Measuring Precision of Statistical Inference on Partially Identified Parameters
AbstractPlanners of surveys and experiments that partially identify parameters of interest face trade offs between using limited resources to reduce sampling error and using them to reduce the extent of partial identification. I evaluate these trade offs in a simple statistical problem with normally distributed sample data and interval partial identification using different frequentist measures of inference precision (length of confidence intervals, minimax mean sqaured error and mean absolute deviation, minimax regret for treatment choice) and analogous Bayes measures with a flat prior. The relative value of collecting data with better identification properties (e.g., increasing response rates in surveys) depends crucially on the choice of the measure of precision. When the extent of partial identification is significant in comparison to sampling error, the length of confidence intervals, which has been used most often, assigns the lowest value to improving identification among the measures considered.
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Bibliographic InfoPaper provided by Collegio Carlo Alberto in its series Carlo Alberto Notebooks with number 98.
Length: 25 pages
Date of creation: 2008
Date of revision: 2012
statistical treatment choice; survey planning; nonresponse; mean squared error; mean absolute deviation; minimax regret;
Find related papers by JEL classification:
- C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
- C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
- C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
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