Estimating demand with distance functions: Parameterization in the primal and dual

Our purpose is to investigate the ability of different parametric forms to ‘correctly’ estimate consumer demands based on distance functions using Monte Carlo methods. Our approach combines economic theory, econometrics and quadratic approximation. We begin by deriving parameterizations for transformed quadratic functions which are linear in parameters and characterized by either homogeneity or which satisfy the translation property. Homogeneity is typical of Shephard distance functions and expenditure functions, whereas translation is characteristic of benefit/shortage or directional distance functions. The functional forms which satisfy these conditions and include both first- and second-order terms are the translog and quadratic forms, respectively. We then derive a primal characterization which is homogeneous and parameterized as translog and a dual model which satisfies the translation property and is specified as quadratic. We assess functional form performance by focusing on empirical violations of the regularity conditions. Our analysis corroborates results from earlier Monte Carlo studies on the production side suggesting that the quadratic form more closely approximates the ‘true’ technology or in our context consumer preferences than the translog.     

客户化供应链下顾客生命周期价值计量研究

在客户化供应链中,顾客资产对企业的生存和发展发挥着至关重要的作用,而目前缺乏对顾客生命周期价值计量的有效探讨。为此,基于客户化供应链的研究背景,在综合现有研究顾客生命周期价值不同成果的基础上获得了顾客生命周期价值计量模型,并针对该模型的不足提出了修正方案,从而获得最终的顾客生命周期价值计量模型,为提高供应链企业顾客资产管理水平,促进供应链企业顾客资产的增值提供计量依据。     

基于 LSSVM 的供应链金融业务中企业信用风险评估研究

针对目前供应链金融业务中企业信用风险评估不科学与不完善的问题,在对企业信用风险评估的指标体系进行梳理和完善的基础上,利用最小二乘支持向量机(LSSVM)的基本原理尝试构建了企业信用风险 LSSVM 评估模型,并对模型的可行性和优越性做了分析和验证。研究结果表明：LSSVM 评估模型收敛速度快、准确度较高、泛化能力强,为供应链金融业务中的企业信用风险评估提供了一种新的办法和思路。     

An Analysis of Concepts and Evidence on the Question of Whether IFRS Should be Conservative

Do international accounting standards require conservative accounting? The IASB's conceptual framework suggests that they should not, while the research literature is largely silent on the matter, typically presuming conservatism to be an outcome of private contracting rather than standardized, public, general purpose financial reporting. In this paper, we analyze the actual requirements of IFRS. We find multiple examples of recognition requirements that lead to unconditional conservatism, measurement requirements that lead to conditional conservatism, and also presentation/disclosure requirements that further support a conservative reporting environment. These findings complement, support and deepen existing evidence in the empirical literature that accounting is in practice conservative. We show, however, that the requirements for conservatism in IFRS conflict with, first, the IASB's stated position in its conceptual framework that accounting should not be conservative and, second, the private contracting explanation for conservatism that is generally accepted in the literature. What is missing, and lies behind both conflicts, is an acknowledgement and understanding of the role of an agency/contracting perspective in enhancing the decision‐usefulness of general purpose accounting standards, given the information/incentive asymmetry and uncertainty that characterizes the real‐world context in which those standards operate. From a policy perspective, such an understanding would reconcile the IASB's conceptual framework with the actual requirements of IFRS. From a research literature perspective, such an understanding would re‐position accounting standards as central to the practice of accounting conservatism, which would in turn require revision to the generally accepted theory of a private contracting explanation for the empirical evidence of conservative accounting practice.     

我国民营企业贷款的距离影响因素分析

选取我国2011年沪深两市 A股的628家民营上市企业的贷款数据为样本进行实证分析发现：银行贷款量与银行到企业之间距离具有显著的负相关关系,说明银企距离是制约我国民营企业贷款的一个重要影响因素;地区金融发展与企业的银行贷款量之间具有显著的负相关的关系,说明了竞争关系对企业外地银行贷款的抑制作用不是表现在区域的银行网点的竞争,而是地区的金融发展。因此,应重视银企距离,加大地区金融发展。     

