Gain or pain? New evidence on mixed syndication between governmental and private venture capital firms in China

I investigate the performance of mixed syndication involving both governmental and private venture capital firms (GVCs and PVCs) in the context of China. Using the data on the investments in start-ups between 1995 and 2011, I find that start-ups backed by mixed syndication in their initial financing round are less likely to survive to the next round to obtain refinancing, compared to those backed by syndication solely among PVCs. I present evidence consistent with two possible explanations of the underperformance of firms backed by mixed syndication: a potentially lower criterion of selecting portfolio companies when led by PVCs and less complementary resources but higher coordination cost in mixed syndication. The empirical results continue to hold when using instrumental variables, propensity score matching analysis and the quasi-maximum likelihood estimation method for mitigating the potential selection bias and endogeneity problems.     

Financial densities in emerging markets: an application of the multivariate ES density

This paper derives and presents the multivariate Edgeworth–Sargan (ES) density, discusses some of its properties, and estimates it for three exchange rates in emerging markets (Chile, Hungary and Singapore). The ES density fits the data adequately, and the model is estimated simultaneously for all variables. This involves estimating a highly non-linear model with 32 parameters. A multivariate Student's t is also estimated, and both sets of results are compared. The empirical results show that, (a) the ES density applies to emerging markets as well as to more developed economies, as shown in previous research, (b) it is feasible to estimate a multivariate density of large dimensionality, and (c) independent estimation of the marginal densities, although a consistent procedure, yields significantly different results from the multivariate estimation for some parameters.     

Influences on Pricing and Markup in Segmented Manufacturing Markets*

The literature indicates that international manufactured product markets are segmented and imperfectly competitive. An industry pricing equation that encompasses markup pricing is constructed from flexible specifications of the firm's cost and demand functions, along with the profit-maximizing equilibrium condition. Instrumental variables estimation is carried out for 24 three-digit International Standard Industrial Classification industries, with data pooled across Germany, Japan, the United Kingdom and the United States, for the period 1970–1991. The markup model is rejected for most industries. However, something approaching a fixed markup does exist in many industries, particularly when the Herfindahl index is low. The results indicate that countries with high levels of industry concentration are more likely to have prices and markups that are influenced by competing foreign prices. Nearly all markups are either pro-cyclical or a-cyclical.     

Credit Support for Export: Robust Evidence from the Czech Republic

The topic of this paper is quite a novel one – it is one of few empirical academic papers dealing with export credit. Moreover, it is the first analysis of this kind which focuses on transition economies. The paper deals with export credit promotion in the Czech Republic. The development and structure of Czech trade and export support is presented first, followed by an econometric analysis of the gravity model of Czech Republic trade. A panel of 160 countries in 1996–2008 is analysed and two gravity models of exports for the Czech Republic are estimated, the static model by fixed effects (LSDV estimator) and the dynamic model by System GMM. Due to ambiguous conclusions we assume that the behaviour of our explanatory variables is not uniform and our data set behaves as a mixture of countries with heterogeneous behaviour. This means that traditional techniques of estimation which include all observations into one model do not give significant results. Thus, we use robust techniques of estimation that solve the problem of heterogeneous patterns in data sets. Out of several possibilities we use the Least Trimmed Squares estimator (LTS) with a leverage point. We show that guarantees are a significant factor that influences positively the volume of exports in the Czech Republic. Moreover, there exist more variables that affect the size of exports in the Czech Republic. Market forces described by GDP, distance, political risk or gross fix capital formation are significant in our econometric model. We find that higher GDP, shorter distance or lower political risk have a positive impact on Czech exports.     

