金融知识获取圈层对农户农地抵押贷款参与的影响研究——基于失地风险感知的中介效应分析

积极稳妥推广农地抵押贷款融资业务,持续深化农村土地资产与金融资源的有机衔接是完善金融服务乡村振兴战略的关键内容,深入探究农户金融知识获取的人际圈层来源以及失地风险感知因素对完善农地抵押贷款相关制度建设具有重要意义。文章首先理论阐释了金融知识获取圈层、失地风险感知对农户农地抵押贷款参与的影响,详细剖析了农户失地风险感知的中介机理;然后采用因子分析法测度金融知识获取圈层变量,提取4个公共因子分别界定为亲属关系圈、朋友关系圈、乡邻关系圈和机构参与圈;最后以陕西、宁夏、山东三省农地抵押贷款试点地区1 312户农户调查数据为样本,利用Logit模型和Bootstrap中介效应检验方法,实证分析了金融知识获取圈层、失地风险感知对农户农地抵押贷款参与的影响,并揭示了失地风险感知的中介效应类型和数值大小。研究结果显示：金融知识获取圈层正向促进农户农地抵押贷款参与,其中金融知识获取亲属关系圈的促进作用大于机构参与圈;金融知识获取圈层负向影响农户失地风险感知,机构参与圈的影响最大,亲属关系圈次之,朋友关系圈最小;在四个金融知识获取圈层下,失地风险感知均对农户农地抵押贷款参与具有负向影响;失地风险感知在金融...     

Estimating probabilities from invariant permutation distributions

Summary We introduce variance reduction techniques as general tools for estimating probabilities from invariant permutation distributions. The paper discusses importance sampling, antithetic sampling and control variates sampling as alternatives to uniform Monte Carlo sampling for estimating exact critical values orP-values in a broad class of permutation tests. Results may be extended to permutation confidence intervals and linear rank tests. An asymptotic theory is provided for each proposed variance reduction method. Invited paper at the Conference held in Bologna, Italy, 27–28 May 1993, on ?Statistical Tests: Methodology and Econometric Applications?.     

Bootstrapping a consistent nonparametric goodness-of-fit test

In this paper, we employ the parametric bootstrap to approximate the finite sample distribution of a goodness-of-fit test statistic in Fan (1994). We show that the proposed bootstrap procedure works in that the bootstrap distribution conditional on the random sample tends to the asymptotic distribution of the test statistic in probability. A simulation study demonstrates that the bootstrap approximation works extremely well in small samples with only 25 observations and is very robust to the value of the smoothing parameter in the kernel density estimation.     

Devaluation expectations and the unit root hypothesis: The Italian Lira in the European monetary system

Summary In several studies the unit root hypothesis of EMS exchange rates is analysed within the context of devaluation expectations estimation. By means of bootstrap inference it is shown that these procedures are not compatible with standard Dickey-Fuller significance levels and may lead to a wrong rejection of the null hypothesis. In the case of the Italian Lira/Deutsche Mark exchange rate, the hypothesis of a unit root is not rejected and expectations can be modelled by means of a reflected Brownian motion. The estimated devaluation expectations are related with some macro variables which provide evidence for the structure of expectations. This research has been partially supported with 40% and 60% MURST grants. The author wishes to thank the Bank of Italy for the exchange rates and the interest rates data and Ulf S?derstr?m for providing macroeconomic indicators. Useful suggestions from Riccardo Cesari, Michele Costa and two anonymous referees are gratefully acknowledged.     

A test for crossing receiver operating characteristic (roc) curves

The receiver operating characteristic (ROC) curve gives a graphical representation of sensitivity and specificity of a prediction model when varying the decision treshold on a diagnostic criterion. A classical test for comparing the overall accuracies for two models -1 and 2- is based on the difference between ROC curves areas - related to its standard error. This test is designed for the situation where ROC curve 1 caps ROC curve 2. Often both curves cross :in this paper, a new test, based on the integrated difference between the curves, is proposed to deal with this situation. In a simulation experiment, the new test was less powerful than the old test for detecting an overall superiority, but much more powerfull against the crossing alternative.     

