Bootstrap Diagnostics for Irregular Estimators / Isaiah Andrews, Jesse M. Shapiro.

By:

Andrews, Isaiah

Contributor(s):

Material type: Text

TextSeries: Working Paper Series (National Bureau of Economic Research) ; no. w32038.Publication details: Cambridge, Mass. National Bureau of Economic Research 2024.Description: 1 online resource: illustrations (black and white)Subject(s):

Other classification:

Online resources:

Available additional physical forms:

Hardcopy version available to institutional subscribers

Abstract: Empirical researchers frequently rely on normal approximations in order to summarize and communicate uncertainty about their findings to their scientific audience. When such approximations are unreliable, they can lead the audience to make misguided decisions. We propose to measure the failure of the conventional normal approximation for a given estimator by the total variation distance between a bootstrap distribution and the normal distribution parameterized by the point estimate and standard error. For a wide class of decision problems and a class of uninformative priors, we show that a multiple of the total variation distance bounds the mistakes which result from relying on the conventional normal approximation. In a sample of recent empirical articles that use a bootstrap for inference, we find that the conventional normal approximation is often poor. We suggest and illustrate convenient alternative reports for such settings.

Tags from this library: No tags from this library for this title. Log in to add tags.

Average rating: 0.0 (0 votes)

Holdings
Item type	Home library	Collection	Call number	Status	Date due	Barcode	Item holds
Working Paper	Biblioteca Digital	Colección NBER	nber w32038 (Browse shelf(Opens below))	Not For Loan

Total holds: 0

Collection: Colección NBER Close shelf browser (Hides shelf browser)

Previous	No cover image available	No cover image available	No cover image available	No cover image available	No cover image available	No cover image available	No cover image available	Next
Previous	nber w32035 Homeward Bound: How Migrants Seek Out Familiar Climates /	nber w32036 The Modern Wholesaler: Global Sourcing, Domestic Distribution, and Scale Economies /	nber w32037 Trade Barriers and Market Power: Evidence from Argentina's Discretionary Import Restrictions /	nber w32038 Bootstrap Diagnostics for Irregular Estimators /	nber w32039 A Scalable Approach to High-Impact Tutoring for Young Readers: Results of a Randomized Controlled Trial /	nber w3204 The Long-Run Behavior of Velocity: The Institutional Approach Revisited /	nber w32040 What Works and For Whom? Effectiveness and Efficiency of School Capital Investments Across The U.S. /	Next

January 2024.

Empirical researchers frequently rely on normal approximations in order to summarize and communicate uncertainty about their findings to their scientific audience. When such approximations are unreliable, they can lead the audience to make misguided decisions. We propose to measure the failure of the conventional normal approximation for a given estimator by the total variation distance between a bootstrap distribution and the normal distribution parameterized by the point estimate and standard error. For a wide class of decision problems and a class of uninformative priors, we show that a multiple of the total variation distance bounds the mistakes which result from relying on the conventional normal approximation. In a sample of recent empirical articles that use a bootstrap for inference, we find that the conventional normal approximation is often poor. We suggest and illustrate convenient alternative reports for such settings.