Raffaello Seri, Samuele Centorrino, Michele Bernasconi
arXiv 25 Apr 2019 · Econometrics
arXiv:1904.11156 · PDF · DOI · OpenAlex · Extracted main text
The goal of this paper is to provide some tools for nonparametric estimation and inference in psychological and economic experiments. We consider an experimental framework in which each of $n$subjects provides $T$ responses to a vector of $T$ stimuli. We propose to estimate the unknown function $f$ linking stimuli to responses through a nonparametric sieve estimator. We give conditions for consistency when either $n$ or $T$ or both diverge. The rate of convergence depends upon the error covariance structure, that is allowed to differ across subjects. With these results we derive the optimal divergence rate of the dimension of the sieve basis with both $n$ and $T$. We provide guidance about the optimal balance between the number of subjects and questions in a laboratory experiment and argue that a large $n$is often better than a large $T$. We derive conditions for asymptotic normality of functionals of the estimator of $T$ and apply them to obtain the asymptotic distribution of the Wald test when the number of constraints under the null is finite and when it diverges along with other asymptotic parameters. Lastly, we investigate the previous properties when the conditional covariance matrix is replaced by an estimator.
appendix boundary found by appendix_titled_section at “Appendix” · 60% of the source is main text. Read the extracted text to check this.
The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Belloni, Chernozhukov, Chetverikov \ Kato (2015) `Some New Asymptotic Theory for Least Squares Series: Pointwise and Uniform Results', Journal of Econometrics 186(2), 345–366 | 0.950 | 7 | 3 | 86% |
| 2 | Newey (1997) `Convergence Rates and Asymptotic Normality for Series Estimators', Journal of Econometrics 79(1), 147 – 168 | 0.916 | 13 | 4 | 77% |
| 3 | Luce (1999) Utility of Gains and Losses: Measurement-Theoretical and Experimental Approaches, Scientific Psychology Series, Taylor & Francis | 0.737 | 3 | 2 | 100% |
| 4 | Bernasconi \ Seri (2016) `What are we estimating when we fit Stevens' power law?', Journal of Mathematical Psychology 75, 137–149 | 0.644 | 2 | 2 | 100% |
| 5 | Bernasconi, Choirat \ Seri (2008) `Measurement by subjective estimation: Testing for separable representations', Journal of Mathematical Psychology 52(3), 184 – 201 | 0.644 | 2 | 2 | 100% |
| 6 | Butler \ Loomes (2007) `Imprecision as an account of the preference reversal phenomenon', American Economic Review 97(1), 277–297 | 0.644 | 2 | 2 | 100% |
| 7 | Chen (2007) Large Sample Sieve Estimation of Semi-Nonparametric Models, in J. J | 0.644 | 2 | 2 | 100% |
| 8 | Chen \ Christensen (2015) `Optimal Uniform Convergence Rates and Asymptotic Normality for Series Estimators under Weak Dependence and Weak Conditions', Jo… | 0.644 | 2 | 2 | 100% |
| 9 | Hershey \ Schoemaker (1985) `Probability versus certainty equivalence methods in utility measurement: Are they equivalent?', Management Science 31(10), 1213… | 0.644 | 2 | 2 | 100% |
| 10 | de Jong (2002) Convergence rates and asymptotic normality for series estimators | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 70 scored citations.