A two-step approach to account for unobserved spatial heterogeneity

A two-step approach to account for unobserved spatial heterogeneity. Spatial Economic Analysis. Empirical analysis in economics often faces the difficulty that the data are correlated and heterogeneous in some unknown form. Spatial econometric models have been widely used to account for dependence structures, but the problem of directly dealing with unobserved spatial heterogeneity has been largely unexplored. The problem can be serious particularly if we have no prior information justified by economic theory. In this paper we propose a two-step procedure to identify endogenously spatial regimes in the first step and to account for spatial dependence in the second step. This procedure is applied to hedonic house price analysis.     

Contagion between real estate and financial markets: A Bayesian quantile-on-quantile approach

We study contagion between Real Estate Investment Trusts (REITs) and the equity market in the U.S. over four sub-samples covering January, 2003 to December, 2017, by using Bayesian nonparametric quantile-on-quantile (QQ) regressions with heteroskedasticity. We find that the spillovers from the REITs on to the equity market has varied over time and quantiles defining the states of these two markets across the four sub-samples, thus providing evidence of shift-contagion. Further, contagion from REITs upon the stock market went up during the global financial crisis particularly, and also over the period corresponding to the European sovereign debt crisis, relative to the pre-crisis period. Our main findings are robust to alternative model specifications of the benchmark Bayesian QQ model, especially when we control for omitted variable bias using the heteroskedastic error structure. Our results have important implications for various agents in the economy namely, academics, investors and policymakers.     

Structural investigation of supply networks: A social network analysis approach

A system of interconnected buyers and suppliers is better modeled as a network than as a linear chain. In this paper we demonstrate how to use social network analysis to investigate the structural characteristics of supply networks. Our theoretical framework relates key social network analysis metrics to supply network constructs. We apply this framework to the three automotive supply networks reported in Choi and Hong (2002). Each of the supply networks is analyzed in terms of both materials flow and contractual relationships. We compare the social network analysis results with the case-based interpretations in Choi and Hong (2002) and conclude that our framework can both supplement and complement case-based analysis of supply networks.     

The employment effects of the minimum wage: A selection ratio approach to measuring treatment effects

I develop a method for measuring a binary treatment's effects when more than one control group is available but each control group is imperfect. To study effects of the minimum wage, for example, I compare border counties where the minimum wage increases to the set of neighboring counties, the set of neighbor-of-neighboring counties, and so forth. The key innovation is to model the ratio of the biases present in these comparisons. Crucially, the model contains a testable implication. The method can address spillovers and, in another application, is used to measure treatment effects away from the discontinuity in a regression discontinuity design.     

Closest targets and minimum distance to the Pareto-efficient frontier in DEA

In this paper, we propose a general approach to find the closest targets for a given unit according to a previously specified criterion of similarity. The idea behind this approach is that closer targets determine less demanding levels of operation for the inputs and outputs of the inefficient units to perform efficiently. Similarity can be interpreted as closeness between the inputs and outputs of the assessed unit and the proposed targets, and this closeness can be measured by using either different distance functions or different efficiency measures. Depending on how closeness is measured, we develop several mathematical programming problems that can be easily solved and guarantee to reach the closest projection point on the Pareto-efficient frontier. Thus, our approach leads to the closest targets by means of a single-stage procedure, which is easier to handle than those based on algorithms aimed at identifying all the facets of the efficient frontier.

全球金融风暴下我国中小企业融资出路探讨

在全球金融风暴的经济背景下,我国中小企业的融资更加困难,已经严重的威胁到中小企业的生存。文章首先概述了我国中小企业融资的现状,然后从政府政策、我国金融服务体系结构、商业银行惜贷等四个方面分析了制约我国中小企业融资的原因,最后从政府政策的扶持、金融服务体系的创新、商业银行信贷创新等方面探讨解决我国中小企业融资难的途径。     

Identifying credit demand,financial intermediation,and supply of funds shocks: A structural VAR approach

In this paper, we identify three exogenous shocks to credit market: demand for credit, supply of funds into the financial system, and the willingness to lend of financial institutions (financial intermediation), and also, determine the contribution of these shocks to fluctuations in the credit market and overall economic activity. We estimate a structural vector autoregression model where the three credit shocks are identified with a set of sign restrictions motivated by a simple partial equilibrium model of financial intermediation. We find that the credit demand shock explains significantly the variations in the long-term loan rate proxied by the Moody’s Baa corporate bond yield, while the supply of funds shock contributes to most of the fluctuations in the short-term commercial paper rate. The financial intermediation shock drives most of the fluctuations in the quantity of loans as well as the spread between the Baa and commercial paper rates. Of the credit shocks, we find that the financial intermediation shock has the largest impact on real economic activity. In fact, our analysis implies that the sharp decline in output during the 2007–2009 financial crisis is largely attributable to the financial intermediation shock, along with shocks originating outside of the financial system.     

