A meta-analysis of community-based studies on quality protein maize

Biofortification, or the improvement of nutritional quality in food crops, is a promising strategy to combat undernutrition, particularly among the rural poor in developing countries. However, traditional methods of impact assessment are inadequate for biofortified crops, as they do not consider their nutritional benefits. Evidence for the nutritional impact of maize varieties with improved protein quality, collectively known as quality protein maize (QPM), was evaluated using meta-analysis of randomized, controlled studies in target communities. A new and generalizable effect size was proposed to quantify the impact of QPM on a key outcome, child growth. The results indicated that consumption of QPM instead of conventional maize leads to a 12% (95% CI: 7–18%) increase in the rate of growth in weight and a 9% (95% CI: 6–15%) increase in the rate of growth in height in infants and young children with mild to moderate undernutrition from populations in which maize is the major staple food. The proposed effect size and use of bootstrapping to determine statistical significance addressed some methodological limitations in the existing studies.     

Airport performance in a tourism receiving country: Evidence from Greece

The aim of this study is to estimate the performance of 38 Greek airports. The analysis is performed in two stages. Firstly, efficiency scores for each airport are estimated using Data Envelopment Analysis (DEA) original and bootstrap techniques. Secondly, statistical assessments (Mann–Whitney U and Kruskal–Wallis tests) and a censored Tobit regression model are employed to identify which factors significantly explain variations in the airport efficiency. The results indicated the scope for substantial efficiency improvements. In addition, island location, connectivity, and hotel infrastructure in the area were found to be significant factors affecting airport efficiency.     

A Comparison of two Robust Estimation Methods for Business Surveys

Two alternative robust estimation methods often employed by National Statistical Institutes in business surveys are two‐sided M‐estimation and one‐sided Winsorisation, which can be regarded as an approximate implementation of one‐sided M‐estimation. We review these methods and evaluate their performance in a simulation of a repeated rotating business survey based on data from the Retail Sales Inquiry conducted by the UK Office for National Statistics. One‐sided and two‐sided M‐estimation are found to have very similar performance, with a slight edge for the former for positive variables. Both methods considerably improve both level and movement estimators. Approaches for setting tuning parameters are evaluated for both methods, and this is a more important issue than the difference between the two approaches. M‐estimation works best when tuning parameters are estimated using historical data but is serviceable even when only live data is available. Confidence interval coverage is much improved by the use of a bootstrap percentile confidence interval.     

The linkage between insurance activity and banking credit: Some evidence from dynamic analysis

This paper investigates the long-run and short-run linkages between insurance activity and banking credit for G-7 countries. To minimize the pretest bias and overcome the structural changes, we adopt the bootstrap Granger causality test applied to full sample and subsamples with a fixed window size. The Johansen cointegration test with GMM-IV estimator finds a long-run positive relation between the series. The full sample results of bootstrap Granger causality test show that there is predictive power from life insurance activity to banking credit only for France and Japan, while the short-run causal relationships between nonlife insurance activity and banking credit are country-specific. However, parameter stability test results suggest that the short-run results in full sample are unreliable. The results of rolling VAR models report that the causal linkages between them are time-varying across various subsamples. These findings offer some useful insights for achieving the co-evolution between insurance and banking credit markets.     

The effect of on-site forest experience on stated preferences for low-impact timber harvesting programs

An important issue in the design of stated-preference surveys is whether the information provided to respondents within a survey instrument is adequate to yield valid value estimates. Providing respondents with on-site experience about forest ecosystem management alternatives may influence their expectation of the effects from new policies and programs. In the research reported here, we investigate whether preference parameters for attributes of low-impact timber harvesting programs differ between respondents to a mail survey versus respondents provided with an on-site forest experience (walk through a research forest). The empirical analysis in our application shows that stated preferences for timber harvesting attributes are not statistically different between the mail and on-site applications of the survey, and this result is robust to pretest (before experience) and post-test (post experience) applications.     

TRB技术分析规则在期货市场的有效性检验

本文以大连大豆期货为标的物,采用TRB(Trading Range Break,交易区间突破)技术分析规则为主要研究方法,实证检验了其在期货市场投资中的获利能力。在我们选定的策略中,中线获利能力最强,即使考虑交易成本后仍不能消除这种超额收益。同时Bootstrap模拟检验的结果也显示,这种获利能力不能通过其他一些收益率序列得到解释。     

Robust testing procedures of process locations

In many manufacturing and service industries, the quality department of the organization works continuously to ensure that the mean or location of the process is close to the target value. In order to understand the process, it is necessary to provide numerical statements of the processes that are being investigated. That is why the researcher needs to check the validity of the hypotheses that are concerned with some physical phenomena. It is usually assumed that the collected data behave well. However, sometimes the data may contain outliers. The presence of one or more outliers might seriously distort the statistical inference. Since the sample mean is very sensitive to outliers, this research will use the smooth adaptive (SA) estimator to estimate the population mean. The SA estimator will be used to construct testing procedures, called smooth adaptive test (SA test), for testing various null hypotheses. A Monte Carlo study is used to simulate the values of the probability of a Type I error and the power of the SA test. This is accomplished by constructing confidence intervals of the process mean by using the SA estimator and bootstrap methods. The SA test will be compared with other tests such as the normal test, t test and a nonparametric statistical method, namely, the Wilcoxon signed-rank test. Also, the cases with and without outliers will be considered. For the right-skewed distributions, the SA test is the best choice. When the population is a right-skewed distribution with one outlier, the SA test controls the probability of a Type I error better than other tests and is recommended.     

Financial integration of GCC banking markets: A non-parametric bootstrap DEA estimation approach

In this paper, we investigate banking sector integration in the Gulf Cooperation Council during the period 1998-2009. The integration inference was derived by testing the convergence of cost efficiency scores. These efficiencies were measured using a smoothed bootstrap procedure that ensures consistency and unbiasedness. The convergence was examined using two tests: a beta convergence test and a sigma convergence test. The two tests show significant convergence, particularly during the transitional period 2003-2009, that witnessed substantial reforms. Therefore, we conclude that integration and harmonization measures taken by the Gulf Cooperation Council Governments have had a significant impact on efficiency and homogeneity of these countries’ banking markets.     

Exchange rates and fundamentals: A bootstrap panel data analysis

This study attempts to re-examine the Granger non-causality from exchange rates to observed fundamentals based on the present value model of Engel and West (2005). To this end, we employ the bootstrap panel Granger non-causality analysis, which allows us to untangle the causal nexus between exchange rates and fundamentals in panel data. Among the main results, it is found that the null hypothesis of no cross-sectional dependence across the members of the panel is strongly rejected, indicating that the bootstrap critical value is required in conducting the panel Granger non-causality test. The null hypothesis of Granger non-causality running from the fundamentals to exchange rates is significantly rejected, implying that the monetary approach of exchange rate determination is a useful benchmark to understand the evolution of the exchange rate. Empirical evidences also show that exchange rates Granger-case the fundamentals, supporting the view that exchange rates are determined as the present value that depends in part on observed fundamentals.     

Testing linear relationships between non-constant variances of economic variables

We aim to assess linear relationships between the non-constant variances of economic variables. A two-step methodology is proposed to solve this problem. First, the conditional mean is filtered by mean of a vector autoregressive (VAR) model. Then, a bootstrap cumulative sum (CUSUM) test is applied to the residuals. Simulations suggest a good behavior of the test, for sample sizes commonly encountered in practice. The tool we provide is intended to highlight relations, or draw common patterns between economic variables, through their non-constant variances. The outputs of this paper are illustrated considering U.S. regional data.