Analysing nonlinear time series with central subspace

Traditionally, time series analysis involves building an appropriate model and using either parametric or nonparametric methods to make inference about the model parameters. Motivated by recent developments for dimension reduction in time series, an empirical application of sufficient dimension reduction (SDR) to nonlinear time series modelling is shown in this article. Here, we use time series central subspace as a tool for SDR and estimate it using mutual information index. Especially, in order to reduce the computational complexity in time series, we propose an efficient estimation method of minimal dimension and lag using a modified Schwarz–Bayesian criterion, when either of the dimensions and the lags is unknown. Through simulations and real data analysis, the approach presented in this article performs well in autoregression and volatility estimation.     

Importance sampling to evaluate bivariate probabilities with an application to sequential Phase II trials

In this paper we propose an importance sampling method for evaluating probabilities associated with the margins of a 2 × 2 table in a fixed or multistage design. These probabilities arise in the work of Conaway and Petroni (1994) on sequential phase I1 trials with bivariate endpoints. The importance sampling method proposed in this paper provides an efficient method, relative to exact enumeration and Monte Carlo techniques with no variance reduction, of estimating these probabilities. The method can also be adapted to estimate probabilities for the designs of Jennison and Turnbull (1993).     

Enhancements of Non‐parametric Generalized Likelihood Ratio Test: Bias Correction and Dimension Reduction

Non‐parametric generalized likelihood ratio test is a popular method of model checking for regressions. However, there are two issues that may be the barriers for its powerfulness: existing bias term and curse of dimensionality. The purpose of this paper is thus twofold: a bias reduction is suggested and a dimension reduction‐based adaptive‐to‐model enhancement is recommended to promote the power performance. The proposed test statistic still possesses the Wilks phenomenon and behaves like a test with only one covariate. Thus, it converges to its limit at a much faster rate and is much more sensitive to alternative models than the classical non‐parametric generalized likelihood ratio test. As a by‐product, we also prove that the bias‐corrected test is more efficient than the one without bias reduction in the sense that its asymptotic variance is smaller. Simulation studies and a real data analysis are conducted to evaluate of proposed tests.     

Sufficient dimension reduction via distance covariance with multivariate responses

In this article, we propose a new method for sufficient dimension reduction when both response and predictor are vectors. The new method, using distance covariance, keeps the model-free advantage, and can fully recover the central subspace even when many predictors are discrete. We then extend this method to the dual central subspace, including a special case of canonical correlation analysis. We illustrated estimators through extensive simulations and real datasets, and compared to some existing methods, showing that our estimators are competitive and robust.     

Unstructured principal fitted response reduction in multivariate regression

In this paper, an unstructured principal fitted response reduction approach is proposed. The new approach is mainly different from two existing model-based approaches, because a required condition is assumed in a covariance matrix of the responses instead of that of a random error. Also, it is invariant under one of popular ways of standardizing responses with its sample covariance equal to the identity matrix. According to numerical studies, the proposed approach yields more robust estimation than the two existing methods, in the sense that its asymptotic performances are not severely sensitive to various situations. So, it can be recommended that the proposed method should be used as a default model-based method.     

政府刺激性投资政策下中小微企业银税互动设计及其投融资决策

银税互动是中国解决中小微企业融资问题的金融创新。文章在政府刺激投资政策(财政补贴和税收减免)下研究银税互动设计与中小微企业投融资问题,采用实物期权方法,并运用资产定价理论和动态规划理论解析地得到了企业各未定权益价值以及企业决策结果。通过静态比较分析,文章发现与税收减免政策相比较,财政补贴的企业投资加速,破产风险较低,企业价值较高,对应银行紧缩的信贷政策。在债务融资结构方面,企业一般选择银税互动融资。但是,对于财政补贴政策而言,在较稳定的金融环境下,企业会选择银税互动和民间借贷混合的融资方式。此外,文章从企业基本面角度发现,对于波动率较低的企业,银行应采取宽松的信贷政策;对于波动率较高的企业,银行应采取紧缩的信贷政策。最后,从企业和政府角度均发现,在政府政策中财政补贴可能占优。     

