我国通货膨胀运动阶段的非线性平滑转换

本文分别构建了两、三以及四机制C-STAR模型来研究我国通货膨胀的非线性运动特征。实证结果表明：我国通胀率是平稳的非线性均值回归过程,依据三机制模型划分的通缩、通缩-通胀中间态以及通胀的三阶段能很好地刻画我国通货膨胀的运动特点;但是把我国通胀率划分为通缩、通胀适中、温和通胀和高通胀的四阶段能进一步提高模型的解释与拟合能力。我们详细阐述了通货膨胀在不同阶段的转换特点以及持久性与不确定性特征,发现通货膨胀的持久性与不确定性成反向非线性关系,与水平值的大小没有必然联系。在温和通胀阶段,其持久性最强,不确定性最小;在通胀适中阶段,其持久性最弱,不确定性最大。另外,温和通胀阶段也是最优通胀目标区间,为了保持经济的稳定增长,央行应把通胀率控制在该区间内。最后,本文给出了实证结果所蕴含的政策涵义。     

数据窥查效应、SPA检验与逐步检验法——兼论开放式基金绩效评价方法

采用新的计量检验方法来评估中国开放式基金的绩效是否具有统计上的显著性。为避免数据窥察所造成的推断错误,采用SPA逐步检验法(Step-SPA)来重新检验具有显著绩效的开放式基金。研究显示,2006-2010年间,若以月平均收益率作为绩效评价标准,中国开放式基金中没有任何一只基金显著地高于沪深300指数等表示的大盘收益率;只有6只基金的夏普指数在10%的显著性水平上超越大盘夏普指数;当绩效比较标准为三因子模型中的超额收益率时,仅有约7%的基金存在显著的超额收益率。     

Macroeconomic Uncertainty Through the Lens of Professional Forecasters

ABSTRACT

We analyze the evolution of macroeconomic uncertainty in the United States, based on the forecast errors of consensus survey forecasts of various economic indicators. Comprehensive information contained in the survey forecasts enables us to capture a real-time measure of uncertainty surrounding subjective forecasts in a simple framework. We jointly model and estimate macroeconomic (common) and indicator-specific uncertainties of four indicators, using a factor stochastic volatility model. Our macroeconomic uncertainty estimates have three major spikes has three major spikes aligned with the 1973–1975, 1980, and 2007–2009 recessions, while other recessions were characterized by increases in indicator-specific uncertainties. We also show that the selection of data vintages affects the estimates and relative size of jumps in estimated uncertainty series. Finally, our macroeconomic uncertainty has a persistent negative impact on real economic activity, rather than producing “wait-and-see” dynamics.     

Bayesian hierarchical model for the prediction of football results

The problem of modelling football data has become increasingly popular in the last few years and many different models have been proposed with the aim of estimating the characteristics that bring a team to lose or win a game, or to predict the score of a particular match. We propose a Bayesian hierarchical model to fulfil both these aims and test its predictive strength based on data about the Italian Serie A 1991–1992 championship. To overcome the issue of overshrinkage produced by the Bayesian hierarchical model, we specify a more complex mixture model that results in a better fit to the observed data. We test its performance using an example of the Italian Serie A 2007–2008 championship.     

Time-Varying Systemic Risk: Evidence From a Dynamic Copula Model of CDS Spreads

This article proposes a new class of copula-based dynamic models for high-dimensional conditional distributions, facilitating the estimation of a wide variety of measures of systemic risk. Our proposed models draw on successful ideas from the literature on modeling high-dimensional covariance matrices and on recent work on models for general time-varying distributions. Our use of copula-based models enables the estimation of the joint model in stages, greatly reducing the computational burden. We use the proposed new models to study a collection of daily credit default swap (CDS) spreads on 100 U.S. firms over the period 2006 to 2012. We find that while the probability of distress for individual firms has greatly reduced since the financial crisis of 2008–2009, the joint probability of distress (a measure of systemic risk) is substantially higher now than in the precrisis period. Supplementary materials for this article are available online.     

