Unconditional tests for association in 2 × 2 contingency tables in the total sum fixed design

The asymptotic approach and Fisher's exact approach have often been used for testing the association between two dichotomous variables. The asymptotic approach may be appropriate to use in large samples but is often criticized for being associated with unacceptable high actual type I error rates for small to medium sample sizes. Fisher's exact approach suffers from conservative type I error rates and low power. For these reasons, a number of exact unconditional approaches have been proposed, which have been seen to be generally more powerful than exact conditional counterparts. We consider the traditional unconditional approach based on maximization and compare it to our presented approach, which is based on estimation and maximization. We extend the unconditional approach based on estimation and maximization to designs with the total sum fixed. The procedures based on the Pearson chi‐square, Yates's corrected, and likelihood ratio test statistics are evaluated with regard to actual type I error rates and powers. A real example is used to illustrate the various testing procedures. The unconditional approach based on estimation and maximization performs well, having an actual level much closer to the nominal level. The Pearson chi‐square and likelihood ratio test statistics work well with this efficient unconditional approach. This approach is generally more powerful than the other p‐value calculation methods in the scenarios considered.     

A non-parametric independence test using permutation entropy

In the present paper we construct a new, simple, consistent and powerful test for independence by using symbolic dynamics and permutation entropy as a measure of serial dependence. We also give a standard asymptotic distribution of an affine transformation of the permutation entropy under the null hypothesis of independence. The test statistic and its standard limit distribution are invariant to any monotonic transformation. The test applies to time series with discrete or continuous distributions. Eventhough the test is based on entropy measures, it avoids smoothed non-parametric estimation. An application to several daily financial time series illustrates our approach.     

Bayesian Comparison of ARIMA and Stationary ARMA Models

Time series analysts have long been concerned with distinguishing stationary "generating processes" from processes for which differencing is required to induce stationarity. In practical applications, this issue is addressed almost invariably through formal hypothesis testing. In this paper, we explore some aspects of the Bayesian approach to the problem, leading to the calculation of posterior odds ratios. Interesting features arise in the simplest possible variant of the problem, where a choice has to be made between a random walk and a stationary first order autoregressive model. We discuss in detail the analysis of this case, and also indicate how our approach extends to the more general comparison of an ARIMA model with a stationary competitor.     

The cross-country impact of ECB policies: Asymmetries in – Asymmetries out?

Since its inception, the adequacy of the Eurozone to be an Optimal Currency Area has been questioned, and, along with it, the homogeneous transmission and impact of the monetary impulses across the member countries. We provide a comprehensive assessment of the transmission mechanism’s functioning, its symmetry, impact on target variables, and evolution, addressing all the questions which have remained unanswered in the previous literature, while adding evidence on the impact of non-standard policy measures. We do so by adopting a Bayesian Time-Varying Parameters FAVAR model that fixes the flaws present in past research. The empirical analysis shows that the occurrence of the two crises significantly altered policy transmission, with both the interest rate and credit channel being consistently affected. It also shows that while they provided effective stimuli to the economies, the unconventional measures implemented were not able to fix those asymmetries. Policy-wise, our findings suggest that authorities must push towards consistent innovation on the fiscal side, while gaining more confidence with regards to the new monetary toolkit.     

Forecasting financial time-series using data mining models: A simulation study

In this paper, we examine the static and dynamic predictive ability of artificial neural networks and random forests for financial time series within a simulation context. Our simulation design, in which we generate data from an AR(1)-GARCH(1,1) model, allows for several degrees of persistence in the mean equation to mimic the behavior of short and long-horizon asset returns. While the true data generating process beats the data mining techniques in terms of static forecasting, the novelty in this paper is to demonstrate that the data mining techniques outperform the true model under a dynamic forecasting scheme for moderate to highly persistent time series. We provide an empirical application using one-day and long-horizon returns on two exchange rates. Our empirical findings corroborate our simulation results in that the data mining models exhibit superior predictive ability for highly persistent time series. We discuss the importance of our findings for asset allocation and portfolio management.     

