What went wrong with the Hungarian consumption function? An econometric investigation of the time series aggregate consumption function for Hungary for 1960–1989

The paper focuses on the time series aggregate consumption function for the Hungarian economy. The empirical econometric analysis presented produces several new results. First, it shows that the income and consumption variables used in this type of model by previous studies are I(2) variables. Consequently, error correction models formulated in terms of their first differences are mis-specified. Second, it provides a strong empirical evidence supporting the view that consumption (and thus saving) was (real) interest rate elastic during the period under investigation, having impact both on the long run and on the short relationships between income and consumption. Third, it provides empirical evidence on choosing the proper income variable in the consumption function. The model selection results clearly supports the model with unadjusted total real money income variable. Fourth, it shows that for the period 1960–1986 a correctly specified and stable error correction model can be established. Finally, the analysis shows that when used for the period beyond 1986, this model suffers from a structural break.     

Dynamic Structure of Disequilibrium Models

This paper investigates the dynamic structure of a standard disequilibrium model. By assuming that the model variables are non-stationary time series with respect to ample empirical evidence, we find the following: 1) It is the exogenous variables rather than the price adjustment process that form the real adjustment force of the model; 2) Quantity disequilibrium and price disequilibrium are isomeric in the model, and follow a weakly stationary process when all the variables areI (1) nonstationary; 3) The disequilibrium process has a none-zero mean when the weakly exogenous variables of the demand equation do not cointegrate with those of the supply equation, corresponding to certain 'chronic disequilibrium' phenomena; 4) The isomerism between quantity disequilibrium and price changes makes it unnecessary to lean on the 'min condition' to characterise disequilibrium.     

Bias corrections for exponentially transformed forecasts: Are they worth the effort?

In many economic applications, it is convenient to model and forecast a variable of interest in logs rather than in levels. However, the reverse transformation from log forecasts to levels introduces a bias. This paper compares different bias correction methods for such transformations of log series which follow a linear process with various types of error distributions. Based on Monte Carlo simulations and an empirical study of realized volatilities, we find no choice of correction method that is uniformly best. We recommend the use of the variance-based correction, either by itself or as part of a hybrid procedure where one first decides (using a pretest) whether the log series is highly persistent or not, and then proceeds either without bias correction (high persistence) or with bias correction (low persistence).     

Commitment: A mathematical model

The concept of commitment has received ever-increasing attention from social scientists during the past fifteen years. Its usage occurs in several seemingly disjoint problem areas, but it is typically introduced when available explanations fail to describe the tendency for individuals to persist on a given course of action, once started, without obvious motive for doing so. Until recently its theoretical status has been as a primitive term, often appearing in ad hoc explanations, and accepted without question. In sociology, the first major query into the nature of commitment was Howard Becker's “Notes on the Concept of Commitment” (1960), a particularly influential article in stimulating research on commitment. However, most subsequent empirical work has dealt with the correlates and effects of commitment rather than with its genesis. Regrettably, what was once a problem in ad hoc theorizing has come to be matched by ad hoc operationalization of the concept. The present paper is a response to a perceived need for a formal model of commitment whose features might make the concept amenable to uniform theoretical and empirical usage. The purpose of this paper is to present a mathematical model of a general commitment mechanism which complements some extant ideas about the formal role of commitment in explaining certain types of behavior. The first part of the paper concentrates on extracting ideas from sociological and social psychological literature which provide the foundation for the intuitions to be formalized later. The second part presents a mathematical model of commitment. The third part discusses commitment in the context of a turnover model, and in concluding suggests some possible revisions and extensions of the model.     

基于预测能力的扩展极限边界分析法

本文在传统EBA方法的基础上,将其引入到时间序列中,构建以预测为导向的AEBA模型选择方法。AEBA在模型选择上更注重于模型的预测能力,在稳健性检验上细分为模型稳健性检验与参数稳健性检验两部分,提出了基于时间序列预测能力的检验方法。最后实证示例用AEBA方法对影响石油股票指数收益率的因素进行了研究,表明该方法选择的模型的预测能力,特别是短期预测能力要显著强于CAPM、三因子模型、ARMA以及VAR。     

The voice of the loyal manager: Distinguishing attachment from commitment

The purpose of this article is to use Albert Hirschman's Exit, Voice, and Loyalty framework to analyze the extent to which corporate employees are merely attached to the firm rather than committed. A model of managerial loyalty is developed where loyalty is defined as the percentage pay increase that an employee would require to leave the current firm for alternative employment. Independent variables in the model include barriers to exit and voice. This model was tested on three data sets from North American airlines. This model received empirical support during a stable environment but was not strongly supported during a more turbulent environment. Implications of the results are discussed.     

