Taking financial frictions to the data

We estimate a Dynamic Stochastic General Equilibrium (DSGE) model with various financial frictions and analyze how well the model explains the Great Recession. Predictive analysis shows that the model can only slightly better explain the large deviation from trend during the crisis relative to a model without financial frictions. Specifically, the risk premium shock, which is a shock to the external finance premium of the entrepreneurs׳ leverage, explains the largest part of the investment downfall during the crisis. However, the ‘balance sheet’ channel of financial frictions in the model, which structurally links balance sheet conditions of financial intermediaries and nonfinancial borrowers to their borrowing rates, is estimated to be weak. We examine alternative prior specifications for how the financial frictions enter the model and continue to find a limited role for these frictions. Rolling-window estimation provides evidence for substantial time variation in parameters governing financial frictions. We conclude that the well-known financial frictions studied in this paper are not able to explain the financial crisis in a linearized and estimated model.     

小样本训练集下人脸图像超分辨率重构算法

文章提出了一种针对小样本训练集的人脸图像超分辨率重构算法。该算法在马尔可夫网络模型的基础上,将视觉相容性检查准则引入到候选分块的匹配准则中,提高了匹配精度和块间兼容性。将权值机制融合进图像的重构过程,强化了训练集的有用性。实验表明,在小样本训练集下,算法能有效地抑制块效应和局部失真现象,明显改善超分辨率图像的质量。     

Modelling Group Heterogeneity for Small Area Estimation Using M‐Quantiles

Small area estimation typically requires model‐based methods that depend on isolating the contribution to overall population heterogeneity associated with group (i.e. small area) membership. One way of doing this is via random effects models with latent group effects. Alternatively, one can use an M‐quantile ensemble model that assigns indices to sampled individuals characterising their contribution to overall sample heterogeneity. These indices are then aggregated to form group effects. The aim of this article is to contrast these two approaches to characterising group effects and to illustrate them in the context of small area estimation. In doing so, we consider a range of different data types, including continuous data, count data and binary response data.     

Robust prediction of domain compositions from uncertain data using isometric logratio transformations in a penalized multivariate Fay–Herriot model

Assessing regional population compositions is an important task in many research fields. Small area estimation with generalized linear mixed models marks a powerful tool for this purpose. However, the method has limitations in practice. When the data are subject to measurement errors, small area models produce inefficient or biased results since they cannot account for data uncertainty. This is particularly problematic for composition prediction, since generalized linear mixed models often rely on approximate likelihood inference. Obtained predictions are not reliable. We propose a robust multivariate Fay–Herriot model to solve these issues. It combines compositional data analysis with robust optimization theory. The nonlinear estimation of compositions is restated as a linear problem through isometric logratio transformations. Robust model parameter estimation is performed via penalized maximum likelihood. A robust best predictor is derived. Simulations are conducted to demonstrate the effectiveness of the approach. An application to alcohol consumption in Germany is provided.     

小城镇之殊途背后的经济社会原因

我国小城镇经过十几年的发展,呈现出兴与衰两种截然相反的局面,这与一定的经济、社会原因相关.以费孝通的<小城镇,大问题>的思路为基础,介绍了三种不同类型的小城镇的路径走向,并分析了其殊途背后的经济、社会原因.     

A note on estimation based on record data

In data-processing standpoint, an efficient algorithm for identifying the minimum value among a set of measurements are record statistics. From a sequence of n independent identically distributed continuous random variables only about log(n) records are expected, so we expect to have little data, hence any prior information is welcome (Houchens, Record value theory and inference, Ph.D. thesis, University of California, Riverside, 1984). In this paper, non-Bayesian and Bayesian estimates are derived for the two parameters of the Exponential distribution based on record statistics with respect to the squared error and Linear-Exponential loss functions and then compared with together. The admissibility of some estimators is discussed.     