Penalty and related estimation strategies in the spatial error model

Spatial autoregressive models are powerful tools in the analysis of data sets from diverse scientific areas of research such as econometrics, plant species richness, cancer mortality rates, image processing, analysis of the functional Magnetic Resonance Imaging (fMRI) data, and many more. An important class in the host of spatial autoregressive models is the class of spatial error models in which spatially lagged error terms are assumed. In this paper, we propose efficient shrinkage and penalty estimators for the regression coefficients of the spatial error model. We carry out asymptotic as well as simulation analyses to illustrate the gain in efficiency achieved by these new estimators. Furthermore, we apply the new methodology to housing prices data and provide a bootstrap approach to compute prediction errors of the new estimators.     

Contagion risk in the Czech financial system: A network analysis and simulation approach

This paper examines the potential for contagion within the Czech banking system via the channel of interbank exposures of domestic banks, enriched by a liquidity channel and an asset price channel, over the period March 2007 to June 2012. A computational model is used to assess the resilience of the Czech banking system to interbank contagion, taking into account the size and structure of interbank exposures as well as balance sheet and regulatory characteristics of individual banks in the network. The simulation results suggest that the potential for contagion due to credit losses on interbank exposures was rather limited. Even after the introduction of a liquidity condition into the simulations, the average contagion was below 3.8% of the remaining banking sector assets, with the exception of the period from December 2007 to September 2008. Activation of the asset price channel further increases the losses due to interbank contagion, showing that the liquidity of government bonds would be essential for the stability of Czech banks in stress situations. Finally, the simulation results for both idiosyncratic and multiple bank failure shocks suggest that the potential for contagion in the Czech banking system has decreased since the onset of the global financial crisis.     

The codispersion map: a graphical tool to visualize the association between two spatial variables

The codispersion coefficient quantifies the association between two spatial processes for a particular direction (spatial lag) on a two‐dimensional space. When this coefficient is computed for many directions, it is useful to display those values on a single graph. In this article, we suggest a graphical tool called a codispersion map to visualize the spatial correlation between two sequences on a plane. We describe how to construct a codispersion map for regular and non‐regular lattices, providing algorithms in both cases. Three numerical examples are given to illustrate how useful this map can be to detect those directions for which the codispersion coefficient attains its maximum and minimum values. We also provide the R code to construct the codispersion map in practice.     

中国三大城市群金融集聚:空间网络及结构分化

金融是国家重要的核心竞争力,金融集聚是推动城市群崛起的重要驱动力。本文基于因子分析和引力模型,研究了2007~2017年京津冀、长三角和珠三角三大城市群的金融集聚及其空间网络。结果发现:从整体来看,三大城市群的金融集聚网络逐步从分割走向融合,且上海在整个金融网络中居于核心位置;从局部来看,城市群内部金融集聚网络的结构差异明显,京津冀表现为主次型单一结构,北京位于绝对核心;长三角为多中心均衡结构,且江苏的金融中心程度明显高于浙江;珠三角为穗深双核结构,深圳的上升趋势明显。据此,应基于城市群金融集聚网络的不同特征,差异化地整合和优化金融资源配置,为区域一体化和经济高质量发展提供高质量的金融服务。     

The impact of market structure on environmental efficiency in the United States: A quantile approach

This paper investigates the relationship between efficiency and market structure for a sample of industrial facilities dispersed among the U.S. states. In order to measure the relevant efficiency scores, we use a data envelopment analysis allowing for the inclusion of desirable and undesirable (toxic chemical releases) outputs in the production function. In the next stage, we utilize the bootstrapped quantile regression methodology to uncover possible nonlinear relationships between efficiency and competition at the mean and at various quantiles before and after the global financial crisis (2002 and 2012). In this way, we impose no functional form constraints on parameter values over the conditional distribution of the dependent variable (efficiency). At the same time, we estimate at which part of its cumulative distribution function the efficiency is located and draw substantial conclusions about the range of policy measures obtained. The empirical findings indicate that the relationship between efficiency and market concentration did change in the aftermath of the global financial crisis. The empirical results survived robustness checks under the inclusion of an alternative market concentration indicator (CR8).     

The recurrence of financial distress: A survival analysis

Companies often suffer periods of financial distress before filing for bankruptcy. Unlike one-off bankruptcies, financial distress can occur repeatedly within the same individual firm. This paper is focused on the recurrence of financial distress and studies the Chinese stock market, where Special Treatment – an official indicator of financial distress – can be repeatedly applied to a listed company. We employ a stratified hazard model to predict the probability of subsequent distress with variables, including duration dependency, event-based factors, institutional variables, financial ratios, market-based variables and macroeconomic conditions. Our empirical results show that accounting and market-based variables have limited power in predicting the recurrence of distress, whereas the duration of recovery, restructuring events and their interaction terms with the accounting and macroeconomic factors affect the recurrent risk significantly. Tested on out-of-time samples, our proposed hazard models show a robust performance in the prediction of recurrent risk over time.     

