scholarly journals Bootstrap Methods in Econometrics

2019 ◽  
Vol 11 (1) ◽  
pp. 193-224 ◽  
Author(s):  
Joel L. Horowitz
Keyword(s):  
Asymptotic Distribution ◽  
Confidence Intervals ◽  
Test Statistic ◽  
Bootstrap Methods ◽  
First Order ◽  
Coverage Probabilities ◽  
Asymptotic Distribution Theory ◽  
Nominal Coverage ◽  
Distributional Approximations ◽  
Approximations To Distributions

The bootstrap is a method for estimating the distribution of an estimator or test statistic by resampling one's data or a model estimated from the data. Under conditions that hold in a wide variety of econometric applications, the bootstrap provides approximations to distributions of statistics, coverage probabilities of confidence intervals, and rejection probabilities of hypothesis tests that are more accurate than the approximations of first-order asymptotic distribution theory. The reductions in the differences between true and nominal coverage or rejection probabilities can be very large. In addition, the bootstrap provides a way to carry out inference in certain settings where obtaining analytic distributional approximations is difficult or impossible. This article explains the usefulness and limitations of the bootstrap in contexts of interest in econometrics. The presentation is informal and expository. It provides an intuitive understanding of how the bootstrap works. Mathematical details are available in the references that are cited.

A Note on Bootstrapping Generalized Method of Moments Estimators

1996 ◽  
Vol 12 (1) ◽  
pp. 187-197 ◽  
Author(s):  
Jinyong Hahn
Keyword(s):  
New York ◽  
Limit Distribution ◽  
Generalized Method Of Moments ◽  
Test Statistic ◽  
Percentile Method ◽  
Coverage Probabilities ◽  
Bootstrap Percentile ◽  
Nominal Coverage ◽  
Moments Estimators ◽  
Bootstrap Distribution

Recently, Arcones and Giné (1992, pp. 13–47, in R. LePage & L. Billard [eds.], Exploring the Limits of Bootstrap, New York: Wiley) established that the bootstrap distribution of the M-estimator converges weakly to the limit distribution of the estimator in probability. In contrast, Brown and Newey (1992, Bootstrapping for GMM, Seminar note) discovered that the bootstrap distribution of the GMM overidentification test statistic does not converge weakly to the x2 distribution. In this paper, it is shown that the bootstrap distribution of the GMM estimator converges weakly to the limit distribution of the estimator in probability. Asymptotic coverage probabilities of the confidence intervals based on the bootstrap percentile method are thus equal to their nominal coverage probability.

scholarly journals Evaluation of Inter-Observer Reliability of Animal Welfare Indicators: Which Is the Best Index to Use?

Animals ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 1445
Author(s):  
Mauro Giammarino ◽  
Silvana Mattiello ◽  
Monica Battini ◽  
Piero Quatto ◽  
Luca Maria Battaglini ◽  
...  
Keyword(s):  
Animal Welfare ◽  
Confidence Intervals ◽  
Bootstrap Method ◽  
Dairy Goat ◽  
Microsoft Excel ◽  
Bootstrap Methods ◽  
Welfare Indicators ◽  
Observer Reliability ◽  
High Concordance ◽  
Variance Estimates

This study focuses on the problem of assessing inter-observer reliability (IOR) in the case of dichotomous categorical animal-based welfare indicators and the presence of two observers. Based on observations obtained from Animal Welfare Indicators (AWIN) project surveys conducted on nine dairy goat farms, and using udder asymmetry as an indicator, we compared the performance of the most popular agreement indexes available in the literature: Scott’s π, Cohen’s k, kPABAK, Holsti’s H, Krippendorff’s α, Hubert’s Γ, Janson and Vegelius’ J, Bangdiwala’s B, Andrés and Marzo’s ∆, and Gwet’s γ(AC1). Confidence intervals were calculated using closed formulas of variance estimates for π, k, kPABAK, H, α, Γ, J, ∆, and γ(AC1), while the bootstrap and exact bootstrap methods were used for all the indexes. All the indexes and closed formulas of variance estimates were calculated using Microsoft Excel. The bootstrap method was performed with R software, while the exact bootstrap method was performed with SAS software. k, π, and α exhibited a paradoxical behavior, showing unacceptably low values even in the presence of very high concordance rates. B and γ(AC1) showed values very close to the concordance rate, independently of its value. Both bootstrap and exact bootstrap methods turned out to be simpler compared to the implementation of closed variance formulas and provided effective confidence intervals for all the considered indexes. The best approach for measuring IOR in these cases is the use of B or γ(AC1), with bootstrap or exact bootstrap methods for confidence interval calculation.

