On efficient estimation of unemployment rates from Labour Force Survey Data - ARCHIVED

The method of minimum Q^(T) estimation for complex survey designs proposed by Singh (1985) provides asymptotically efficient estimates of model parameters analogous to Neyman’s (1949) min X^2 estimation procedure for simple random samples. The Q^(T) can be viewed as a X^2 type statistic for categorical survey data, and min Q^(T) estimates provide a robust alternative to Weighted Least Squares estimates, which often display unstable behaviour for complex surveys. In this paper, the min Q^(T) method is first described and then illustrated for the problem of estimating parameters of a logit model for survey estimates of unemployment rates which are obtained from the October 1980 Canadian LFS data cross-classified according to age-education covariate categories. It is seen that the trace efficiency of smoothed estimates obtained by Kumar and Rao (1986), who applied the method of pseudo maximum likelihood estimates (pseudo mle) to the same problem can be slightly improved by the min Q^(T) method. Interestingly enough, pseudo mle for individual cells behave much the same way as the efficient min Q^(T) estimates for the particular LFS example.