Nonstationary nonlinear heteroskedasticity

In this paper, we consider time series with the conditional heteroskedasticities that are given by nonlinear functions of integrated processes. Such time series are said to have nonlinear nonstationary heteroskedasticity (NNH), and the functions generating conditional heterogeneity are called heterogeneity generating functions (HGF's). Various statistical properties of time series with NNH are investigated for a wide class of HGF's. For NNH models with a variety of HGF's, volatility clustering and leptokurtosis, which are common features of ARCH type models, are manifest. In particular, it is shown that the sample autocorrelations of their squared processes vanish only very slowly, or do not even vanish at all, in the limit. Volatility clustering is therefore well expected. The NNH models with certain types of HGF's indeed have sample characteristics that are very similar to those of ARCH type models. Moreover, the sample kurtosis of the NNH model either diverges or has a stable limiting distribution with support truncated on the left by the kurtosis of the innovations. This would well explain the presence of leptokurtosis in many observed time series data. To illustrate the empirical relevancy of our model, we analyze the spreads between the forward and spot rates of USD/DM exchange rates. It is found that the conditional variances of the spreads can be well modelled as a nonlinear function of the levels of the spot rates.     

Discussion of “Nowcasting US GDP: The role of ISM Business Surveys”

This paper discusses the contribution of Lahiri and Monokroussos, published in the current issue of this journal, where they investigate the nowcasting power of ISM Business Surveys for real US GDP. The second part of this note includes some empirical considerations on nowcasting quarterly real GDP by using the monthly PMI index for Switzerland. The results indicate that the Swiss PMI is not leading GDP growth; rather, it is coincident, and its nowcasting power is quite good. The signs of the fitted values mostly correspond to the sign of the actual GDP growth, and the important turning points are identified accurately by the model. This also holds true during the recent crisis.     

基于GIS和粒子群算法的两级物流配送中心选址优化方法及应用

根据总运费最小化原则,以用户空间数据库为背景建立了针对两级配送中心的选址优化模型。为了对选址优化模型求解,提出了一种变异粒子群算法。以地理信息系统(GIS)为平台,对山东省区域物流配送中心的选址优化进行了实证研究,所得结果符合山东省实际情况,表明本文提出的选址优化方法具有较大的应用价值。     

A theoretical and empirical evaluation and extension of the Todaro migration model

"This paper postulates that it is theoretically and empirically preferable to base internal labor migration on the relative difference in rural-urban real income streams and rates of unemployment, taken as separate and independent variables, rather than on the difference in the expected real income streams as postulated by the very influential and often quoted Todaro model. The paper goes on to specify several important ways of extending the resulting migration model and improving its empirical performance." The analysis is based on Italian data.     

Double Empirical Bayes Testing

Analysing data from large-scale, multiexperiment studies requires scientists to both analyse each experiment and to assess the results as a whole. In this article, we develop double empirical Bayes testing (DEBT), an empirical Bayes method for analysing multiexperiment studies when many covariates are gathered per experiment. DEBT is a two-stage method: in the first stage, it reports which experiments yielded significant outcomes and in the second stage, it hypothesises which covariates drive the experimental significance. In both of its stages, DEBT builds on the work of Efron, who laid out an elegant empirical Bayes approach to testing. DEBT enhances this framework by learning a series of black box predictive models to boost power and control the false discovery rate. In Stage 1, it uses a deep neural network prior to report which experiments yielded significant outcomes. In Stage 2, it uses an empirical Bayes version of the knockoff filter to select covariates that have significant predictive power of Stage 1 significance. In both simulated and real data, DEBT increases the proportion of discovered significant outcomes and selects more features when signals are weak. In a real study of cancer cell lines, DEBT selects a robust set of biologically plausible genomic drivers of drug sensitivity and resistance in cancer.     

基于模糊综合评价法的酒店供应链风险评价研究

企业运用供应链管理,提高企业核心竞争力和增加利润的同时,也面临着供应链管理带来的一系列风险。文中通过分析酒店供应链模型及其特点,识别出酒店供应链所面临的风险,构建了酒店供应链风险评价指标体系和模糊综合评价模型,并通过实证分析酒店供应链的风险水平。.     

