Nonparametric Estimation of Search Costs for Differentiated Products: Evidence from Medigap

Abstract

This article develops a method to estimate search frictions as well as preference parameters in differentiated product markets. Search costs are nonparametrically identified, which means our method can be used to estimate search costs in differentiated product markets that lack a suitable search cost shifter. We apply our model to the U.S. Medigap insurance market. We find that search costs are substantial: the estimated median cost of searching for an insurer is $30. Using the estimated parameters we find that eliminating search costs could result in price decreases of as much as $71 (or 4.7%), along with increases in average consumer welfare of up to $374.     

空气质量对生活满意度的效应研究——基于序数分层空间自回归Probit模型

在居民生活满意度的相关研究中,除考虑人口学特征外,越来越多的实证同时考虑了微观个体所处的宏观环境,对这类呈嵌套结构的分层数据需构建分层统计模型,但传统的分层统计模型未考虑真实的空间依赖。本文将分层统计模型和空间自回归模型相结合,创新性地构建了四种序数分层空间自回归Probit模型,该类模型能够合理地对因变量为序数且存在空间依赖情况并呈分层结构的数据进行建模,模型可避免忽略真实的空间依赖对模型估计的不利影响,且能够对高层组间的空间效应和低层个体间的空间效应区别对待,更有利于模型的解释。最后,空气质量对居民生活满意度的效应实证研究表明:空气质量确实能够对生活满意度产生影响,居民对空气质量的认识和要求并非孤立地局限于本地,而是对一个区域空气质量的空间综合结果。对比2018年和2016年模型结果可知:空气质量的福利效应无法被其他民生福祉因素所取代,并且随着空气质量相关统计信息的高度开放和广泛传播,居民更加重视空气质量,也形成了更加全局的了解。     

Multiresolution analysis of linearly oriented spatial point patterns

The assumption of direction invariance, i.e. isotropy, is often made in the practical analysis of spatial point patterns due to simpler interpretation and ease of analysis. However, this assumption is many times hard to find in real applications. We propose a wavelet-based approach to test for isotropy in spatial patterns based on the logarithm of the directional scalogram. Under the null hypothesis of isotropy, a random isotropic point pattern should be expected to have the same value of the directional scalogram for any possible direction. Monte Carlo simulations of the logarithm of the directional scalogram over all directions are used to approximate the test distribution and the critical values. We demonstrate the efficacy of the approach through simulation studies and an application to a desert plant data set, where our approach confirms suspected directional effects in the spatial distribution of the desert plant species.     

Institutions and Economic Outcomes: A Dominance-Based Analysis

An important issue in both welfare and development economics is the interaction between institutions and economic outcomes. While welfarists are typically concerned with how these variables contribute to overall wellbeing, empirical assessments of their joint contribution are limited. Development economists, on the other hand, have focused extensively on whether institutions cause or are caused by growth yet the relevant literature is still rife with debate. In this article, we use a notion of distributional dominance to tackle both the measurement of multivariate welfare and the evaluation of inter-temporal dependence without hindrance from the mix of discrete (political) and continuous (economic) variables in our data set. On the causality front, our results support the view that institutions promote growth more than growth promotes institutions. On the welfare front, we find that economic growth had a positive impact from 1960 to 2000 but declines in institutional quality over the earlier part of this period were sufficient to produce a decline in overall wellbeing until the mid-1970s. Subsequent improvements in institutions then reversed the trend and, ultimately, wellbeing in 2000 was higher than that in 1960.     