An empirical study on the threshold cointegration of Chinese A and H cross-listed shares

We investigate the dynamic relationship between the prices of Chinese A and H market cross-listed shares using the Enders–Siklos threshold cointegration approach. Our data are the daily closing prices of the Hang Seng China AH (A) index and the Hang Seng China AH (H) index from 4 January 2006 to 1 November 2013. We find a threshold cointegration between these two indices, instead of the linear cointegration well established in the literature. The short-term adjustment to the equilibrium shows an asymmetric effect according to the price deviation from the equilibrium. Moreover, using a Granger causality test, we find a bi-directional causality between these two markets, indicating a close relationship between them. A pairs trading rule, based on the estimated threshold cointegration model, demonstrates the usefulness of our results as it generates a significantly higher return than a naive buy-and-hold trading rule.     

基于PMI分解的制造业损失评估

2008年金融危机对我国制造业造成巨大冲击,对危机的影响进行评估对建立科学的危机预防机制具有重要的意义。文章运用X-13A-S模型将我国制造业PMI进行分解,研究各成分在危机期间的波动情况,分析金融危机的动态演变过程并构建本底趋势线进行损失评估,最后划分金融危机的生命周期。结论表明：①X-13A-S模型对指数调整效果较好,长期波动趋势分为三个时期;季节成分波动表现为“三波峰、三波谷”;异常值与危机事件相对应。②全面爆发前期PMI指数损失5.11%,平均损失1.28%,爆发后期指数损失49.31%,平均损失4.11%,本轮危机共损失54.42个百分点。③2008年1月至4月为生成期,2008年5月至12月为全面爆发期,2009年1月至5月为衰退期,2009年6月至8月为消亡期,整个危机持续16个月。④全面爆发时期以8月份为分界点,表现出“前期趋势-循环成分,后期不规则变动”的动态机制。     

国民收入流量矩阵的编制与预测方法研究

本文首先在国家统计局公布的资金流量（实物交易）表的基础上编制了1992-2008年的“部门×交易(S-by-T)”和“部门×部门(S-by-S)”国民收入流量矩阵,设计了一个国民收入流量组合预测模型（The combination forecasting model for national income flow. NCFM）,表述了编表的内容、方法、步骤,配合该模型又设计了国民收入动态均衡模型（The dynamic equilibrium model for national income flow. NDEM）和单方程模型对主要控制总量进行预测,采用状态空间模型对所预测的总量进行了部门和交易分解,用DRAS法编制了2009和2010年的“S-by -T”国民收入流量矩阵的延长表,用“部门收入转移法（SITM）”编制了各年度的“S-by-S”表。最后用Theil’s U、SWAD和STPE三种方法分别对数据质量进行了检验。     

Bayesian optimal design for changepoint problems

We investigate Bayesian optimal designs for changepoint problems. We find robust optimal designs which allow for arbitrary distributions before and after the change, arbitrary prior densities on the parameters before and after the change, and any log‐concave prior density on the changepoint. We define a new design measure for Bayesian optimal design problems as a means of finding the optimal design. Our results apply to any design criterion function concave in the design measure. We illustrate our results by finding the optimal design in a problem motivated by a previous clinical trial. The Canadian Journal of Statistics 37: 495–513; 2009 © 2009 Statistical Society of Canada     

中国金融资金流量矩阵表延长模型研究

金融资金流量矩阵表具有重要应用价值,然而我国资金流量表只采用账户表式,而且数据滞后严重,使其应用价值难以实现。本文在对资金流量矩阵表表式结构、账户分类和平衡关系进行深入理解的基础上,将计量经济模型中的多元回归模型、时间序列模型、状态空间模型及RAS模型有效地组合在一起,设计了一套延长金融资金流量矩阵表的组合模型,然后应用该模型将我国金融资金流量矩阵表从2009年延长至2012年。经后验法检验,数据质量达到了一个较为理想的状态。这套模型可以作为官方统计机构或民间研究机构延长资金流量表数据的通用方法使用。     

中国农村居民的健康动态研究

 利用四轮中国健康与营养状况调查构成的面板数据（1997-2006），本文研究了中国农村居民的健康动态决定过程及健康持续性。我们使用动态随机效应probit模型控制了个体的不可观测异质性。同时，为了避免状态依赖效应的估计偏差，本文分别采纳了Heckman(1981)和Wooldridge(2005)提出的估计方法来处理“初始条件问题”。结论表明：在控制了其他因素之后，状态依赖效应对中国农村居民健康状况具有显著的影响；此外，那些年龄较大、教育水平较低、收入水平较低的农村弱势群体陷入持续性健康问题的可能性更大。     

