How do changes in monetary policy affect bank lending? An analysis of Austrian bank data

Using a panel of Austrian bank data we show that the lending decisions of the smallest banks are more sensitive to interest rate changes, and that for all banks, sensitivity changes over time. We propose to estimate the groups of banks that display similar lending reactions by means of a group indicator which, after estimation, indicates each bank's classification. Additionally, we estimate a state indicator that indicates the periods during which the lending reaction differs from what we normally observe. Bayesian methods are used for estimation; a sensitivity analysis and a forecast evaluation confirm our model choice. Copyright © 2006 John Wiley & Sons, Ltd.     

Modeling Item Nonresponse in Questionnaires

The statistical analysis of empirical questionnaire data can be hampered by the fact that not all questions are answered by all individuals. In this paper we propose a simple practical method to deal with such item nonresponse in case of ordinal questionnaire data, where we assume that item nonresponse is caused by an incomplete set of answers between which the individuals are supposed to choose. Our statistical method is based on extending the ordinal regression model with an additional category for nonresponse, and on investigating whether this extended model describes and forecasts the data well. We illustrate our approach for two questions from a questionnaire held amongst a sample of clients of a financial investment company.     

Heterogeneous Traders,the Leverage Effect and Volatility of the Chinese P2P Market

The new financial industry represented by peer-to-peer lending has gradually become a new source of volatility due to the increasing complexity of the Chinese financial market. This volatility leads to greater risk to P2P investors and has become the focus of the regulatory authorities in China. Based on the background data of the P2P platform, Honglingchuangtou, we use the factor analysis method to construct a platform volatility (PV) index and we construct an HAR model to study the heterogeneous traders and leverage effect in the Chinese P2P market. The empirical results show that there are both short-term and long-term heterogeneous traders in the Chinese P2P market and that long-term traders have the greatest impact on market volatility. Similar to traditional financial markets, the volatility of the P2P market also shows a leverage effect, which means that the negative volatility of trader actions should have a negative impact on market fluctuations. With regard to the leverage effect, the LHAR-PV model is superior because of a higher goodness of fit and a lower prediction error.     

中小微企业的信贷决策

P2P网络借贷在为个人和中小企业投融资提供便利的同时,也带来了一系列的问题,不利于行业的稳定发展,因此,对各中小企业的风险评估特别重要。论文选取若干个风险指标,对123家中小企业进行数据调研,计算各风险指标因素的风险值,利用理想算法确定各指标因素的权重提出风险防范的建议。     

Foreign bank lending during a crisis: The impact of financial regulations

This study examines foreign bank lending during crises by using data on 1,558 individual banks in Asian and Latin American countries during the period 1987-2013. Our results reveal that, in a crisis period, Asian banks with a higher level of foreign ownership tend to reduce their lending. Nevertheless, during crises banks consistently increase their lending in order to support their borrowers; in fact, in Latin America, crises stimulate foreign banks to lend more. Our evidence on lending during a crisis supports credit rationing theories with a flight to quality. The international substitution effect also holds based on our results. Taking financial structures and regulation into consideration, for banks with more foreign ownership in a highly concentrated financial system in Asia, the crisis has less effect on a cut in lending, while it has a greater effect on cuts in lending for countries with a higher level of government-owned assets. This paper contributes to the existing literature on the bank lending channel and provides implications for policymakers.     

The GMLE based Buckley–James estimator with modified case–cohort data

We consider the estimation problem under the linear regression model with the modified case–cohort design. The extensions of the Buckley–James estimator (BJE) under the case–cohort designs have been studied under an additional assumption that the censoring variable and the covariate are independent. If this assumption is violated, as is the case in a typical real data set in the literature, our simulation results suggest that those extensions are not consistent and we propose a new extension. Our estimator is based on the generalized maximum likelihood estimator (GMLE) of the underlying distributions. We propose a self-consistent algorithm, which is quite different from the one for multivariate interval-censored data. We also show that under certain regularity conditions, the GMLE and the BJE are consistent and asymptotically normally distributed. Some simulation results are presented. The BJE is also applied to the real data set in the literature.     