Stochastic dominance amongst swedish income distributions

:Sweden's income distribution for the whole population and for subgroups, including its immigants, has been extensively studied. The interest in this area has grown with increasing availability of data, including panels. The previous studies are based on indices of inequality or mobility. While indices are useful for complete ordering and have an air of "decisiveness" about them, they lack universal acceptance of the value judgements inherent to the welfare functions that underlay any index. In contrast, uniformpartial order relations are studied in this paper which rank welfare situations over very wide classes of welfare functions. We conduct bootstrap tests for the existence of first and second order stochastic dominance amongst Sweden's income distributions over time and for several subgroups of immigrants. Analysis of immigrant's income is motivated by the fact that the development of income for immigrants has been different and strongly affected by their length of residence and countries of origin. We consider several non-consecutive waves of a panel of incomes in Sweden. Two income definitions are developed. One is pre-transfers and taxes, gross income, the other is a post-transfers and taxes, disposable income. The comparison of the distribution of these two variables affords a partial view of Sweden's welfare system. We have focused on the incomes of Swede's and immigrant groups of single individuals identified by country of origin, length of residence, age, education, gender, marital status and other relevant characteristics. We find that first order dominance is rare, but second order relations hold in several cases, especially amongst disposable income distributions. Sweden's incomes and welfare policies favor the elderly, females, larger families, and longer periods of residency. We find, in general, the higher the educational credentials, the higher is the burden of this equalization policy.     

Estimating the probability of simultaneous rainfall extremes within a region: a spatial approach

In this paper we investigate the impact of model mis-specification, in terms of the dependence structure in the extremes of a spatial process, on the estimation of key quantities that are of interest to hydrologists and engineers. For example, it is often the case that severe flooding occurs as a result of the observation of rainfall extremes at several locations in a region simultaneously. Thus, practitioners might be interested in estimates of the joint exceedance probability of some high levels across these locations. It is likely that there will be spatial dependence present between the extremes, and this should be properly accounted for when estimating such probabilities. We compare the use of standard models from the geostatistics literature with max-stables models from extreme value theory. We find that, in some situations, using an incorrect spatial model for our extremes results in a significant under-estimation of these probabilities which – in flood defence terms – could lead to substantial under-protection.     

Crossover design in clinical trials for binary response

In this paper, we consider a binary response model for the analysis of the two-treatment, two-period and four-sequence crossover design. We have introduced intra-patient drug dependency parameter in the model and provide two tests for the hypothesis of equality of treatment effects. We employ Monte Carlo simulation to compare our tests and a test that works under parallel design on the basis of type I error rate and power. We find that our procedures are dominant over the competitor with respect to power. Finally, we use a data set to illustrate the applicability of our procedure.     

等价尺度及其应用研究

文章首先讨论了等价尺度的概念,然后通过对两个等价尺度模型的分析来把握等价尺度构造的基本思想,最后借鉴国外应用等价尺度的经验对中国家庭贫困线加以测算。从对等价尺度的初步探讨中发现,等价尺度是家庭福利比较的重要工具,这将对中国社会保障政策的制定有所裨益。     

基础设施投资效率的空间溢出与门限效应研究

利用数据包络分析（DEA）估计了中国31个省份1999—2006年基础设施投资的相对效率,并以此代表各地区基础设施投资的成效,进而分析其对经济增长的贡献是否存在空间溢出和门限效应。采用普通面板回归、空间面板回归和门限面板回归这三种基于面板数据的回归模型进行实证分析后发现：基础设施投资效率对本地区和相邻地区的经济增长均有显著促进作用,并且随着经济规模的扩大,基础设施投资效率对经济增长的促进作用也有所提高,即证实了空间溢出和门限效应的存在。     

我国房地产价格区域互动的实证研究

本文采用广义空间动态面板数据模型（GSDPD）和2002第一季度至2010年第三季度省际面板数据分析了我国房地产价格的区域互动。实证结果表明,我国房地产价格互动存在空间滞后效应和时间滞后效应,且城镇居民可支配收入、信贷扩张、土地价格和房屋竣工面积是影响我国房价的重要因素。实证结果还表明,相邻地区之间房价的影响程度比其它不相邻地区之间房价的影响程度大,经济特征相似地区间房价的相互影响程度比经济特征不相似地区间房价的相互影响程度小。     

Computationally feasible estimation of the covariance structure in generalized linear mixed models