Welfare State,family and the market's SINS in the EU

Our purpose is to show that a well-designed Welfare State is a fundamental fiscal policy instrument which allows mending of the main shortcomings of the market mechanism, i.e. lack of Stability, Inequality, disregard of Needs, lack of economic Security: in short, we call them the market's SINS. We show how the family is the main agent for an effective Welfare State, as the firms are for the market. We use detailed data on all the European countries to characterize the different models of welfare within Europe. A cross-section analysis shows that the main goals are better achieved when the social protection benefits focus on the functions of family and children, sickness and health care, because they induce the greatest poverty reduction. We show that for families with children the better working arrangement – which is associated with higher GDP per capita and lower inequality – is one parent working full-time and one parent part-time. We quantify the cross-section variability related to the age structure for the function of pensions. For a sample of seven major European countries we measure the degree of pro-cyclical or countercyclical relationship between social protection expenditures and GDP per capita from 1995 to 2010. In Germany, a Keynesian economic policy in 2009 is associated with a sharp rebound in 2010. We measure the relationship between Welfare State benefits and public budget, given as a constraint. We show a possible relationship between social protection benefits and family savings.     

What Did US$18 bn Achieve? The 2005 Debt Relief to Nigeria

Since 2003 Nigeria's economic growth has been consistently above 6% and has been driven by non‐oil sectors. This article attempts to assess the contribution of the 2005 debt‐relief agreement to this higher growth. The agreement eliminated Nigeria's US$30 billion debt to Paris Club creditors who cancelled US$18 bn, while Nigeria paid back US$12 bn. The article traces the three possible impact channels of debt relief, namely, the flow (reduced debt service), the stock (removal of debt overhang) and the conditionality channel, and comes to the conclusion that the debt‐relief agreement played a key role in the country's improved economic performance, in particular through successful conditionality.     

Promoting Resilient Livelihoods through Adaptive Social Protection: Lessons from 124 programmes in South Asia

Adaptive Social Protection refers to efforts to integrate social protection (SP), disaster risk reduction (DRR) and climate change adaptation (CCA), the need for which is increasingly recognised by practitioners and academics. Relying on 124 agricultural programmes implemented in five countries in Asia, this article considers how these elements are being brought together, and explores the potential gains of these linkages. It shows that full integration is still relatively limited but that, when it occurs, it helps to shift the time horizon beyond short‐term interventions aimed at supporting peoples' coping strategies and/or graduation objectives, towards longer‐term interventions that can help promote transformation towards climate and disaster resilient livelihood options.     

Seattle’s Law Enforcement Assisted Diversion (LEAD): Program effects on recidivism outcomes

Drug users and dealers frequently cycle through the criminal justice system in what is sometimes referred to as a “revolving door.” Arrest, incarceration and prosecution have not deterred this recidivism. Seattle’s Law Enforcement Assisted Diversion (LEAD) program was established to divert these individuals to case management and supportive services instead of jail and prosecution. A nonrandomized controlled evaluation was conducted to examine LEAD effects on criminal recidivism (i.e., arrests, criminal charges). The sample included 318 people suspected of low-level drug and prostitution activity in downtown Seattle: 203 received LEAD, and 115 experienced the system-as-usual control condition. Analyses were conducted using logistic generalized estimating equation models over both the shorter term (i.e., six months prior and subsequent to evaluation entry) and longer term (i.e., two years prior to the LEAD start date through July 2014). Compared to controls, LEAD participants had 60% lower odds of arrest during the six months subsequent to evaluation entry; and both a 58% lower odds of arrest and 39% lower odds of being charged with a felony over the longer term. These statistically significant differences in arrests and felony charges for LEAD versus control participants indicated positive effects of the LEAD program on recidivism.