formulated as an ARMA process. An example using data from the Canadian Travel Survey is presented.