Bayesian estimation of a simultaneous probit model using error augmentation: An application to multi-buying and churning behavior

Researchers in marketing are often interested in analyzing how an agent’s discrete choice decision affects a subsequent or concurrent discrete choice decision by the same or different agent. This analysis may necessitate the use of a simultaneous equations model with discrete and continuous endogenous variables as explanatory variables. In this paper, we offer an error augmentation approach to Hierarchical Bayesian estimation of a simultaneous bivariate probit model containing both discrete and continuous endogenous variables. We accomplish the error augmentation in our MCMC algorithm using a Metropolis-Hastings step that generates the error components of the latent variables in our model. Using simulated data, we demonstrate that our error augmentation algorithm recovers closely the true parameters of the simultaneous bivariate probit model. We then apply our algorithm to customer churn data from a wireless service provider. We formulate a simultaneous bivariate probit model to study the impact of a customer’s multiple product relationships with a firm (multi-buying) on the likelihood of churn by that customer. The empirical results show that the act of multi-buying significantly reduces churn even though the customers who are more predisposed to multi-buy have an inherently higher predisposition to churn.     

Bayesian Analysis of Simultaneous Demand and Supply

In models of demand and supply, consumer price sensitivity affects both the sales of a good through price, and the price that is set by producers and retailers. The relationship between the dependent variables (e.g., demand and price) and the common parameters (e.g., price sensitivity) is typically non-linear, especially when heterogeneity is present. In this paper, we develop a Bayesian method to address the computational challenge of estimating simultaneous demand and supply models that can be applied to both the analysis of household panel data and aggregated demand data. The method is developed within the context of a heterogeneous discrete choice model coupled with pricing equations derived from either specific competitive structures, or linear equations of the kind used in instrumental variable estimation, and applied to a scanner panel dataset of light beer purchases. Our analysis indicates that incorporating heterogeneity into the demand model all but eliminates the bias in the price parameter due to the endogeneity of price. The analysis also supports the use of a full information analysis.     

The impact of civil war on foreign direct investment flows to developing countries

We investigate the impact of civil war on foreign direct investment (FDI) flows to developing countries. We employ a new data-set that disaggregates FDI inflows to primary, secondary and tertiary sectors. Second, we control for a richer set of economic and institutional variables that could determine FDI inflows including population, gross domestic product (GDP) per capita, the degree of trade openness, exchange rate variability, inflation, the governance structure of the host country using International Country Risk Guide data and its regime type using the POLITY autocracy–democracy data. We also address the reverse causality between FDI and conflict and the potential endogeneity of explanatory variables by employing dynamic system generalised method of moments (GMM) techniques in estimation. Our results indicate that primary sector FDI flows to developing countries are not significantly affected by civil war, whereas secondary and tertiary sectors FDI are more sensitive to such outbreak, potentially leading to reversals of existing FDI. Among institutional variables, government stability and control of corruption are more significant compared to regime type, law and order, and bureaucratic quality.     

Re-examining the Determinants of Non-Performing Loans in Ghana’s Banking Industry: Role of the 2007–2009 Financial Crisis

This paper uses robust econometric methods to estimate the determinants of non-performing loans (NPLs) with a specific focus on the role of the 2007–2009 financial crisis in explaining NPLs in the banking industry of Ghana. Findings suggest that non-performing loans are significantly affected by bank-specific, industry, and macroeconomic variables. We observed heterogeneity in the determinants of NPLs for sub-samples of the data. The effect of the financial crisis on NPLs is observed to be conditional on the level of credit risk in our sub-sample analysis. The results from the impulse response corroborate that of the regression estimation.     

Bias in consumer panel and store audit data

Sample survey data such as consumer panel data and store audit data may deviate remarkably from each other and from internal secondary data. These deviations are called data biases. In this study we use information regarding some brands of a fast moving nondurable consumer good to show that the data bias may lead to a large bias in parameter estimation, implying wrong indications for the use of marketing decision variables.A formal analysis explaining the coverage factor - measuring data bias - by a number of exogeneous variables can lead to a reduction in the case of consumer panel data.A similar reduction of the data bias in store audit data can be reached through the use of a reweighing procedure.     

