New PDF release: Handbook of Econometrics, Volume 5

By James J. Heckman

ISBN-10: 0444823409

ISBN-13: 9780444823403

The guide is a definitive reference resource and instructing reduction for econometricians. It examines types, estimation thought, facts research and box functions in econometrics. complete surveys, written by means of specialists, speak about contemporary advancements at a degree appropriate for pro use via economists, econometricians, statisticians, and in complex graduate econometrics classes. for additional info at the Handbooks in Economics sequence, please see our domestic web page on http://www.elsevier.nl/locate/hes

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Additional resources for Handbook of Econometrics, Volume 5

Example text

1 is satisfied. The Lindeberg–Levy central limit theorem implies that T n is asymptotically normally distributed. The cumulative normal distribution function is continuous, so condition (ii) holds. By using arguments similar to those used to prove the Lindeberg–Levy theorem, it can be shown that condition (iii) holds. „ A theorem by Mammen (1992) gives necessary and sufficient conditions for the bootstrap to consistently estimate the distribution of a linear functional of F 0 when Fn is the EDF of the data.

The bootstrap critical value is more accurate than the asymptotic critical value, z ∞, a/ 2 , whose error is O(n−1 ). Now consider the rejection probability of the test based on T n when H0 is true. With the exact but infeasible a-level critical value, the RP is P(|T n | > z n, a/ 2 ) = a. 13). Thus, with the asymptotic critical value, the true and nominal RP’s differ by O(n−1 ). Now consider the RP with the bootstrap critical value, P(|T n | ¾ z ∗n, a/ 2 ). Because ∗ z n, a/ 2 is a random variable, P(|T n | ¾ z ∗n, a/ 2 ) Ñ 1 − [G n (z ∗n, a/ 2 , F 0 ) − G n (−z ∗n, a/ 2 , F 0 )].

The Monte Carlo procedure for computing B∗n is as follows: Monte Carlo Procedure for Bootstrap Bias Estimation B1: Use the estimation data to compute q n . B2: Generate a bootstrap sample of size n by sampling the estimation data randomly with replacement. Compute qn∗ = g(X¯ ∗ ). B3: Compute E ∗ q ∗n by averaging the results of many repetitions of step B2. Set B∗n = E ∗ q ∗n − q n . To implement this procedure it is necessary to choose the number of repetitions, m, of step B2. It usually suffices to choose m sufficiently large that the estimate of E ∗ q ∗n does not change significantly if m is increased further.

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Handbook of Econometrics, Volume 5 by James J. Heckman


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