Hierarchical Bayesian conjoint models incorporating measurement uncertainty

The authors explore situations where consumers supplement their judgments with a measurement of uncertainty about their own preferences, either implicitly or explicitly, and develop two sets of hierarchical Bayesian conjoint models incorporating such measurements. The first set of models uses the relative location of a rating to determine the importance or weight given to the rating, in a regression setting. The second set uses interval judgment as a dependent variable in a regression setting. After specifying the models, the authors perform a theoretical comparison with a basic Bayesian regression model. They show that, under different conditions, the proposed models will yield more precise individual-level partworth estimates. Two simulated data examples and data from a conjoint study are used to illustrate the gains that could be obtained from modeling uncertainty. In the empirical application, the authors show that model fit improves when ratings for items that respondents do not like are given more weight compared to ratings for items that they do like. Electronic Supplementary Material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.

中国外贸进出口对税收的影响：理论与实证

对外贸易对中国的税收有怎样的影响?回答这一问题，对制定贸易政策有重要的现实意义。本文对1-2-3模型进行修正，将总产出区分为4种具有不同税收特征的产品，构建相应的税收模型，从理论上分析了对外贸易对税收有正面的影响。实际测算显示，一般贸易出口每增加1元人民币，会相应带动当年税收增加0．4～0．5元人民币。通过对季度数据的Granger因果检验和向量误差修正模型分析，本文发现出口增加对税收有持续的正面影响。     

The FDI location decision: Distance and the effects of spatial dependence

We investigate how different conceptions of distance impact upon one of the fundamental decisions made by foreign investors, the choice of foreign direct investment (FDI) location within the selected host country. We argue that the attractiveness of host country locations to foreign investors depends not only upon location-specific attributes such as labor costs, but also upon the location's proximity to alternative locations. We provide theoretical rationales for how and why alternative concepts of distance might impact upon firms’ FDI location decisions, and explicitly model different measures of geographic, economic and administrative distance. Empirically we illustrate the use of a number of spatial regression models with a new dataset on FDI in Chinese prefecture-cities, and have shown, in this context, that geographic distance is not the ‘best’ measure of distance to use. We find clear evidence of spatial dependence between the cities based upon economic distance, with weaker evidence related to administrative distance. The distinctive contribution of this paper is to emphasize that city-level policy to attract FDI is more likely to succeed if the prefecture-city is economically (and administratively) close to alternative city locations, while any policy expenditure may fail to attract FDI inflows if the prefecture-city is distant from other city locations.     

How does competition affect bank systemic risk?

Using bank level measures of competition and co-dependence, we show a robust negative relationship between bank competition and systemic risk. Whereas much of the extant literature has focused on the relationship between competition and the absolute level of risk of individual banks, in this paper we examine the correlation in the risk taking behavior of banks. We find that greater competition encourages banks to take on more diversified risks, making the banking system less fragile to shocks. Examining the impact of the institutional and regulatory environment on bank systemic risk shows that banking systems are more fragile in countries with weak supervision and private monitoring, greater government ownership of banks, and with public policies that restrict competition. We also find that the negative effect of lack of competition can be mitigated by a strong institutional environment that allows for efficient public and private monitoring of financial institutions.     

Linear predictability vs. bull and bear market models in strategic asset allocation decisions: evidence from UK data

Most papers in the portfolio choice literature have examined linear predictability frameworks based on the idea that simple but flexible Vector Autoregressive (VAR) models can be expanded to produce portfolio allocations that hedge against the bull and bear dynamics typical of financial markets through careful selection of predictor variables that capture business cycles and market sentiment. Yet, a distinct literature exists that shows that non-linear econometric frameworks, such as Markov switching, are also natural tools to compute optimal portfolios arising from the existence of good and bad market states. This paper examines whether and how simple VARs can produce portfolio rules similar to those obtained under a simple Markov switching, by studying the effects of expanding both the order of the VAR and the number/selection of predictor variables included. In a typical stock-bond strategic asset allocation problem for UK data, we compute the out-of-sample certainty equivalent returns for a wide range of VARs and compare these measures of performance with those of non-linear models. We conclude that most VARs cannot produce portfolio rules, hedging demands or (net of transaction costs) out-of-sample performances that approximate those obtained from simple non-linear frameworks.