A SIMULTANEOUS EQUATIONS APPROACH TO MODELING INDUSTRY STRUCTURE AND ECONOMIC PERFORMANCE

This paper presents a simultaneous equations model of profitability, concentration, advertising expenditures, and research and developed outiays. The data are subjected to regression diagnostics in an effort to ascertain the importance to the estimation of a subset of the data than can have a disproportional influence. The estimation results, especially for the advertising expenditures relationship and the research and development outlays equation, do change when a truncated data sample (based on the omission of the outliers) is used. This serves as a partial explanation of why there is disagreement in the literature on market structure and economic performance.     

Analysing the effect of environmental factors on service performance

The aim of this paper is to analyse the effect of some environmental variables when analysing efficiency in a service process. For the first time in the grocery retail industry, this paper considers two different approaches employed in the efficiency measurement literature to analyse the influence of environmental factors, and compares the results obtained with a model that does not include the environmental factors. The methodology is based on the estimation of a stochastic parametric function. The results obtained highlight the importance of considering environmental variables when evaluating efficiency in retailing. As a consequence, managers should be aware that differences in performance could be due to more or less favourable environmental conditions. It is important for service assessment programmes to provide precise enough measures for managers to draw the correct conclusion when evaluating their firms' performance.     

The Misuse of Measurement in Economics

This paper is concerned with the problems that arise in economic analysis when ordinal variables are taken to be cardinal variables in models that require variables to be cardinally quantified for manipulative purposes. The main conclusion is that, in many important cases, this practice results in constructions that are without meaning, significance and power. Such a result applies, for example, to the efficiency wage model if effort, as many believe, is capable of only ordinal measurement. Similar problems arise (i) with the use of proxy variables, and (ii) even when prediction is the only goal.     

较差质量数据下的返回弹道计算及对落点影响的分析

针对某老式卫星返回时雷达跟踪数据曾发生的测量异常及质量较差等情况，为解决此类数据在返回弹道及落点计算中出现的实际困难以及给出应对措施，基于工程实际情况给出了三种不同建模类型下的弹道计算方法——当前统计解耦扩展卡尔曼滤波（EKF）、动力学建模无迹卡尔曼滤波（UKF）、多项式滑窗最小二乘估计算法，并应用该次质量较差的实际观测数据进行了算法应用效果分析与落点计算情况对比，对计算中的经验及教训进行了总结。研究结果表明，所给出的三种算法在面对较差质量数据时性能差异较大，在工程实际应用中可考虑将多种算法互为参考。研究结果及其经验教训对卫星返回弹道及落点的实时估计的工程实施有一定的参考价值。     

采用数据辅助的大规模MIMO信道半盲迭代估计

由于大规模多输入多输出(Multiple-Input Multiple-Output,MIMO)信道衰落参数的维度较高,导致最优估计算法计算量大且需要的导频数较多而影响到频谱效率。为降低计算复杂度并减少导频开销,提出了两种基于期望最大化(Expectation Maximization,EM)估计的半盲迭代改进算法。利用少量正交导频序列估计出信道初值,通过用户与基站间信道的大尺度衰落系数把用户分簇,根据这些系数按比例地分配接收噪声,再利用数据的统计特性推导出信道衰落参数的均值和方差。仿真结果表明,当导频数远少于待估计参数的个数时,半盲估计算法的均方误差(Mean Square Error,MSE)优于导频估计的极大似然(Maximum likelihood,ML)算法。     

Multi level categorical data fusion using partially fused data

Data fusion poses challenging methodological issues for inferring the joint distribution of two random variables when the information available is mainly confined to the marginal distributions. When the variables are categorical, the challenges are even more severe. Applications of categorical data fusion are of top importance in marketing, especially in advertising. A great deal of categorical data fusion methods are confined to binary variables. In this paper we develop an innovative approach to categorical data fusion that extends previous methodologies and applies to categorical variables with any number of levels. We introduce a new concept of “evident dependence” that describes a variety of patterns of joint distributions given the marginals. Using information from partially fused data, our method smoothly accommodates a Bayesian approach based on mixtures of joint distributions constructed using evident dependence. The approach is illustrated using data from the advertising industry.     

