中国玉米芯资源量估算及其开发利用

文章首先采用文献考证法,确定玉米芯产量与玉米产量之比值;在此基础上,采用副产品比重法,结合玉米产量统计数据,对全国及各省(市、自治区)的玉米芯产量进行估算,并分析其区域分布;通过实地调查,结合文献综述,从原料化、饲料化、基料化、能源化的角度,系统阐述我国玉米芯资源综合利用途径。结果表明:玉米芯产量与玉米产量之比为0.21;2013年全国玉米芯资源总产量为4 590万t,相当于全国秸秆总产量的4.90%或玉米秸秆总产量的19.10%。主要分布于东北地区与黄淮海地区,产量分别为1 586.61万t与1 185.41万t,两地区合计占全国玉米芯产量的60.42%。玉米芯是我国各类秸秆资源中高值化利用率较高的种类,其原料化、基料化、饲料化及能源化利用总量达到800万t左右。食用菌基料化、新型能源化利用是提高我国玉米芯综合利用水平的基本途径。多产品联产可有效地促进我国玉米芯工业原料化利用水平的不断提升。积极开展玉米芯发酵饲料技术推广与应用可为我国玉米芯资源高值化利用开辟新途径。     

Accurate Short-Term Yield Curve Forecasting using Functional Gradient Descent

We propose a multivariate nonparametric technique for generatingreliable short-term historical yield curve scenarios and confidenceintervals. The approach is based on a Functional Gradient Descent(FGD) estimation of the conditional mean vector and covariancematrix of a multivariate interest rate series. It is computationallyfeasible in large dimensions and it can account for nonlinearitiesin the dependence of interest rates at all available maturities.Based on FGD we apply filtered historical simulation to computereliable out-of-sample yield curve scenarios and confidenceintervals. We back-test our methodology on daily USD bond datafor forecasting horizons from 1 to 10 days. Based on severalstatistical performance measures we find significant evidenceof a higher predictive power of our method when compared toscenarios generating techniques based on (i) factor analysis,(ii) a multivariate CCC-GARCH model, or (iii) an exponentialsmoothing covariances estimator as in the RiskMetrics^TM approach.     

技术创新、金融创新与经济增长——基于中国省际面板数据

本文从实证分析层面对金融创新、技术创新与经济增长之间的作用关系进行探讨。基于我国 2000~2015 年省级面板数据,采用GMM估计方法,利用动态面板模型进行实证检验。结果表明：技术创新对经济增长的影响显著为正,单独的金融创新对经济增长起抑制作用,二者的交互作用则对经济增长起显著性正向作用。脱离实体经济的金融创新将抑制经济增长,也会间接阻碍技术创新对经济增长的影响。本文结合当前经济形势,从金融机构与创新企业相接轨共同提高创新水平、培养创新型人才等角度提出建议。     

The expected real yield and inflation components of the nominal yield curve

The term structure of real yields and expected inflation are two unobserved components of the nominal yield curve. The primary objectives of this study are to decompose nominal yields into their expected real yield and inflation components and to examine their behaviour using state-space and regime-switching frameworks. The dynamic yield-curve models capture three well-known latent factors – level, slope, and curvature – that accurately aggregate the information for the nominal yields and the expected real and inflation components for all maturities. The nominal yield curve is found to increase slightly with a slope of about 120 basis points, while the real yield curve slopes upward by about 20 basis points, and the expected inflation curve is virtually flat at slightly above 2 per cent. The regime-switching estimations reveal that the nominal yield, real yield and expected inflation curves have shifted down significantly since 1999.     

Asymptotic expansion of S-estimators of location and covariance

By means of a straightforward application of empirical process theory, we show that S-estimators of multivariate location and covariance are asymptotically equivalent to a sum of independent vector and matrix valued random elements respectively. This provides an alternative proof of asymptotic normality of S-estimators and clearly explains the limiting covariance structure. It also leads to a relatively simple proof of asymptotic normality of the length of the shortest α-fraction.     