新形势下小城镇发展的思路与对策——以邛崃市夹关镇为例

加快小城镇件事是统筹城乡发展和建设社会主义新农村的重要途径,建设好小城镇对于启动农村内需、增加农民收入、转移农村劳动力等方面都有积极意义。     

Combining Data from New and Traditional Sources in Population Surveys

This paper is a review of some applications of the combination of data sets, such as combining census or administrative data and survey data, constructing expanded data sets through linkage, combining large‐scale commercial databases with survey data and harnessing designed data collection to be able to make use of non‐probability samples. It is aimed to highlight their commonalities and differences and to formulate some general principles for data set combination.     

Localised Estimates of Dynamics of Multi‐dimensional Disadvantage: An Application of the Small Area Estimation Technique Using Australian Survey and Census Data

Deep and persistent disadvantage is an important, but statistically rare, phenomenon in the population, and sample sizes are usually not large enough to provide reliable estimates for disaggregated analysis. Survey samples are typically designed to produce estimates of population characteristics of planned areas. The sample sizes are calculated so that the survey estimator for each of the planned areas is of a desired level of precision. However, in many instances, estimators are required for areas of the population for which the survey providing the data was unplanned. Then, for areas with small sample sizes, direct estimation of population characteristics based only on the data available from the particular area tends to be unreliable. This has led to the development of a class of indirect estimators that make use of information from related areas through modelling. A model is used to link similar areas to enhance the estimation of unplanned areas; in other words, they borrow strength from the other areas. Doing so improves the precision of estimated characteristics in the small area, especially in areas with smaller sample sizes. Social science researchers have increasingly employed small area estimation to provide localised estimates of population characteristics from surveys. We explore how to extend this approach within the context of deep and persistent disadvantage in Australia. We find that because of the unique circumstances of the Australian population distribution, direct estimates of disadvantage have substantial variation, but by applying small area estimation, there are significant improvements in precision of estimates.     

Model‐Assisted Estimation of a Spatial Population Mean

This paper deals with the estimation of the mean of a spatial population. Under a design‐based approach to inference, an estimator assisted by a penalized spline regression model is proposed and studied. Proof that the estimator is design‐consistent and has a normal limiting distribution is provided. A simulation study is carried out to investigate the performance of the new estimator and its variance estimator, in terms of relative bias, efficiency, and confidence interval coverage rate. The results show that gains in efficiency over standard estimators in classical sampling theory may be impressive.     

A Predictive Estimator of the Mean with Missing Data

One of the most difficult problems confronting investigators who analyze data from surveys is how treat missing data. Many statistical procedures can not be used immediately if any values are missing. This paper considers the problem of estimating the population mean using auxiliary information when some observations on the sample are missing and the population mean of the auxiliary variable is not available. We use tools of classical statistical estimation theory to find a suitable estimator. We study the model and design properties of the proposed estimator. We also report the results of a broad-based simulation study of the efficiency of the estimator, which reveals very promising results.     

A smoothed maximum score estimator for the binary choice panel data model with an application to labour force participation

In a binary choice panel data model with individual effects and two time periods, Manski proposed the maximum score estimator based on a discontinuous objective function and proved its consistency under weak distributional assumptions. The rate of convergence is low ( N ^1/3) and its limit distribution cannot easily be used for statistical inference. In this paper we apply the idea of Horowitz to smooth Manski's objective function. The resulting smoothed maximum score estimator is consistent and asymptotically normal with a rate of convergence that can be made arbitrarily close to N ^1/2, depending on the strength of the smoothness assumptions imposed. The estimator can be applied to panels with more than two time periods and to unbalanced panels. We apply the estimator to analyze labour force participation of married Dutch females.     

Using Complex Surveys to Estimate the L1‐Median of a Functional Variable: Application to Electricity Load Curves

Mean profiles are widely used as indicators of the electricity consumption habits of customers. Currently, in Électricité De France (EDF), class load profiles are estimated using point‐wise mean profiles. Unfortunately, it is well known that the mean is highly sensitive to the presence of outliers, such as one or more consumers with unusually high‐levels of consumption. In this paper, we propose an alternative to the mean profile: the L ₁ ‐ median profile which is more robust. When dealing with large data sets of functional data (load curves for example), survey sampling approaches are useful for estimating the median profile avoiding storing the whole data. We propose here several sampling strategies and estimators to estimate the median trajectory. A comparison between them is illustrated by means of a test population. We develop a stratification based on the linearized variable which substantially improves the accuracy of the estimator compared to simple random sampling without replacement. We suggest also an improved estimator that takes into account auxiliary information. Some potential areas for future research are also highlighted.     