利用频谱排序和筛选的突变噪声快速估计

针对最小值控制递归平均(Minima Controlled Recursive Averaging,MCRA)算法不能快速跟踪突变噪声的问题，提出了一种基于频谱排序和筛选的突变噪声快速估计方法。该方法在MCRA算法的基础上对带噪语音的功率谱进行排序，筛选出不含语音信号的频点来估计噪声的平均功率谱；当检测到噪声突变时，对当前的平滑参数和状态变量进行校正。仿真结果表明，该方法可以将突变噪声的跟踪时间缩短90%以上；用于语音降噪处理时，音质可以提升约0.4分。该方法具有一定的工程应用价值。     

Assessing new empirical industrial organization (NEIO) methods: The cases of five industries

The methods proposed in the new empirical industrial organization (NEIO) literature have made significant contributions to our understanding of competitive behavior. However, these methods have yet to be compared with each other for their performance in explaining and diagnosing competitive market conduct. This inter-method comparison is important because conclusions about competitive behavior based on these methods have significant strategic as well as policy implications for firms. Our objective in this paper is to examine the performance of these different NEIO methods in terms of their discriminatory power, ability to identify strategic variables, and robustness in estimation. For empirical demonstration, we use data from diverse industries such as microprocessors, personal computers, facial tissue, disposable diapers and automobiles. Our results suggest that two commonly used NEIO methods-conjectural variation and non-nested model comparison-exhibit quite good convergence with each other and are consistent with a traditional time series method. This suggests that simpler methods such as conjectural variations deserve more credit. We also find that using these methods in tandem provides valuable additional information that may not be available when using any one method alone. While the emphasis in this study is on comparing different methods of analyzing competitive interaction, the findings also reveal some substantive insights about each market studied.     

Estimation of traffic accident costs: a prompted model

Traffic accidents are the reason for 25% of unnatural deaths in Iran. The main objective of this study is to find a simple model for the estimation of economic costs especially in Islamic countries (like Iran) in a straightforward manner. The model can show the magnitude of traffic accident costs with monetary equivalent. Data were collected from different sources that included traffic police records, insurance companies and hospitals. The conceptual framework, in our study, was based on the method of Ayati. He used this method for the estimation of economic costs in Iran. We promoted his method via minimum variables. Our final model has only three available variables which can be taken from insurance companies and police records. The running model showed that the traffic accident costs were US$2.2 million in 2007 for our case study route.     

Monte Carlo analysis of methods for extracting risk-neutral densities with affine jump diffusions

This article compares several widely used and recently developed methods to extract risk-neutral densities (RNDs) from option prices in terms of estimation accuracy. It shows that the positive convolution approximation method consistently yields the most accurate RND estimates, and is insensitive to the discreteness of option prices. RND methods are less likely to produce accurate RND estimates when the underlying process incorporates jumps and when estimations are performed on sparse data, especially for short time-to-maturities, though sensitivity to the discreteness of the data differs across different methods.     

Integrating partitioned prices via computational estimation

This paper introduces computational estimation to the literature on consumers’ numerical cognition. Computational estimation involves simplifying an arithmetic problem via mathematical procedures to produce an approximate answer. Employing calculation knowledge and approximation together, consumers are likely to use computational estimation as it is relatively accurate while saving cognitive effort compared to calculating values. Three studies applied to partitioned prices in the form of a base price and a percentage discount, demonstrate that when faced with this numeric integration task, the strategy consumers undertake is dependent on the characteristics of the numerals with discounts that are round or close to round being associated with greater use of computational estimation. Further, when employing computational estimation, consumers arrive at more accurate, and lower, price estimates in which they place more confidence than when using alternative an integration strategy. As a result, discounts that are near a round value are preferred to those that are not; a result that is dependent upon the use of computational estimation.