Job Market Candidates

Víctor Sancibrián
Research fields
Econometrics, Empirical Macroeconomics, Labor Economics
Job market paper
Estimation uncertainty in repeated finite populations
Standard errors need to be adjusted down when the sample is a large fraction of the population of interest (a finite population setup). I consider the empirically relevant case where a finite population coexists with a measurement problem, in that the features of interest are not necessarily observable even if the entire population is sampled. I show that conventional standard errors remain generally conservative in this context and propose Finite Population Corrections (FPCs) that guarantee non-conservative inference when repeated measurements are available. FPCs rely on weak dependence across measurements and are very simple to implement. I apply these methods to two empirical settings where uncertainty has been previously understood in different ways: predicting lethal encounters with police using data on all U.S. police departments, and studying firm misallocation with a census of large Indonesian firms. Finite-population inference leads to confidence intervals that are up to 50% shorter in the former and illustrates the need to account for measurement uncertainty in the latter.
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References
- Manuel Arellano (Advisor) (CEMFI) (arellano@cemfi.es)
- Dmitry Arkhangelsky (CEMFI) (darkhangel@cemfi.es)
- Mikkel Plagborg-Møller (Princeton University) (mikkelpm@princeton.edu)
- Enrique Sentana (CEMFI) (sentana@cemfi.es)