The time series nature of repeated surveys is seldom taken into account. The few studies that take this into account usually smooth the period-wise estimates without using the cross sectional information. This leads to inefficient estimation. I present a statistical model of repeated surveys and construct a computationally simple estimator based on the Kalman filter which efficiently uses the whole underlying data set, but which is computationally very simple as we only need the first and second empirical moments of the data.
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Paper provided by Oslo University, Department of Economics in its series Memorandum with number
19/2004.
Find related papers by JEL classification: C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Other Model Applications C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Microeconomic Data
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