Monitoring structural change in dynamic econometric models

The classical approach to testing for structural change employs retrospective tests using a historical data set of a given length. Here we consider a wide array of fluctuation‐type tests in a monitoring situation—given a history period for which a regression relationship is known to be stable, we test whether incoming data are consistent with the previously established relationship. Procedures based on estimates of the regression coefficients are extended in three directions: we introduce (a) procedures based on OLS residuals, (b) rescaled statistics and (c) alternative asymptotic boundaries. Compared to the existing tests our extensions offer ease of computation, improved size in finite samples for dynamic models and better power against certain alternatives, respectively. We apply our methods to three data sets, German M1 money demand, US labour productivity and S&P 500 stock returns. Copyright © 2005 John Wiley & Sons, Ltd.     

P2P网贷平台的发展与建议

P2P网贷作为一种新兴借贷方式,其便捷的交易方式和较低的投融资门槛,为融资人和投资人带来了极大的便利。然而由于行业发展迅猛,监管反应滞后,以及平台天然的准入门槛低,融资人信用普遍偏低等原因,P2P行业近几年来暴雷现象频发,大量P2P平台企业出清。论文从我国P2P行业的发展历程入手,研究了平台企业违约现象的产生原因,结合政府出台的管理措施,提出相关建议,希望这一行业能够持续良性发展,为实体经济注入活力。     

A Non-Iterative Bayesian Approach to Statistical Matching

Data fusion or statistical matching techniques merge datasets from different survey samples to achieve a complete but artificial data file which contains all variables of interest. The merging of datasets is usually done on the basis of variables common to all files, but traditional methods implicitly assume conditional independence between the variables never jointly observed given the common variables. Therefore we suggest using model based approaches tackling the data fusion task by more flexible procedures. By means of suitable multiple imputation techniques, the identification problem which is inherent in statistical matching is reflected. Here a non-iterative Bayesian version of Rubin's implicit regression model is presented and compared in a simulation study with imputations from a data augmentation algorithm as well as an iterative approach using chained equations.     

A study of the family service expenditures and the socio-demographic characteristics via fixed marginals correspondence analysis

Our study focuses on two data set, the former provides the expenditures for several services for each family and the latter contains socio-demographic variables for the same statistical units. The main aim is to analyze, in a Correspondence Analysis context, the service expenditure of families based on the whole given data-set under two types of constraints: the global relative expenses for a given service and the global relative expenses for a given socio-demographic category. The purpose of measuring the relationship between expenditure on social services and the socio-demographic characteristics of families is conducted in an exploratory and predictive perspective. A new approach is then introduced which ensures compliance with the required constraints. Moreover, through a procedure, we have obtained a table of regression coefficients. This table shows interesting properties and it is easy to interpret. Finally, the performance of the results has been evaluated using computer-based resampling techniques.     

科学划分我国城市的职能类型 建立分类指导的扩大内需政策

基于我国第五次人口普查和第二次基本单位普查数据(企业级),综合采用聚类分析和判别分析等方法,科学划分了我国649个县级以上城市的职能类型,将其划分为3个大类、15个亚类和37个职能组。建议下一步以分类结果为参考,以不同职能组城市为单元建立扩大内需投资效果评价和信息反馈机制,重点以大区级和省区级综合性城市为扩大内需着力点,积极发展城市群,促进我国宏观经济平稳启动。     

Spillover effects of subprime mortgage originations: The effects of single-family mortgage credit expansion on the multifamily rental market

The dramatic expansion in subprime mortgage credit fueled a remarkable boom and bust in the US housing market and created a global financial crisis. Even though considerable research examines the housing and mortgage markets during the previous decade, how the expansion in mortgage credit affected the rental market remains unclear; and yet, over 30 percent of all U.S. households reside in the rental market. Our study fills this gap by showing how the multifamily rental market was adversely affected by the development of subprime lending in the single-family market before the advent of the 2007/2008 subprime induced financial crisis. We provide evidence for a fundamentals based linkage by which the effect of an innovation in one market (i.e, the growth in subprime mortgage originations) is propagated through to another market. Using a large database of residential rental lease payment records, our results confirm that the expansion in subprime lending corresponds with an overall decline in the quality of rental payments. Finally, we present evidence showing that the financial performance of multifamily rental properties reflected the increase in rental lease defaults.     