Testing hypotheses about covariance matrices using bootstrap methods

The usual chi-squared approximation to test statistics based on normal theory for testing covariance structures of multivariate populations is very sensitive to the normality assumption. Two general bootstrap procedures are developed in this paper to obtain approximately valid critical values for these test statistics when the data are not normally distributed. The first is based on separate sampling from individual samples, and the second is based on sampling from pooled samples. Although the second method requires more assumptions, its small sample properties are better.     

Exponential-Bound Property of Estimators and Variable Selection in Generalized Additive Models

In this article, utilizing a scale mixture of skew-normal distribution in which mixing random variable is assumed to follow a mixture model with varying weights for each observation, we introduce a generalization of skew-normal linear regression model with the aim to provide resistant results. This model, which also includes the skew-slash distribution in a particular case, allows us to accommodate and detect outlying observations under the skew-normal linear regression model. Inferences about the model are carried out through the empirical Bayes approach. The conditions for propriety of the posterior and for existence of posterior moments are given under the standard noninformative priors for regression and scale parameters as well as proper prior for skewness parameter. Then, for Bayesian inference, a Markov chain Monte Carlo method is described. Since posterior results depend on the prior hyperparameters, we estimate them adopting the empirical Bayes method as well as using a Monte Carlo EM algorithm. Furthermore, to identify possible outliers, we also apply the Bayes factor obtained through the generalized Savage-Dickey density ratio. Examining the proposed approach on simulated instance and real data, it is found to provide not only satisfactory parameter estimates rather allow identifying outliers favorably.     

Bootstrap Prediction Intervals for Factor Models

We propose bootstrap prediction intervals for an observation h periods into the future and its conditional mean. We assume that these forecasts are made using a set of factors extracted from a large panel of variables. Because we treat these factors as latent, our forecasts depend both on estimated factors and estimated regression coefficients. Under regularity conditions, asymptotic intervals have been shown to be valid under Gaussianity of the innovations. The bootstrap allows us to relax this assumption and to construct valid prediction intervals under more general conditions. Moreover, even under Gaussianity, the bootstrap leads to more accurate intervals in cases where the cross-sectional dimension is relatively small as it reduces the bias of the ordinary least-squares (OLS) estimator.     

Kernel estimations for multivariate density functional with bootstrap

In this article the bootstrap method is discussed for the kernel estimation of the multivariate density function. We have considered sample mean functional and constructed its consistency and asymptotic normality by bootstrap estimator. It has been shown that the bootstrap works for kernel estimates of multivariate density functional. The convergence rate with bootstrap for density has been proved. Finally, two simulations of application are given.     

Confidence intervals for the closed population size under inverse sampling without replacement

Inverse sampling is an appropriate design for the second phase of capture-recapture experiments which provides an exactly unbiased estimator of the population size. However, the sampling distribution of the resulting estimator tends to be highly right skewed for small recapture samples, so, the traditional Wald-type confidence intervals appear to be inappropriate. The objective of this paper is to study the performance of interval estimators for the population size under inverse recapture sampling without replacement. To this aim, we consider the Wald-type, the logarithmic transformation-based, the Wilson score, the likelihood ratio and the exact methods. Also, we propose some bootstrap confidence intervals for the population size, including the with-replacement bootstrap (BWR), the without replacement bootstrap (BWO), and the Rao–Wu’s rescaling method. A Monte Carlo simulation is employed to evaluate the performance of suggested methods in terms of the coverage probability, error rates and standardized average length. Our results show that the likelihood ratio and exact confidence intervals are preferred to other competitors, having the coverage probabilities close to the desired nominal level for any sample size, with more balanced error rate for exact method and shorter length for likelihood ratio method. It is notable that the BWO and Rao–Wu’s rescaling methods also may provide good intervals for some situations, however, those coverage probabilities are not invariant with respect to the population arguments, so one must be careful to use them.