少数民族地区中小企业可持续发展问题及对策

少数民族地区中小企业发展过程中面临着交通不发达与信息化水平较低、政府支持力度不够、融资难、人力资源匮乏、核心竞争力不强、缺乏持续创新能力等问题,这些问题的存在阻碍着少数民族地区中小企业的可持续发展。文章在分析以上问题的基础上,提出了促进少数民族地区中小企业可持续发展的对策和建议。     

湘鄂渝黔边区中小企业竞争力提升的研究

湘鄂渝黔边区中小企业是该地区经济的基础,对国民经济和社会发展起到不可替代的作用,但总量大、规模小,普遍竞争实力不强。文章在全面分析边区中小企业现状和特点的基础上,对如何提升边区中小企业的竞争力进行研究,进而从改善企业发展环境、寻找竞争优势、培育核心竞争力、形成产业集群等方面提出竞争力提升的对策。     

Estimation of a survival curve with randomly censored data in the presence of a covariate

This paper deals with the estimation of a survival curve in models with random right censoring and dependent censoring mechanism. We consider a specific dependent censorship model in which conditional on a covariate, the survival and censoring times are assumed to be independent. We investigate asymptotic properties of a corrected version of a survival curve estimator introduced by Cheng (1989). In particular we show uniform strong consistency and weak convergence to a Gaussian process. Comparisons of this estimator with the well-known Kaplan-Meier-estimator are included. Finally, some examples illustrate how the estimator performs. Received: February 2000     

Semiparametric estimation of a censored regression model with an unknown transformation of the dependent variable

This paper presents a method for estimating the model Λ(Y)=min(β′X+U, C), where Y is a scalar, Λ is an unknown increasing function, X is a vector of explanatory variables, β is a vector of unknown parameters, U has unknown cumulative distribution function F, and C is a censoring threshold. It is not assumed that Λ and F belong to known parametric families; they are estimated nonparametrically. This model includes many widely used models as special cases, including the proportional hazards model with unobserved heterogeneity. The paper develops n^1/2-consistent, asymptotically normal estimators of Λ and F. Estimators of β that are n^1/2-consistent and asymptotically normal already exist. The results of Monte Carlo experiments illustrate the finite-sample behavior of the estimators.     

An application of approximate finite sample results to parameter estimation in a linear errors-in-variables model

In the simple errors-in-variables model the least squares estimator of the slope coefficient is known to be biased towards zero for finite sample size as well as asymptotically. In this paper we suggest a new corrected least squares estimator, where the bias correction is based on approximating the finite sample bias by a lower bound. This estimator is computationally very simple. It is compared with previously proposed corrected least squares estimators, where the correction aims at removing the asymptotic bias or the exact finite sample bias. For each type of corrected least squares estimators we consider the theoretical form, which depends on an unknown parameter, as well as various feasible forms. An analytical comparison of the theoretical estimators is complemented by a Monte Carlo study evaluating the performance of the feasible estimators. The new estimator proposed in this paper proves to be superior with respect to the mean squared error.     

Linking customisation of ERP systems to support effort: an empirical study

The amount of customisation to an enterprise resource planning (ERP) system has always been a major concern in the context of the implementation. This article focuses on the phase of maintenance and presents an empirical study about the relationship between the amount of customising and the resulting support effort. We establish a structural equation modelling model that explains support effort using customisation effort, organisational characteristics and scope of implementation. The findings using data from an ERP provider show that there is a statistically significant effect: with an increasing amount of customisation, the quantity of telephone calls to support increases, as well as the duration of each call.     

Efficiency of independence working analysis of correlated survival data with general cluster size

We consider estimation of the regression coefficients in the multivariate positive stable frailty model with Weibull marginals introduced by Hougaard (1986) . For general cluster sizes and various covariate patterns, we study the efficiency, relative to full maximum likelihood, of estimation from the independence working model and from the fixed effects model.