Why we should not be silent about noise

J D  Hey

doi:10.1007/s10683-005-5373-8

Why we should not be silent about noise

J D Hey

Economics

Research output: Contribution to journal › Article › peer-review

Abstract

There is an odd contradiction about much of the empirical (experimental) literature: The data is analysed using statistical tools which presuppose that there is some noise or randomness in the data, but the source and possible nature of the noise are rarely explicitly discussed. This paper argues that the noise should be brought out into the open, and its nature and implications openly discussed. Whether the statistical analysis involves testing or estimation, the analysis inevitably is built upon some assumed stochastic structure to the noise. Different assumptions justify different analyses, which means that the appropriate type of analysis depends crucially on the stochastic nature of the noise. This paper explores such issues and argues that ignoring the noise can be dangerous.

Original language	English
Pages (from-to)	325-345
Number of pages	21
Journal	Experimental Economics
Volume	8
Issue number	4
DOIs	https://doi.org/10.1007/s10683-005-5373-8
Publication status	Published - Dec 2005

Keywords

noise
stochastic assumptions
testing
estimation
inferences
PROBABILITY WEIGHTING FUNCTION
EXPECTED-UTILITY
DETERMINISTIC PREFERENCES
STOCHASTIC CHOICE
RISKY CHOICE
ERROR

Access to Document

10.1007/s10683-005-5373-8

Cite this

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abstract = "There is an odd contradiction about much of the empirical (experimental) literature: The data is analysed using statistical tools which presuppose that there is some noise or randomness in the data, but the source and possible nature of the noise are rarely explicitly discussed. This paper argues that the noise should be brought out into the open, and its nature and implications openly discussed. Whether the statistical analysis involves testing or estimation, the analysis inevitably is built upon some assumed stochastic structure to the noise. Different assumptions justify different analyses, which means that the appropriate type of analysis depends crucially on the stochastic nature of the noise. This paper explores such issues and argues that ignoring the noise can be dangerous.",

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KW - noise

KW - stochastic assumptions

KW - testing

KW - estimation

KW - inferences

KW - PROBABILITY WEIGHTING FUNCTION

KW - EXPECTED-UTILITY

KW - DETERMINISTIC PREFERENCES

KW - STOCHASTIC CHOICE

KW - RISKY CHOICE

KW - ERROR

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