Decreased susceptibility of marginal odds ratios to finite-sample bias

We study the finite-sample bias and mean squared error, when properly defined, of the sample coefficient of variation under a general distribution. We employ a Nagar-type expansion and use moments of quadratic forms to derive the results. We find that the approximate bias depends on not only the skewness but also the kurtosis of the distribution, whereas the approximate mean squared error depends on the cumulants up to order 6.

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Approximating the Finite Sample Bias for Maximum Likelihood Estimators Using the Score–Solution

Econometric Theory ◽

10.1017/s0266466600005806 ◽

1997 ◽

Vol 13 (2) ◽

pp. 310-312 ◽

Cited By ~ 1

Author(s):

Bert Lambrecht ◽

William Perraudin ◽

Stephen Satchell

Keyword(s):

Maximum Likelihood ◽

Maximum Likelihood Estimators ◽

Finite Sample ◽

Sample Bias ◽

Finite Sample Bias

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Finite-Sample Bias-Correction Factors for the Median Absolute Deviation

Communications in Statistics - Simulation and Computation ◽

10.1080/03610918.2012.748913 ◽

2014 ◽

Vol 43 (10) ◽

pp. 2205-2212 ◽

Cited By ~ 5

Author(s):

Kevin Hayes

Keyword(s):

Bias Correction ◽

Median Absolute Deviation ◽

Correction Factors ◽

Finite Sample ◽

Absolute Deviation ◽

Sample Bias ◽

Finite Sample Bias

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GAUSSIAN INFERENCE IN AR(1) TIME SERIES WITH OR WITHOUT A UNIT ROOT

Econometric Theory ◽

10.1017/s0266466608080262 ◽

2008 ◽

Vol 24 (3) ◽

pp. 631-650 ◽

Cited By ~ 22

Author(s):

Peter C.B. Phillips ◽

Chirok Han

Keyword(s):

Time Series ◽

Central Limit ◽

Unit Root ◽

Initial Conditions ◽

Finite Sample ◽

Linear Processes ◽

Limit Theory ◽

Sample Bias ◽

Dynamic Panels ◽

Finite Sample Bias

This paper introduces a simple first-difference-based approach to estimation and inference for the AR(1) model. The estimates have virtually no finite-sample bias and are not sensitive to initial conditions, and the approach has the unusual advantage that a Gaussian central limit theory applies and is continuous as the autoregressive coefficient passes through unity with a uniform $\sqrt{n}$ rate of convergence. En route, a useful central limit theorem (CLT) for sample covariances of linear processes is given, following Phillips and Solo (1992, Annals of Statistics, 20, 971–1001). The approach also has useful extensions to dynamic panels.

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Measuring Group Differences in High‐Dimensional Choices: Method and Application to Congressional Speech

Econometrica ◽

10.3982/ecta16566 ◽

2019 ◽

Vol 87 (4) ◽

pp. 1307-1340 ◽

Cited By ~ 27

Author(s):

Matthew Gentzkow ◽

Jesse M. Shapiro ◽

Matt Taddy

Keyword(s):

Machine Learning ◽

High Dimensional ◽

Group Differences ◽

Finite Sample ◽

Sample Bias ◽

The Past ◽

Recent Advances ◽

Finite Sample Bias ◽

Choice Set

We study the problem of measuring group differences in choices when the dimensionality of the choice set is large. We show that standard approaches suffer from a severe finite‐sample bias, and we propose an estimator that applies recent advances in machine learning to address this bias. We apply this method to measure trends in the partisanship of congressional speech from 1873 to 2016, defining partisanship to be the ease with which an observer could infer a congressperson's party from a single utterance. Our estimates imply that partisanship is far greater in recent years than in the past, and that it increased sharply in the early 1990s after remaining low and relatively constant over the preceding century.

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