Simplified Jackknife Variance Estimates for Fuzzy Measures of Multidimensional Poverty

In this paper, we present a practical methodology for variance estimation for multi‐dimensional measures of poverty and deprivation of households and individuals, derived from sample surveys with complex designs and fairly large sample sizes. The measures considered are based on fuzzy representation of individuals' propensity to deprivation in monetary and diverse non‐monetary dimensions. We believe this to be the first original contribution for estimating standard errors for such fuzzy poverty measures. The second objective is to describe and numerically illustrate computational procedures and difficulties in producing reliable and robust estimates of sampling error for such complex statistics. We attempt to identify some of these problems and provide solutions in the context of actual situations. A detailed application based on European Union Statistics on Income and Living Conditions data for 19 NUTS2 regions in Spain is provided.     

Some Variants of Constrained Estimation in Finite Population Sampling

This is a review article that unifies several important examples using constrained optimisation techniques. The basic tools are three simple mathematical optimisation results subject to certain constraints. Applications include calibration, benchmarking in small area estimation and imputation. A final illustration is constrained optimisation under a general divergence loss.     

Structural VAR and financial networks: A minimum distance approach to spatial modeling

In this paper, I interpret a time series spatial model (T-SAR) as a constrained structural vector autoregressive (SVAR) model. Based on these restrictions, I propose a minimum distance approach to estimate the (row-standardized) network matrix and the overall network influence parameter of the T-SAR from the SVAR estimates. I also develop a Wald-type test to assess the distance between these two models. To implement the methodology, I discuss machine learning methods as one possible identification strategy of SVAR models. Finally, I illustrate the methodology through an application to volatility spillovers across major stock markets using daily realized volatility data for 2004–2018.     

Maximum Likelihood Estimation of a General Unbalanced Spatial Random Effects Model: a Monte Carlo Study

Abstract

This paper discusses the maximum likelihood estimator of a general unbalanced spatial random effects model with normal disturbances, assuming that some observations are missing at random. Monte Carlo simulations show that the maximum likelihood estimator for unbalanced panels performs well and that missing observations affect mainly the root mean square error. As expected, these estimates are less efficient than those based on the unobserved balanced model, especially if the share of missing observations is large or spatial autocorrelation in the error terms is pronounced.

Estimation de vraisemblance maximale d'un modèle général d'effets aléatoires spatiaux déséquilibré: une étude Monte Carlo

RÉSUMÉ La présente communication se penche sur l'estimateur du maximum de vraisemblance d'un modèle général d'effets aléatoires spatiaux déséquilibré avec des perturbations normales, en supposant l'absence aléatoire de certaines observations. Des simulations de Monte Carlo montrent que des groupes déséquilibrés se comporte bien, et que les observations manquantes affectent principalement l'erreur de la moyenne quadratique. Comme prévu, ces évaluations sont moins efficaces que celles qui sont basées sur le modèle équilibré non observé, notamment si la part des observations manquantes est importantes, ou l'on déclare une autocorrélation spatiale dans les termes d'erreur.

Estimación de la probabilidad máxima de un modelo espacial general desequilibrado de efectos al azar: un estudio de Monte Carlo

RÉSUMÉN Este trabajo discute el estimador de probabilidad máxima de un modelo espacial general desequilibrado de efectos al azar con alteraciones normales, suponiendo que faltan algunas observaciones al azar. Las simulaciones de Monte Carlo muestran que el estimador de probabilidad máxima para los paneles desequilibrados funciona satisfactoriamente, y que las observaciones omisas afectan principalmente al error de la media cuadrática. Como se suponía, estas estimaciones son menos eficientes que las basadas en el modelo equilibrado inadvertido, especialmente si la cantidad de omisiones es grande/o la autocorrelación en los términos de error es pronunciada.

     

军用直升机使用保障费用分析

针对军用直升机装备保障的特点和影响使用与保障费用的重要活动,利用工程法分析了使用与保障费用分解结构。同时,建立了相应的费用估算表达式。