宏观金融效率的变化趋势分析

本文采用因子分析法，对1991—2006年我国宏观金融效率的变化趋势进行了综合评价分析。结果显示，如果把金融运行分为储蓄动员、储蓄投资转化和投资投向这样三个前后关联的环节，那么，在此期间我国的宏观金融效率经历了”由强转弱，再由弱变强”的变化过程；目前我国的资金使用效率相对最好，储蓄动员效率次之，而储蓄投资转化效率则相对最差。     

Particle Learning for Fat-Tailed Distributions

It is well known that parameter estimates and forecasts are sensitive to assumptions about the tail behavior of the error distribution. In this article, we develop an approach to sequential inference that also simultaneously estimates the tail of the accompanying error distribution. Our simulation-based approach models errors with a t_ν-distribution and, as new data arrives, we sequentially compute the marginal posterior distribution of the tail thickness. Our method naturally incorporates fat-tailed error distributions and can be extended to other data features such as stochastic volatility. We show that the sequential Bayes factor provides an optimal test of fat-tails versus normality. We provide an empirical and theoretical analysis of the rate of learning of tail thickness under a default Jeffreys prior. We illustrate our sequential methodology on the British pound/U.S. dollar daily exchange rate data and on data from the 2008–2009 credit crisis using daily S&P500 returns. Our method naturally extends to multivariate and dynamic panel data.     

Economic and economic-statistical designs of the T2 control charts with SVSSI sampling scheme

Recent studies have shown that the adaptive T² chart with two different sampling interval and three sample sizes (SVSSI) shows a good performance in detecting small to large shifts in the process mean. This paper investigates the economic and economic statistical designs of the SVSSI T² charts. We use the Markov chain approach to developing the cost model proposed by Costa and Rahim (Journal of applied statistics 2001; 28: 875–885). A genetic algorithm approach is used to find the optimal solutions. Using numerical examples, we illustrate the performance of the proposed model and compare the statistical, economic, and economic statistical designs of the SVSSI T² chart with respect to the economic and statistical criteria. Furthermore, we compare the performance of the SVSSI T² chart with the other T² control schemes.     

On the application of statistical learning approaches to construct inverse probability weights in marginal structural Cox models: Hedging against weight-model misspecification

The marginal structural Cox model (MSCM) estimates can be highly sensitive to weight-model misspecification. We assess the performance of various popular statistical learners, such as LASSO, support vector machines, CART, bagged CART, and boosted CART, in estimating MSCM weights. When weight-models are misspecified, we find that the weights computed from boosted CART generally lead to less MSE and better coverage for the MSCM estimates. This study is motivated by the investigation of the impact of beta-interferon treatment on disability progression in subjects with multiple sclerosis from British Columbia, Canada (1995–2008).     

Search With Dirichlet Priors: Estimation and Implications for Consumer Demand

This article is an empirical application of the search model with an unknown distribution, as introduced by Rothschild in 1974. For searchers who hold Dirichlet priors, we develop a novel characterization of optimal search behavior. Our solution delivers easily computable formulas for the ex-ante purchase probabilities as outcomes of search, as required by discrete-choice-based estimation. Using our method, we investigate the consequences of consumer learning on the properties of search-generated demand. Holding search costs constant, the search model from a known distribution predicts larger price elasticities, mainly for the lower-priced products. We estimate a search model with Dirichlet priors, on a dataset of prices and market shares of S&P 500 mutual funds. We find that the assumption of no uncertainty in consumer priors leads to substantial biases in search cost estimates.     

André Dabrowski's work on limit theorems and weak dependence

André Robert Dabrowski, Professor of Mathematics and Dean of the Faculty of Sciences at the University of Ottawa, died October 7, 2006, after a short battle with cancer. The author of the present paper, a long‐term friend and collaborator of André Dabrowski, gives a survey of André's work on weak dependence and limit theorems in probability theory. The Canadian Journal of Statistics 37: 307–326; 2009 © 2009 Statistical Society of Canada     

Small area estimation of the mean using non-parametric M-quantile regression: a comparison when a linear mixed model does not hold