Oil price shocks and credit spread: Structural effect and dynamic spillover

This study analyzes the heterogeneous response of U.S. credit spread to global oil price shocks by building an extended structural vector autoregressive model (SVAR), which can distinguish among the U.S. and non-US oil supply shocks, aggregated demand shocks and oil market-specific demand shocks behind the real oil prices. Meanwhile, a spillover index model developed by Diebold and Yilmaz (2012) (hereafter D.Y. (2012)) is used to estimate the link between oil price shocks and the U.S. credit spread over time. The results show that (i) the credit spread does not respond to global oil supply shocks and non-US oil supply shocks, but has a negative reaction to the U.S. oil supply shocks, aggregate demand shocks, and oil-market-specific demand shocks. (ii) There exists a close connectedness between oil price shocks and the U.S. credit spread, and the link fluctuates cyclically and relates to the economic cycle and the U.S. shale oil revolution. (iii) The spillover from different oil price shocks to the U.S. credit spread shows significant heterogeneity over time. Our findings suggest that policymakers and investors can better track the U.S. credit spread changes using oil price information.     

Penalized estimation of panel vector autoregressive models: A panel LASSO approach

This paper proposes LASSO estimation specific for panel vector autoregressive (PVAR) models. The penalty term allows for shrinkage for different lags, for shrinkage towards homogeneous coefficients across panel units, for penalization of lags of variables belonging to another cross-sectional unit, and for varying penalization across equations. The penalty parameters therefore build on time series and cross-sectional properties that are commonly found in PVAR models. Simulation results point towards advantages of using the proposed LASSO for PVAR models over ordinary least squares in terms of forecast accuracy. An empirical forecasting application including 20 countries supports these findings.     

The transmission of international shocks to CIS economies: A global VAR approach

This paper employs a Global Vector Auto Regressive (GVAR) model to study the evolution of the response of the Commonwealth of Independent States (CIS) to foreign output and oil price shocks. During an observation period of two decades, cross-country trade and financial linkages experience notable changes. We find CIS countries to be highly sensitive to global and regional shocks, with that sensitivity increasing after the global financial crisis. CIS countries show the strongest responses to output shocks originating in the US, Russia and within the region itself, but their sensitivity to euro area shocks also increases substantially. Despite growing trade relations with China, the responses of CIS countries to output shocks originating in China are still relatively moderate.     

Pricing used books on Amazon.com: a spatial approach to price dispersion

This paper estimates a spatial autoregressive (SAR) model of price dispersion using publicly available internet bookselling data. It uses a semiparametric adaptive estimator that does not require the usual Gaussian assumption of maximum likelihood (ML) estimators. The results suggest that both price competition and seller heterogeneity are key drivers of the observed price dispersion. The paper finds that sellers with large sales volume, newly established sellers and US mainland states-based sellers tend to price lower. The identified significant spatial interaction is evidence of spatial price competition. Controlling for everything else, a seller asks a lower price when large sellers charge relatively high prices, which is also evidence of price-based selling and undercutting.     

The interplay between the Bayesian and frequentist approaches: a general nesting spatial panel data model

The interplay between the Bayesian and Frequentist approaches: a general nesting spatial panel-data model. Spatial Economic Analysis. An econometric framework mixing the Frequentist and Bayesian approaches is proposed in order to estimate a general nesting spatial model. First, it avoids specific dependency structures between unobserved heterogeneity and regressors, which improves mixing properties of Markov chain Monte Carlo (MCMC) procedures in the presence of unobserved heterogeneity. Second, it allows model selection based on a strong statistical framework, characteristics that are not easily introduced using a Frequentist approach. We perform some simulation exercises, finding good performance of the properties of our approach, and apply the methodology to analyse the relation between productivity and public investment in the United States.     

Knowledge flow and inter-firm networks: The influence of network resources,spatial proximity and firm size

The objective of this paper is to analyse the characteristics and nature of the networks that firms utilize to access knowledge and facilitate innovation. The paper draws on the notion of network resources, distinguishing two types: social capital–consisting of the social relations and networks held by individuals; and network capital–consisting of the strategic and calculative relations and networks held by firms. The methodological approach consists of a quantitative analysis of data from a survey of firms operating in knowledge-intensive sectors of activity. The key findings include: social capital investment is more prevalent among firms frequently interacting with actors from within their own region; social capital investment is related to the size of firms; firm size plays a role in knowledge network patterns; and network dynamism is an important source of innovation. Overall, firms investing more in the development of their inter-firm and other external knowledge networks enjoy higher levels of innovation. It is suggested that an over-reliance on social capital forms of network resource investment may hinder the capability of firms to manage their knowledge networks. It is concluded that the link between a dynamic inter-firm network environment and innovation provides an alternative thesis to that advocating the advantage of network stability.     

Cultural distance and international trade in services: A disaggregate view

In this paper, we estimate the effect of “cultural distance” on bilateral trade in services. The measure of cultural distance we use is based on scores that reflect country averages of individuals’ attitudes towards inequality, self-orientation, competition, uncertainty, traditions, and indulgence. Controlling for standard ingredients of gravity equations, we show that an aggregate measure of cultural distance has a significantly negative effect on total bilateral services trade. Once we take a more disaggregate view, we find that the strength of this effect differs across various types of services and various aspects of cultural distance.