How corruption affects bank lending in Russia

The aim of this study is to investigate the impact of corruption on bank lending in Russia. This issue is of major interest in order to understand the causes of financial underdevelopment and the effects of corruption in Russia. We use regional measures of corruption and bank-level data to perform this investigation. Our main estimations show that corruption hampers bank lending in Russia. We investigate whether this negative role of corruption is influenced by the degree of bank risk aversion, but find no effect. The detrimental effect of corruption is only observed for loans to households and firms, in opposition to loans to government. Additional controls confirm the detrimental impact of corruption on bank lending. Therefore, our results provide motivations to fight corruption to favor bank lending in Russia.     

Classification-based model selection in retail demand forecasting

Retailers supply a wide range of stock keeping units (SKUs), which may differ for example in terms of demand quantity, demand frequency, demand regularity, and demand variation. Given this diversity in demand patterns, it is unlikely that any single model for demand forecasting can yield the highest forecasting accuracy across all SKUs. To save costs through improved forecasting, there is thus a need to match any given demand pattern to its most appropriate prediction model. To this end, we propose an automated model selection framework for retail demand forecasting. Specifically, we consider model selection as a classification problem, where classes correspond to the different models available for forecasting. We first build labeled training data based on the models’ performances in previous demand periods with similar demand characteristics. For future data, we then automatically select the most promising model via classification based on the labeled training data. The performance is measured by economic profitability, taking into account asymmetric shortage and inventory costs. In an exploratory case study using data from an e-grocery retailer, we compare our approach to established benchmarks. We find promising results, but also that no single approach clearly outperforms its competitors, underlying the need for case-specific solutions.     

论我国经济发达地区民间借贷的发展与规制——以江苏为实证分析样本

经济发达地区民间借贷的发展具有复杂性和先行性,出现了很多规避法律和监管的结构化创新。研究经济发达地区民间借贷法律规制可以从具有代表性的客观对象中概括可取的经验,以提炼普遍性的规则,解决制度与监管的不足,保证社会的稳定发展。融资性民间借贷是现代民间借贷的常态,应当立足商法和经济法思维,坚持营业自由、过程性规制的理念,以信息披露、登记制度、利率和借贷担保为重点进行分类规制。     

Matching and semi-parametric IV estimation, a distance-based measure of migration, and the wages of young men

