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MacKinnon, James G. - 2021

As I document using evidence from a journal data repository that I manage, the datasets used in empirical work are getting larger. When we use very large datasets, it can be dangerous to rely on standard methods for statistical inference. In addition, we need to worry about computational issues....

Persistent link: https://www.econbiz.de/10012815681

Sample sizes for reliably estimating lower and upper income shares in income distribution analysis

Beach, Charles M. - 2023

This paper uses distribution-free formulas for the asymptotic variances of sample quantile income shares - as typically published by statistical agencies as measures of the distribution of income inequality - to calculate how large a survey sample must be in order to estimate a more refined...

Persistent link: https://www.econbiz.de/10014253712

Statistical inference in stable distributions based on asymptotic normality of the sample characteristic function

Kleinman, David Charles - 1977

Persistent link: https://www.econbiz.de/10000573300

Large sample estimation and inference for poverty measures

Preston, Ian - 1991

Persistent link: https://www.econbiz.de/10000854224

Monte-Carlo simulation studies in survey statistics : an appraisal

Burgard, Jan Pablo; Dörr, Patricia; Münnich, Ralf T. - 2020

statistical modelling. Second, survey data is typically gathered using random sampling schemes from a finite population. In this … case, the sampling inference under a finite population model drives statistical conclusions. For empirical analyses, in … randomization with sampling inference. The question arises under which circumstances - if any - the sampling design can then be …

Persistent link: https://www.econbiz.de/10012256215

Chapter 8 Robust Estimation and Inference for Importance Sampling Estimators with Infinite Variance

Chan, Joshua C. C.; Hou, Chenghan; Yang, Thomas Tao - In: Essays in honor of Cheng Hsiao, (pp. 255-285). 2020

Importance sampling is a popular Monte Carlo method used in a variety of areas in econometrics. When the variance of … the importance sampling estimator is infinite, the central limit theorem does not apply and estimates tend to be erratic …

Persistent link: https://www.econbiz.de/10015088870

Tricks with metrics : combining statistics for improved inference in regression analysis

Nguimkeu, Pierre - In: Econometric reviews 43 (2024) 8, pp. 581-594

Persistent link: https://www.econbiz.de/10015050623

Causal inference under outcome-based sampling with monotonicity assumptions

Jun, Sung Jae; Lee, Sokbae - In: Journal of business & economic statistics : JBES ; a … 42 (2024) 3, pp. 998-1009

Persistent link: https://www.econbiz.de/10015053526

Testing for signal-to-noise ratio in linear regression : a test under large or massive sample

Kim, Jae H.; Ji, Philip Inyeob - In: Review of managerial science : RMS 18 (2024) 10, pp. 3007-3024

Persistent link: https://www.econbiz.de/10015135850

Bayesian inference using adaptive sampling

Giordani, Paolo; Kohn, Robert - In: Bayesian econometrics, (pp. 61-84). 2008

Our paper discusses simulation-based Bayesian inference using information from previous draws to build the proposals. The aim is to produce samplers that are easy to implement, that explore the target distribution effectively, and that are computationally efficient and mix well.

Persistent link: https://www.econbiz.de/10015383644