In this paper, we discuss how a regression model, with a non-continuous response variable, which allows for dependency between observations, should be estimated when observations are clustered and measurements on the subjects are repeated. The cluster sizes are assumed to be large. We find that the conventional estimation technique suggested by the literature on generalized linear mixed models (GLMM) is slow and sometimes fails due to non-convergence and lack of memory on standard PCs. We suggest to estimate the random effects as fixed effects by generalized linear model and to derive the covariance matrix from these estimates. A simulation study shows that our proposal is feasible in terms of mean-square error and computation time. We recommend that our proposal be implemented in the software of GLMM techniques so that the estimation procedure can switch between the conventional technique and our proposal, depending on the size of the clusters.     

An empirical investigation into the effect of childbearing on economic wellbeing in Europe

This paper investigates the extent childbearing among couples in Europe affects their level of economic well being. We do so by implementing a propensity score matching procedure in combination with a difference-in-difference estimator. Using data from European Community Household Panel Survey (ECHP), we compare how the impact of childbearing on wellbeing varies among countries. We use several measures for wellbeing, including poverty status and various deprivation indices that take into account the multidimensionality of individuals‘ assessment of wellbeing. Not unexpected we find childbearing tend to worsen the economic wellbeing of households, but with important differences in magnitude across countries. In Scandinavian countries the effect is small and rarely significant, it is strong in the UK and also significant in Mediterranean countries. Depending on the measure of wellbeing, we find important differences among countries that are similar in terms of welfare provision.     

Modelling concurrency of events in on-line auctions via spatiotemporal semiparametric models

Summary.  We introduce a semiparametric approach for modelling the effect of concurrent events on an outcome of interest. Concurrency manifests itself as temporal and spatial dependences. By temporal dependence we mean the effect of an event in the past. Modelling this effect is challenging since events arrive at irregularly spaced time intervals. For the spatial part we use an abstract notion of 'feature space' to conceptualize distances among a set of item features. We motivate our model in the context of on-line auctions by modelling the effect of concurrent auctions on an auction's price. Our concurrency model consists of three components: a transaction-related component that accounts for auction design and bidding competition, a spatial component that takes into account similarity between item features and a temporal component that accounts for recently closed auctions. To construct each of these we borrow ideas from spatial and mixed model methodology. The power of this model is illustrated on a large and diverse set of laptop auctions on eBay.com. We show that our model results in superior predictive performance compared with a set of competitor models. The model also allows for new insight into the factors that drive price in on-line auctions and their relationship to bidding competition, auction design, product variety and temporal learning effects.     

To move or not to move to find a new job: spatial duration time model with dynamic covariate effects

The aim of this paper is to show the flexibility and capacity of penalized spline smoothing as estimation routine for modelling duration time data. We analyse the unemployment behaviour in Germany between 2000 and 2004 using a massive database from the German Federal Employment Agency. To investigate dynamic covariate effects and differences between competing job markets depending on the distance between former and recent working place, a functional duration time model with competing risks is used. It is build upon a competing hazard function where some of the smooth covariate effects are allowed to vary with unemployment duration. The focus of our analysis is on contrasting the spatial, economic and individual covariate effects of the competing job markets and on analysing their general influence on the unemployed's re-employment probabilities. As a result of our analyses, we reveal differences concerning gender, age and education. We also discover an effect between the newly formed and the old West German states. Moreover, the spatial pattern between the considered job markets differs.     

Optimal predictive design augmentation for spatial generalised linear mixed models

A typical model for geostatistical data when the observations are counts is the spatial generalised linear mixed model. We present a criterion for optimal sampling design under this framework which aims to minimise the error in the prediction of the underlying spatial random effects. The proposed criterion is derived by performing an asymptotic expansion to the conditional prediction variance. We argue that the mean of the spatial process needs to be taken into account in the construction of the predictive design, which we demonstrate through a simulation study where we compare the proposed criterion against the widely used space-filling design. Furthermore, our results are applied to the Norway precipitation data and the rhizoctonia disease data.     