Investigating association between factors fostering attention to a stock and rationales to buy it: an empirical analysis

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ripsy Bondia ◽  
Pratap C. Biswal ◽  
Abinash Panda
Keyword(s):  
Decision Making ◽  
Categorical Data ◽  
Categorical Variables ◽  
Individual Investors ◽  
Multiple Response ◽  
Individual Investor ◽  
Test Statistic ◽  
Chi Square ◽  
Content Type ◽  
First Order

PurposeCan something that drives our initial attention toward a stock have any implications on final decision to buy it? This paper empirically and statistically tests association, if any, between factors fostering attention toward a stock and rationales to buy it.Design/methodology/approachThis paper uses survey responses of individual investors involving multiple response categorical data. Association between attention fostering factors and rationales is tested using a modified first-order corrected Rao-Scott chi-square test statistic (to adjust for within-participant dependence among responses in case of multiple response categorical variables). Further, odds ratios and mosaic plots are used to determine the effect size of association.FindingsStrong association is seen between attention fostering factors and rationales to buy a stock. Further, strongest associations are seen in cases where origin is the same underlying influencing factor. Some of the most cited attention fostering factors and rationales in this research stem from familiarity bias and expert bias.Practical implicationsWhat starts as a trivial attention fostering factor, which may not even be recognized by majority investors, can go on to become one of the rationales for buying a stock. This can result in substantial financial implications for an individual investor. Investor education agencies and regulatory authorities can make investors cognizant of such association, which can help investors to improve and adjust their decision making accordingly.Originality/valueThe extant literature discusses factors/biases influencing buying decisions of individual investors. This research takes a step ahead by distinguishing these factors in terms of whether they play role of (1) fostering attention toward a stock or (2) of reasons for ultimately buying it. Such dissection of factors/biases, to the best of authors' knowledge, has not been done previously in any empirical and statistical analysis. The paper uses multiple response categorical data and applies a modified first-order corrected Rao-Scott chi-square statistic to test association. Application of the above-mentioned test statistic has not been done previously in context of individual investor decision-making.

scholarly journals Coverage versus Confidence

2021 ◽  
Vol 23 ◽  
Author(s):  
Peyton Cook
Keyword(s):  
Confidence Interval ◽  
Confidence Intervals ◽  
Negative Binomial Distribution ◽  
Binomial Distribution ◽  
Coverage Probability ◽  
Negative Binomial ◽  
Asymptotic Confidence Interval ◽  
Population Proportion ◽  
Coverage Probabilities ◽  
The Mean

This article is intended to help students understand the concept of a coverage probability involving confidence intervals. Mathematica is used as a language for describing an algorithm to compute the coverage probability for a simple confidence interval based on the binomial distribution. Then, higher-level functions are used to compute probabilities of expressions in order to obtain coverage probabilities. Several examples are presented: two confidence intervals for a population proportion based on the binomial distribution, an asymptotic confidence interval for the mean of the Poisson distribution, and an asymptotic confidence interval for a population proportion based on the negative binomial distribution.

scholarly journals Parametric Bootstrap Methods for Estimating Model Parameters of Non-homogeneous Gamma Process

2018 ◽  
Vol 3 (2) ◽  
pp. 167-176 ◽  
Author(s):  
Yasuhiro Saito ◽  
Tadashi Dohi
Keyword(s):  
Confidence Intervals ◽  
Bootstrap Method ◽  
Parametric Bootstrap ◽  
Gamma Process ◽  
Estimation Accuracy ◽  
Model Parameters ◽  
Trend Function ◽  
Bootstrap Methods ◽  
Parametric Bootstrapping ◽  
Simulation Based

Non-Homogeneous Gamma Process (NHGP) is characterized by an arbitrary trend function and a gamma renewal distribution. In this paper, we estimate the confidence intervals of model parameters of NHGP via two parametric bootstrap methods: simulation-based approach and re-sampling-based approach. For each bootstrap method, we apply three methods to construct the confidence intervals. Through simulation experiments, we investigate each parametric bootstrapping and each construction method of confidence intervals in terms of the estimation accuracy. Finally, we find the best combination to estimate the model parameters in trend function and gamma renewal distribution in NHGP.

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