Arbeitspapier
Inference on a distribution from noisy draws
We consider a situation where the distribution of a random variable is being estimated by the empirical distribution of noisy measurements of that variable. This is common practice in, for example, teacher value-added models and other fixed-effect models for panel data. We use an asymptotic embedding where the noise shrinks with the sample size to calculate the leading bias in the empirical distribution arising from the presence of noise. The leading bias in the empirical quantile function is equally obtained. These calculations are new in the literature, where only results on smooth functionals such as the mean and variance have been derived. We provide both analytical and jackknife corrections that recenter the limit distribution and yield confidence intervals with correct coverage in large samples. Our approach can be connected to corrections for selection bias and shrinkage estimation and is to be contrasted with deconvolution. Simulation results confirm the much-improved sampling behavior of the corrected estimators. An empirical illustration on heterogeneity in deviations from the law of one price is equally provided.
- Sprache
-
Englisch
- Erschienen in
-
Series: cemmap working paper ; No. CWP42/21
- Klassifikation
-
Wirtschaft
Semiparametric and Nonparametric Methods: General
Single Equation Models; Single Variables: Panel Data Models; Spatio-temporal Models
- Thema
-
bias correction
estimation noise
nonparametric inference
measurement error
panel data
regression to the mean
shrinkage
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Jochmans, Koen
Weidner, Martin
- Ereignis
-
Veröffentlichung
- (wer)
-
Centre for Microdata Methods and Practice (cemmap)
- (wo)
-
London
- (wann)
-
2021
- DOI
-
doi:10.47004/wp.cem.2021.4221
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:44 MEZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Jochmans, Koen
- Weidner, Martin
- Centre for Microdata Methods and Practice (cemmap)
Entstanden
- 2021
Ähnliche Objekte (12)
Inference on a distribution from noisy draws
Inference on a distribution from noisy draws
Divide and Conquer: Noisy Communication in Networks, Power, and Wealth Distribution
Noisy monetary policy
A nifty fix for published distribution statistics: Simplified distribution-free statistical inference
Bargaining with Noisy Communication
Bayesian inference for hedge funds with stable distribution of returns
Spectral estimation of covolatility from noisy observations using local weights
Is deflation costly after all? Evidence from noisy historical data
Consistent noisy independent component analysis
Noisy information and fundamental disagreement
Distribution-Free Statistical Inference for Inequality Dominance with Crossing Lorenz Curves
Inference on a distribution from noisy draws
Inference on a distribution from noisy draws
Divide and Conquer: Noisy Communication in Networks, Power, and Wealth Distribution
Noisy monetary policy
A nifty fix for published distribution statistics: Simplified distribution-free statistical inference
Bargaining with Noisy Communication
Bayesian inference for hedge funds with stable distribution of returns
Spectral estimation of covolatility from noisy observations using local weights
Is deflation costly after all? Evidence from noisy historical data
Consistent noisy independent component analysis
Noisy information and fundamental disagreement
Distribution-Free Statistical Inference for Inequality Dominance with Crossing Lorenz Curves
Inference on a distribution from noisy draws
Inference on a distribution from noisy draws
Divide and Conquer: Noisy Communication in Networks, Power, and Wealth Distribution
Noisy monetary policy
A nifty fix for published distribution statistics: Simplified distribution-free statistical inference
Bargaining with Noisy Communication
Bayesian inference for hedge funds with stable distribution of returns
Spectral estimation of covolatility from noisy observations using local weights
Is deflation costly after all? Evidence from noisy historical data
Consistent noisy independent component analysis
Noisy information and fundamental disagreement