Our paper estimates the effect of US internal migration on wage growth for young men between their first and second job. Our analysis of migration extends previous research by: (i) exploiting the distance-based measures of migration in the National Longitudinal Surveys of Youth 1979 (NLSY79); (ii) allowing the effect of migration to differ by schooling level and (iii) using propensity score matching to estimate the average treatment effect on the treated (ATET) for movers and (iv) using local average treatment effect (LATE) estimators with covariates to estimate the average treatment effect (ATE) and ATET for compliers.We believe the Conditional Independence Assumption (CIA) is reasonable for our matching estimators since the NLSY79 provides a relatively rich array of variables on which to match. Our matching methods are based on local linear, local cubic, and local linear ridge regressions. Local linear and local ridge regression matching produce relatively similar point estimates and standard errors, while local cubic regression matching badly over-fits the data and provides very noisy estimates.We use the bootstrap to calculate standard errors. Since the validity of the bootstrap has not been investigated for the matching estimators we use, and has been shown to be invalid for nearest neighbor matching estimators, we conduct a Monte Carlo study on the appropriateness of using the bootstrap to calculate standard errors for local linear regression matching. The data generating processes in our Monte Carlo study are relatively rich and calibrated to match our empirical models or to test the sensitivity of our results to the choice of parameter values. The estimated standard errors from the bootstrap are very close to those from the Monte Carlo experiments, which lends support to our using the bootstrap to calculate standard errors in our setting.From the matching estimators we find a significant positive effect of migration on the wage growth of college graduates, and a marginally significant negative effect for high school dropouts. We do not find any significant effects for other educational groups or for the overall sample. Our results are generally robust to changes in the model specification and changes in our distance-based measure of migration. We find that better data matters; if we use a measure of migration based on moving across county lines, we overstate the number of moves, while if we use a measure based on moving across state lines, we understate the number of moves. Further, using either the county or state measures leads to much less precise estimates.We also consider semi-parametric LATE estimators with covariates (Frölich 2007), using two sets of instrumental variables. We precisely estimate the proportion of compliers in our data, but because we have a small number of compliers, we cannot obtain precise LATE estimates.     

Hierarchical clustering of spatially correlated functional data

Classification problems of functional data arise naturally in many applications. Several approaches have been considered for solving the problem of finding groups based on functional data. In this paper, we are interested in detecting groups when the functional data are spatially correlated. Our methodology allows to find spatially homogeneous groups of sites when the observations at each sampling location consist of samples of random functions. In univariable and multivariable geostatistics, various methods of incorporating spatial information into the clustering analysis have been considered. Here, we extend these methods to the functional context to fulfil the task of clustering spatially correlated curves. In our approach, we initially use basis functions to smooth the observed data, and then, we weight the dissimilarity matrix among curves by either the trace‐variogram or the multivariable variogram calculated with the coefficients of the basis functions. This paper contains a simulation study as well as the analysis of a real data set corresponding to average daily temperatures measured at 35 Canadian weather stations.     

Lending attitude as a financial accelerator in a credit network economy

In existing literature, commercial banks are often considered mere financial intermediaries that facilitate the flow of credit in an imperfect credit market. However, as demonstrated in the history of financial instability, the behavior of financial institutions plays an important role. This paper examines how lenders’ attitudes affect macroeconomic performance. In our analysis, the economy is composed of multiple borrowers (firms) and one lender (bank). Each borrower is directly connected to the lender through its credit contract. At the same time, each borrower is indirectly connected to all the other borrowers within the credit network. Using this model, we execute computer simulations to examine the economic consequences of lending attitudes. The results of the simulations demonstrate that the bank’s lending attitude functions as a financial accelerator; that is, it significantly affects the dynamics of the economic system through the credit network. Consequently, the same level of exogenous shock generates completely different outcomes depending on the different lending attitudes. The results also show that there exists an optimal lending attitude that leads to high economic growth and a stable growth path.     

An alternative quasi likelihood approach,Bayesian analysis and data-based inference for model specification

This paper studies an alternative quasi likelihood approach under possible model misspecification. We derive a filtered likelihood from a given quasi likelihood (QL), called a limited information quasi likelihood (LI-QL), that contains relevant but limited information on the data generation process. Our LI-QL approach, in one hand, extends robustness of the QL approach to inference problems for which the existing approach does not apply. Our study in this paper, on the other hand, builds a bridge between the classical and Bayesian approaches for statistical inference under possible model misspecification. We can establish a large sample correspondence between the classical QL approach and our LI-QL based Bayesian approach. An interesting finding is that the asymptotic distribution of an LI-QL based posterior and that of the corresponding quasi maximum likelihood estimator share the same “sandwich”-type second moment. Based on the LI-QL we can develop inference methods that are useful for practical applications under possible model misspecification. In particular, we can develop the Bayesian counterparts of classical QL methods that carry all the nice features of the latter studied in  White (1982). In addition, we can develop a Bayesian method for analyzing model specification based on an LI-QL.