The demand for reliable statistics in subpopulations, when only reduced sample sizes are available, has promoted the development of small area estimation methods. In particular, an approach that is now widely used is based on the seminal work by Battese et al. [An error-components model for prediction of county crop areas using survey and satellite data, J. Am. Statist. Assoc. 83 (1988), pp. 28–36] that uses linear mixed models (MM). We investigate alternatives when a linear MM does not hold because, on one side, linearity may not be assumed and/or, on the other, normality of the random effects may not be assumed. In particular, Opsomer et al. [Nonparametric small area estimation using penalized spline regression, J. R. Statist. Soc. Ser. B 70 (2008), pp. 265–283] propose an estimator that extends the linear MM approach to the case in which a linear relationship may not be assumed using penalized splines regression. From a very different perspective, Chambers and Tzavidis [M-quantile models for small area estimation, Biometrika 93 (2006), pp. 255–268] have recently proposed an approach for small-area estimation that is based on M-quantile (MQ) regression. This allows for models robust to outliers and to distributional assumptions on the errors and the area effects. However, when the functional form of the relationship between the qth MQ and the covariates is not linear, it can lead to biased estimates of the small area parameters. Pratesi et al. [Semiparametric M-quantile regression for estimating the proportion of acidic lakes in 8-digit HUCs of the Northeastern US, Environmetrics 19(7) (2008), pp. 687–701] apply an extended version of this approach for the estimation of the small area distribution function using a non-parametric specification of the conditional MQ of the response variable given the covariates [M. Pratesi, M.G. Ranalli, and N. Salvati, Nonparametric m-quantile regression using penalized splines, J. Nonparametric Stat. 21 (2009), pp. 287–304]. We will derive the small area estimator of the mean under this model, together with its mean-squared error estimator and compare its performance to the other estimators via simulations on both real and simulated data.     

A segmented regime-switching model with its application to stock market indices

This paper evaluates the ability of a Markov regime-switching log-normal (RSLN) model to capture the time-varying features of stock return and volatility. The model displays a better ability to depict a fat tail distribution as compared with using a log-normal model, which means that the RSLN model can describe observed market behavior better. Our major objective is to explore the capability of the model to capture stock market behavior over time. By analyzing the behavior of calibrated regime-switching parameters over different lengths of time intervals, the change-point concept is introduced and an algorithm is proposed for identifying the change-points in the series corresponding to the times when there are changes in parameter estimates. This algorithm for identifying change-points is tested on the Standard and Poor's 500 monthly index data from 1971 to 2008, and the Nikkei 225 monthly index data from 1984 to 2008. It is evident that the change-points we identify match the big events observed in the US stock market and the Japan stock market (e.g., the October 1987 stock market crash), and that the segmentations of stock index series, which are defined as the periods between change-points, match the observed bear–bull market phases.     

Model selection criteria for reduced rank multivariate time series: a simulation study

The main focus of our paper is to compare the performance of different model selection criteria used for multivariate reduced rank time series. We consider one of the most commonly used reduced rank model, that is, the reduced rank vector autoregression (RRVAR (p, r)) introduced by Velu et al. [Reduced rank models for multiple time series. Biometrika. 1986;7(31):105–118]. In our study, the most popular model selection criteria are included. The criteria are divided into two groups, that is, simultaneous selection and two-step selection criteria, accordingly. Methods from the former group select both an autoregressive order p and a rank r simultaneously, while in the case of two-step criteria, first an optimal order p is chosen (using model selection criteria intended for the unrestricted VAR model) and then an optimal rank r of coefficient matrices is selected (e.g. by means of sequential testing). Considered model selection criteria include well-known information criteria (such as Akaike information criterion, Schwarz criterion, Hannan–Quinn criterion, etc.) as well as widely used sequential tests (e.g. the Bartlett test) and the bootstrap method. An extensive simulation study is carried out in order to investigate the efficiency of all model selection criteria included in our study. The analysis takes into account 34 methods, including 6 simultaneous methods and 28 two-step approaches, accordingly. In order to carefully analyse how different factors affect performance of model selection criteria, we consider over 150 simulation settings. In particular, we investigate the influence of the following factors: time series dimension, different covariance structure, different level of correlation among components and different level of noise (variance). Moreover, we analyse the prediction accuracy concerned with the application of the RRVAR model and compare it with results obtained for the unrestricted vector autoregression. In this paper, we also present a real data application of model selection criteria for the RRVAR model using the Polish macroeconomic time series data observed in the period 1997–2007.