The time-varying effect of monetary policy in China

A sound understanding of monetary transmission mechanism is valuable because it helps the central bank to determine the proper course of monetary policy to balance growth and inflation. As China’s domestic financial markets deepen and develop further towards a market-based system, the country’s monetary policy instrument and transmission should continue to improve for managing economic conditions. Using a short-term key interest rate as standard monetary policy tool and time-varying parameter techniques, this study empirically demonstrates that China’s monetary policy framework is in the midst of transitioning to a market-based approach.     

Exploring the adoption of precision agricultural technologies: A cross regional study of EU farmers

Precision agricultural technologies (PATs) allow more detailed management of in-field variability. Policy and advisory communities have championed PATs as a route to preserving natural capital whilst increasing productivity from agricultural land. A range of PATs are currently available for the agricultural producer but uptake varies by the type of technology and region. Whereas most studies on uptake have focused on US or Australia we empirically examine uptake of machine guidance (MG) and variable rate nitrogen technologies (VRNT) within European farming systems. Using primary information from 971 arable crop growers across five countries: Belgium, Germany, Greece, the Netherlands and the UK, a multilevel random intercept regression estimated a) the differences between adoption and non-adoption and b) the differences between VRNT and MG adoption. We find, aside from size and income differences, which reflect the economic cost barrier to adoption, an attitudinal difference, in terms of optimism towards the technology’s economic return leading to more probability of uptake. Moreover innovative and information seeking behaviour also proved significant when upgrading from machine guidance to variable rate technologies. Subsidy and taxation were considered positive drivers of uptake within the community. However, results suggest that more indirect interventions, such as informational support to counteract industry bias, and demonstration to prove the viability of economic return may be effective at meeting land manager and policy expectations towards PATs.     

Bayesian robustness modelling using the O‐regularly varying distributions

The theory of robustness modelling is essentially based on heavy‐tailed distributions, because longer tails are more prepared to deal with diverse information (such as outliers) because of the higher probabilities on the tails. There are many classes of distributions that can be regarded as heavy tails; some of them have interesting properties and are not explored in statistics. In the present work, we propose a robustness modelling approach based on the O‐regularly varying class (ORV), which is a generalization of the regular variation family; however, the ORV class allows more flexible tails behaviour, which can improve the way in which the outlying information is discarded by the model. We establish sufficient conditions in the location and in the scale parameter structures, which allow to resolve automatically the conflicts of information. We also provide a procedure for generating new distributions within the ORV class.     

Agricultural land-use dynamics: Assessing the relative importance of socioeconomic and biophysical drivers for more targeted policy

A detailed understanding of multiple human and environmental factors influencing land allocations among agricultural uses can facilitate more efficient and targeted land policy. To show this, we used a comprehensive dataset of socioeconomic, physiographic, and climatic indicators to investigate potential determinants of land-use in Australia’s intensive agricultural region during the period 1992–2010. We applied a seemingly unrelated regressions land-use shares spatial error model with random effects coupled with variance decomposition analysis to identify the statistical significance, direction and magnitude of observed associations between land-use and its drivers.Population: density, rainfall, equity ratio, and access to markets were the most influential policy-relevant land-use factors. Land allocations to cereals and livestock production were significantly influenced by spatiotemporal rainfall and temperature variability. Improved pastures, cereals, annual and perennial crops plantations were larger in regions with better access to markets. Increases in equity ratio (i.e., better financial position) were associated with larger land allocations to improved pastures and annual crops and smaller extensive grazing area. Marginal associations were detected between land-use and output prices, and higher population density was associated with lower shares for all high value agricultural land-uses. The results suggest that improved transportation infrastructure, zoning regulations, and mechanisms to reduce farm debt exposure and risks from climate variability could have significant impact on the configuration of the Australian agricultural landscape.     

浅孔爆破在露天场地平整中的应用

文章介绍了浅孔爆破在露天场地平整的应用,通过选取合理的爆破参数,采取有效措施控制爆破飞石、爆破震动,保证了爆破区域周边建筑物安全。