The effects of exchange rate regimes on economic growth: evidence from propensity score matching estimates

This is the first study that employs the propensity score matching framework to examine the average treatment effect of exchange rate regimes on economic growth. Previous studies examining the effects of different exchange regimes on growth often apply time series or panel data techniques and provide mixed results. This study employs a variety of non-parametric matching methods to address the self-selection problem, which potentially causes a bias in the traditional linear regressions. We evaluate the average treatment effect of the floating exchange rate regime on economic growth in 164 countries. Time period of the quasi experiment starts in 1970, capturing the collapse of the Bretton Woods fixed exchange rate commitment system. Results show that the average treatment effect of floating exchange rate regimes on economic growth is statistically insignificant. Verifying the results with the Rosenbaum's bounds, our findings are strong and robust. The research states that there is no evidence that employing a floating exchange rate regime compared to a fixed one leads to a higher economic growth for the countries that use this particular policy.     

Modified Normal-based Approximation to the Percentiles of Linear Combination of Independent Random Variables with Applications

A modified normal-based approximation for calculating the percentiles of a linear combination of independent random variables is proposed. This approximation is applicable in situations where expectations and percentiles of the individual random variables can be readily obtained. The merits of the approximation are evaluated for the chi-square and beta distributions using Monte Carlo simulation. An approximation to the percentiles of the ratio of two independent random variables is also given. Solutions based on the approximations are given for some classical problems such as interval estimation of the normal coefficient of variation, survival probability, the difference between or the ratio of two binomial proportions, and for some other problems. Furthermore, approximation to the percentiles of a doubly noncentral F distribution is also given. For all the problems considered, the approximation provides simple satisfactory solutions. Two examples are given to show applications of the approximation.     

Bayesian predictive power: choice of prior and some recommendations for its use as probability of success in drug development

Bayesian predictive power, the expectation of the power function with respect to a prior distribution for the true underlying effect size, is routinely used in drug development to quantify the probability of success of a clinical trial. Choosing the prior is crucial for the properties and interpretability of Bayesian predictive power. We review recommendations on the choice of prior for Bayesian predictive power and explore its features as a function of the prior. The density of power values induced by a given prior is derived analytically and its shape characterized. We find that for a typical clinical trial scenario, this density has a u‐shape very similar, but not equal, to a β‐distribution. Alternative priors are discussed, and practical recommendations to assess the sensitivity of Bayesian predictive power to its input parameters are provided. Copyright © 2016 John Wiley & Sons, Ltd.     

组群层次下风险投资多边联盟的形成

基于中国风险投资行业中的多边联合投资实践,以"组群"为单位探讨组群成员间的关系密度在不同情境下对组群成员组建多边联盟这一行为的影响。主要研究结论如下:第一,潜在合作伙伴之间的前期关系密度对于它们未来形成多边联盟具有重要的积极作用。第二,潜在合作伙伴之间的行业知识异质性显著增强了前期关系密度的正向效应,声誉异质性的影响并不显著。第三,关系断层显著增强了关系密度的正向作用。第四,地位断层并未显著增强关系密度的正向作用,但对多边联盟的形成却有正向的直接作用。     

Empirical likelihood for nonlinear regression models with nonignorable missing responses

This article develops three empirical likelihood (EL) approaches to estimate parameters in nonlinear regression models in the presence of nonignorable missing responses. These are based on the inverse probability weighted (IPW) method, the augmented IPW (AIPW) method and the imputation technique. A logistic regression model is adopted to specify the propensity score. Maximum likelihood estimation is used to estimate parameters in the propensity score by combining the idea of importance sampling and imputing estimating equations. Under some regularity conditions, we obtain the asymptotic properties of the maximum EL estimators of these unknown parameters. Simulation studies are conducted to investigate the finite sample performance of our proposed estimation procedures. Empirical results provide evidence that the AIPW procedure exhibits better performance than the other two procedures. Data from a survey conducted in 2002 are used to illustrate the proposed estimation procedure. The Canadian Journal of Statistics 48: 386–416; 2020 © 2020 Statistical Society of Canada     

Regression model for surrogate data in high dimensional statistics

Abstract

This paper deals with the problem of estimating the regression of a surrogated scalar response variable given a functional random one. We construct an estimator of the regression operator by using, in addition to the available (true) response data, a surrogate data. We then establish some asymptotic properties of the constructed estimator in terms of the almost-complete and the quadratic mean convergences. Notice that the obtained results generalize a part of the results obtained in the finite dimensional framework. Finally, an illustration on the applicability of our results on both simulated data and a real dataset was realized. We have thus shown the superiority of our estimator on classical estimators when we are lacking complete data.     

Statistical analysis of accelerated temperature cycling test based on Coffin-Manson model

Abstract

This paper investigates the statistical analysis of grouped accelerated temperature cycling test data when the product lifetime follows a Weibull distribution. A log-linear acceleration equation is derived from the Coffin-Manson model. The problem is transformed to a constant-stress accelerated life test with grouped data and multiple acceleration variables. The Jeffreys prior and reference priors are derived. Maximum likelihood estimation and Bayesian estimation with objective priors are obtained by applying the technique of data augmentation. A simulation study shows that both of these two methods perform well when sample size is large, and the Bayesian method gives better performance under small sample sizes.     

Tail Probability Ratios of Normal and Student’s t Distributions

The ratio of normal tail probabilities and the ratio of Student’s t tail probabilities have gained an increased attention in statistics and related areas. However, they are not well studied in the literature. In this paper, we systematically study the functional behaviors of these two ratios. Meanwhile, we explore their difference as well as their relationship. It is surprising that the two ratios behave very different to each other. Finally, we conclude the paper by conducting some lower and upper bounds for the two ratios.     

Is automatic enrollment consistent with a life cycle model?

We examine retirement saving for young adults in a life cycle model. We find that optimal retirement saving is zero for liquidity-constrained young adults who anticipate significant earnings growth. With a plausible age-earnings profile for college-educated workers, retirement saving does not begin until the late 30s or early 40s. Workers facing a flat earnings profile begin saving much sooner. Participating may be optimal for younger workers facing steep earnings profiles if they anticipate switching jobs and cashing out after 1–2 years. Our results suggest that automatically enrolling workers, regardless of age, is not consistent with a life cycle model.