This paper is one of a series produced by the Data and Evidence to End Extreme Poverty (DEEP) research programme to explore how innovation in data collection, data processing, and data analysis might, with further development, provide solutions to ‘pinch points’ in policymaking and policy management for poverty reduction.

This work presents the estimation process in repeated surveys using State Space Models and the generalized linear squares filter, GLS filter, under the time series approach. This filter deals with autocorrelated errors in the observation equation, in a simpler way than the well-known Kalman filter. Additionally, it allows for modeling jointly several domains under benchmark constraints obtained from the same survey. The benchmarking not only achieves coherence between the model-based estimates and the corresponding design-based aggregated estimates, but also provides protection against possible model failures.For the scenario of samples with a rotation scheme, the estimation of the autocorrelation structure of observational errors, using the pseudo-errors method, is also addressed.Simulation are used to compare the GLS and Kalman filter estimators. Moreover, the application of GLS filter under benchmark restrictions is illustrated, using the unemployment rate time serie from the Brazilian monthly labor force survey, from March 2002 to February 2012.

Annexe to the paper ‘How can new technology support better measurement of extreme poverty?’, covering methods for producing high-resolution and high-frequency poverty estimates.