Rights statement: https://www.cambridge.org/core/journals/econometric-theory/article/inference-in-nonparametric-series-estimation-with-specification-searches-for-the-number-of-series-terms/8112698259D6E213EA068D28E9FE32AB The final, definitive version of this article has been published in the Journal, Econometric Theory, ? (?), pp ?-? 2020, © 2020 Cambridge University Press.
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Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Inference in Nonparametric Series Estimation with Specification Searches for the Number of Series Terms
AU - Kang, David
N1 - https://www.cambridge.org/core/journals/econometric-theory/article/inference-in-nonparametric-series-estimation-with-specification-searches-for-the-number-of-series-terms/8112698259D6E213EA068D28E9FE32AB The final, definitive version of this article has been published in the Journal, Econometric Theory, ? (?), pp ?-? 2020, © 2020 Cambridge University Press.
PY - 2021/4/13
Y1 - 2021/4/13
N2 - Nonparametric series regression often involves specification search over the tuning parameter, that is, evaluating estimates and confidence intervals with a different number of series terms. This paper develops pointwise and uniform inferences for conditional mean functions in nonparametric series estimations that are uniform in the number of series terms. As a result, this paper constructs confidence intervals and confidence bands with possibly data-dependent series terms that have valid asymptotic coverage probabilities. This paper also considers a partially linear model setup and develops inference methods for the parametric part uniform in the number of series terms. The finite sample performance of the proposed methods is investigated in various simulation setups as well as in an illustrative example, that is, the nonparametric estimation of the wage elasticity of the expected labor supply from Blomquist and Newey (2002, Econometrica 70, 2455-2480).
AB - Nonparametric series regression often involves specification search over the tuning parameter, that is, evaluating estimates and confidence intervals with a different number of series terms. This paper develops pointwise and uniform inferences for conditional mean functions in nonparametric series estimations that are uniform in the number of series terms. As a result, this paper constructs confidence intervals and confidence bands with possibly data-dependent series terms that have valid asymptotic coverage probabilities. This paper also considers a partially linear model setup and develops inference methods for the parametric part uniform in the number of series terms. The finite sample performance of the proposed methods is investigated in various simulation setups as well as in an illustrative example, that is, the nonparametric estimation of the wage elasticity of the expected labor supply from Blomquist and Newey (2002, Econometrica 70, 2455-2480).
U2 - 10.1017/S0266466620000158
DO - 10.1017/S0266466620000158
M3 - Journal article
VL - 37
SP - 311
EP - 345
JO - Econometric Theory
JF - Econometric Theory
SN - 0266-4666
IS - 2
ER -