影响中国城市增长的因素:地级及以上城市的实证检验

论文以国内外相关城市增长理论为基础,构建了分析中国城市增长的系统模型,并利用206个城市1990年与2005年的社会经济统计数据进行了实证研究.研究结果表明:(1)中国城市的经济增长主要是由劳动生产率提高而非人口规模增长推动的;(2)政府财政支出和城市人力资本积累是目前中国城市经济增长的主要驱动力;(3)人力资本积累并没有在工资增长上得到体现;(4)城市公共设施和环境的改善引致了城市人口增长,但对城市经济发展却没有明显的作用.在以上研究结果基础上,论文提出了保持城市可持续发展的一些政策建议.     

Robust Inference on Average Economic Growth*

We discuss a method to estimate the confidence bounds for average economic growth, which is robust to misspecification of the unit root property of a given time series. We derive asymptotic theory for the consequences of such misspecification. Our empirical method amounts to an implementation of the subsampling procedure advocated in Romano and Wolf (Econometrica, 2001, Vol. 69, p. 1283). Simulation evidence supports the theory and it also indicates the practical relevance of the subsampling method. We use quarterly postwar US industrial production for illustration and we show that non‐robust approaches rather lead to different conclusions on average economic growth than our robust approach.     

基于GIS的多级物流中心选址动态模型分析

介绍GIS网络分析技术的基础上,重点分析了影响城市物流配送中心选址的若干因素。结合鲍姆尔-沃尔夫法的选址思想,提出了基于GIS的城市配送中心选址模型,并对模型进行了实证分析。     

A note on the microeconomics of migration

"The purpose of this note is to demonstrate in a simple model that an individual's migration from a small town to a large city may be rationalized purely by a consumption motive, rather than the motive of obtaining a higher income. More specifically, it is shown that in a large city an individual may derive a higher utility from spending a given amount of income than in a small town." A formal model is first developed that includes the principal forces at work and is then illustrated using a graphic example. The theoretical and empirical issues raised are considered in the concluding section.     

Exchange rate regime choice and currency crises

Exchange rate regime choice is not exogenous, but it depends on the structural, political and financial features of countries. However, it is often the case that the regime actually pursued and the one that is imposed by country features do not match one to one. The existing empirical crisis models do not take fully into account the regime in which the crisis unfolded. The aim of this paper is to incorporate the appropriateness of the regime choice into the standard currency crisis model. The results show that the odds of crisis increase significantly in countries which have chosen regimes inconsistently.     

城镇居民收入水平对零售业与批发业影响的实证研究——基于协整和向量自回归模型

文章选取1995年-2008年我国城镇居民家庭人均可支配收入和零售业销售总额、批发业销售总额的时间序列数据,通过基于VAR模型的协整分析、脉冲响应分析和方差分解的有机结合进行研究,发现上述序列间存在长期均衡。对标准化协整方程和误差校正模型的分析表明,我国城镇居民收入水平和零售业销售总额、批发业销售总额之间形成了长期的、稳定的关系,且城镇居民收入水平是零售业销售总额、批发业销售总额的格兰杰原因。脉冲响应分析和方差分解均显示,城镇居民收入水平在过去十四年内对零售业、批发业产生了持续性的影响。     

Direct comparison of agent-based models of herding in financial markets

The present paper tests a new model comparison methodology by comparing multiple calibrations of three agent-based models of financial markets on the daily returns of 24 stock market indices and exchange rate series. The models chosen for this empirical application are the herding model of Gilli and Winker (2003), its asymmetric version by Alfarano et al. (2005) and the more recent model by Franke and Westerhoff (2011), which all share a common lineage to the herding model introduced by Kirman (1993). In addition, standard ARCH processes are included for each financial series to provide a benchmark for the explanatory power of the models. The methodology provides a consistent and statistically significant ranking of the three models. More importantly, it also reveals that the best performing model, Franke and Westerhoff, is generally not distinguishable from an ARCH-type process, suggesting their explanatory power on the data is similar.     

Time series properties of an artificial stock market

This paper presents results from an experimental computer simulated stock market. In this market artificial intelligence algorithms take on the role of traders. They make predictions about the future, and buy and sell stock as indicated by their expectations of future risk and return. Prices are set endogenously to clear the market. Time series from this market are analyzed from the standpoint of well-known empirical features in real markets. The simulated market is able to replicate several of these phenomenon, including fundamental and technical predictability, volatility persistence, and leptokurtosis. Moreover, agent behavior is shown to be consistent with these features, in that they condition on the variables that are found to be significant in the time series tests. Agents are also able to collectively learn a homogeneous rational expectations equilibrium for certain parameters giving both time series and individual forecast values consistent with the equilibrium parameter values.