Testing a Class of Semi- or Nonparametric Conditional Moment Restriction Models using Series Methods
Research output: Working paper › Research
This paper proposes a new test for a class of conditional moment restrictions whose parameterization involves unknown, unrestricted conditional expectation functions. Examples of such conditional moment restrictions are conditional mean independence (leading to a nonparametric significance test) and conditional homoskedasticity (with an otherwise unrestricted conditional mean) and also arise from models of single-agent discrete choice under uncertainty and static games of incomplete information. The proposed test may be viewed as a semi-/nonparametric extension of the Bierens (1982) goodness-of-fit test of a parametric model for the conditional mean. Estimating conditional expectations using series methods and employing a Gaussian multiplier bootstrap to obtain critical values, the resulting test is shown to be asymptotically correctly sized and consistent. A simulation study applies the procedure to test the specification of a two-player, binary-action static game of incomplete information, treating equilibrium beliefs as nonparametric conditional expectations.
Original language | English |
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Number of pages | 92 |
DOIs | |
Publication status | Published - 3 Sep 2020 |
Series | University of Copenhagen. Institute of Economics. Discussion Papers (Online) |
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Number | 20-04 |
ISSN | 1601-2461 |
- Omnibus specification testing, Semiparametric, Conditional moment restriction, Conditional expectation, Series estimation, Bootstrap, Cramer-von Mises distance, C01, C14
Research areas
Links
- https://www.economics.ku.dk/research/publications/wp/dp-2020/2004.pdf
Submitted manuscript
ID: 248295489