Maximum likelihood estimation of constant multiplicative bias benchmarking model with application - ARCHIVED

The maximum likelihood estimation of a non-linear benchmarking model, proposed by Laniel and Fyfe (1989; 1990), is considered. This model takes into account the biases and sampling errors associated with the original series. Since the maximum likelihood estimators of the model parameters are not obtainable in closed forms, two iterative procedures to find the maximum likelihood estimates are discussed. The closed form expressions for the asymptotic variances and covariances of the benchmarked series, and of the fitted values are also provided. The methodology is illustrated using published Canadian retail trade data.