observations several times or, on the contrary, failing to represent in any way some subpopulations that may have a particular interest from the economic and social point of view, such as the movers or newborns or those who enter the population at a given moment.

Moving now to the estimation methodology, we have seen how the GWSME estimator offers a method that allows us to provide unbiased estimates and is simple to implement. The calibration of the estimates with the correction of the weights at the household level presents several theoretical and applicative advantages. The variance estimation has a well-established asymptotic behavior and is far more transparent and well-founded. Because the estimation is based on a small number of adjustment stages, the process flow is simpler. Lastly, the approach greatly improves the consistency between estimates created with household-level variables and those utilizing individual-level definitions of the variables.

Finally, the last aspect examined concerns data collection and the different strategies that can be implemented to mitigate the bias resulting from non-response and panel attrition.

In conclusion, we used data from the Uganda National Panel Survey (UNPS) to experimentally evaluate the suggested technique. In summary, we found that the calibrated GWSM base estimator yields individual-level statistics that appear to be more accurate than those produced by the current UNPS estimator. Additionally, the calibrated GWSM base cross-sectional estimates on the transition matrix show a generally higher degree of stability when the sample is changed compared to the current UNPS estimates.

We stress that the above results are obtained using less auxiliary information of the current UNPS estimator, both because some of this information is not clearly documented and because one of the objectives is to propose a simplified estimator with respect to the current one, using fewer computational steps and based on a robust sampling theory well known in the scientific literature.

# REFERENCES

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FAO (2015). Integrated Survey Framework, Guidelines, Rome. <u>http://www.gsars.org/wpcontent/uploads/2015/05/ISF-Guidelines_12_05_2015-WEB.pdf. Accessed on 16 December</u> 2015.

FAO (2014). The Global Strategy to Improve Agricultural and Rural Statistics. Technical Report on the Integrated Survey Framework, Technical Report Series GO-02-2014, <u>http://gsars.org/wp-content/uploads/2014/07/Technical_report_on-ISF-Final.pdf. Accessed</u> on 1 December 2014.

Groves, R.M., Don A. Dillman, J. L. Eltinge, R- J. A. Little . (2001). Survey No-Response, Wiley. New York.

Falorsi P. D., P. Righi, P. Lavallée . (2019). Optimal Sampling for the Integrated Observation of Different Populations. Survey methodology, Vol. 45, No. 3, pp. 485-511. Statistics Canada, Catalogue No. 12-001-X.

Falorsi, F, Russo A. (2009) . Popolazioni e parametri di interesse nelle indagini ripetute nel tempo. In Alleva G., Falorsi P. Indicatori e modelli statistici per la valutazione degli squilibri territoriali. Franco Angeli, Milano. ISBN: 9788856812077.

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