Functions to calculate weights, estimates of changes and corresponding variance estimates for panel data with non-response. Partially overlapping samples are handled. Initially, weights are calculated by linear calibration. By default, the survey package is used for this purpose. It is also possible to use ReGenesees, which can be installed from < https://github.com/DiegoZardetto/ReGenesees>. Variances of linear combinations (changes and averages) and ratios are calculated from a covariance matrix based on residuals according to the calibration model. The methodology was presented at the conference, The Use of R in Official Statistics, and is described in Langsrud (2016) < http://www.revistadestatistica.ro/wp-content/uploads/2016/06/RRS2_2016_A021.pdf>.
Weighting and Estimation for Panel Data with Non-Response
| Function | |
|---|---|
| AkuData | Generate test data |
| CalibrateSSB | Calibration weighting and estimation |
| CalibrateSSBpanel | Calibration weighting and variance estimation for panel data |
| CalSSBobj | Create or modify a CalSSB object |
| CrossStrata | Crossing several factor variables |
| LagDiff | Creation of linear combination matrices |
| LinCombMatrix | Creation of linear combination matrices |
| PanelEstimation | Variance estimation for panel data |
| Period | Creation of linear combination matrices |
| PeriodDiff | Creation of linear combination matrices |
| WideFromCalibrate | Rearrange output from CalibrateSSB (calSSB object). Ready for input to PanelEstimation. |
CalibrateSSB is an R-package that handles repeated surveys with partially overlapping samples. Initially the samples are weighted by linear calibration using known or estimated population totals. A robust model based covariance matrix for all relevant estimated totals is calculated from the residuals according to the calibration model. Alternatively a design based covariance matrix is calculated in a very similar way. A cluster robust version is also possible. In the case of estimated populations totals the covariance matrix is adjusted by utilizing the theory of Särndal and Lundström (2005). Variances of linear combinations (changes and averages) and ratios are calculated from this covariance matrix. The linear combinations and ratios can involve variables within and/or between sample waves.
Langsrud, Ø (2016): “A variance estimation R-package for repeated surveys - useful for estimates of changes in quarterly and annual averages”, Romanian Statistical Review nr. 2 / 2016, pp. 17-28. CONFERENCE: New Challenges for Statistical Software - The Use of R in Official Statistics, Bucharest, Romania, 7-8 April.
Särndal, C.-E. and Lundström, S. (2005): Estimation in Surveys with Nonresponse, John Wiley and Sons, New York.