Modelling spatial intensity for replicated inhomogeneous point patterns in brain imaging

Summary.  Pharmacological experiments in brain microscopy study patterns of cellular activ- ation in response to psychotropic drugs for connected neuroanatomic regions. A typical ex- perimental design produces replicated point patterns having highly complex spatial variability. Modelling this variability hierarchically can enhance the inference for comparing treatments. We propose a semiparametric formulation that combines the robustness of a nonparametric kernel method with the efficiency of likelihood-based parameter estimation. In the convenient framework of a generalized linear mixed model, we decompose pattern variation by kriging the intensities of a hierarchically heterogeneous spatial point process. This approximation entails discretizing the inhomogeneous Poisson likelihood by Voronoi tiling of augmented point patterns. The resulting intensity-weighted log-linear model accommodates spatial smoothing through a reduced rank penalized linear spline. To correct for anatomic distortion between subjects, we interpolate point locations via an isomorphic mapping so that smoothing occurs relative to common neuroanatomical atlas co-ordinates. We propose a criterion for choosing the degree and spatial locale of smoothing based on truncating the ordered set of smoothing covariates to minimize residual extra-dispersion. Additional spatial covariates, experimental design factors, hierarchical random effects and intensity functions are readily accommodated in the linear predictor, enabling comprehensive analyses of the salient properties underlying replicated point patterns. We illustrate our method through application to data from a novel study of drug effects on neuronal activation patterns in the brain of rats.     

Nonlinearity,Breaks, and Long-Range Dependence in Time-Series Models

We study the simultaneous occurrence of long memory and nonlinear effects, such as parameter changes and threshold effects, in time series models and apply our modeling framework to daily realized measures of integrated variance. We develop asymptotic theory for parameter estimation and propose two model-building procedures. The methodology is applied to stocks of the Dow Jones Industrial Average during the period 2000 to 2009. We find strong evidence of nonlinear effects in financial volatility. An out-of-sample analysis shows that modeling these effects can improve forecast performance. Supplementary materials for this article are available online.     

空间自回归模型的局部影响分析和运用

由于空间数据的相依特性，使得通常的删除点诊断异常值的方法不适合采用。为了寻找数据中的异常点和影响点，采用局部影响分析技术，通过引入扰动的方法来发现影响点，最后通过实例说明局部影响分析技术能够有效地发现模型中可能的影响点，并且能够揭示更多的细节信息。     

中国进口持续时间及其决定因素

 本文运用1998～2006年CEPII-BACI数据库考察了HS6分位产品层面中国进口贸易关系的持续时间及其决定因素。我们发现,中国进口持续时间的中位值为1年,均值为2.26年,中国进口贸易关系的危险函数同样也具有明显的负时间依存性。我们运用离散时间cloglog模型考察了进口贸易持续时间的决定因素。结果发现,传统的引力模型变量对持续时间具有与贸易流量类似的影响。同时,汇率波动、初始贸易额和产品差异化等因素都对中国进口贸易关系持续时间产生了显著影响。因此,在进口贸易政策调整过程中必须重视进口持续时间。     

Modelling individual fertility levels in Malawian women: a spatial semiparametric regression model

We applied semiparametric spatial Poisson models to analyse fertility decisions at individual level in Malawi. We used the 2000 Malawi Demographic and Health Survey (MDHS) to investigate determinants of fertility, in the model that allowed for nonlinear, fixed and spatial risk factors. Inference was based on the Bayesian approach. The unstructured spatial effects were modelled using the exchangeable prior, while for the structured spatial effects we used the intrinsic conditional autoregressive models. Nonlinear effects were modelled using P-splines. Results showed non-linear declining trends of fertility with year of marriage and increasing trends with age at marriage. We also observed significant unstructured effects, however, no significant spatial autocorrelated effects were displayed. Overall, total spatial effects were significantly different at district level.