Approximate inference in multivariate nonlinear functional relationships

An estimation procedure based on estimating equations is presented for the parameters in a multivariate functional relationship model, where all observations are subject to error. The covariance matrix of the observational errors may be parametrized and is allowed to be different for different sets of observations. Estimators are defined for the unknown relation parameters and error parameters.
For linear models (i.e. where the model function is linear in the incidental parameters) the estimators are consistent and asymptotically normal. A consistent expression for the covariance matrix of the estimators is derived. The results are valid for general error distributions.
For nonlinear models the estimators are based on locally linear approximations to the model function. The afore mentioned properties of the estimators are now only approximately valid. The adequacy of the approximate inference, based on asymptotic theory for the linearized model, needs at least informal check. Some examples are given to illustrate the estimation procedure.     

Bayesian inference and prediction in the single server Markovian queue

The use of stochastic models and performance measures for the analysis of real life queuing scenarios are based on the fundamental premise that parameters values are known. This is a rarity since more often than not, parameters are usually unknown and require to be estimated. This paper presents techniques for the same from Bayesian perspective. The queue we intend to deal with is the M/M/1 queuing model. Several closed form expressions on posterior inference and prediction are presented which can be readily implemented using standard spreadsheet tools. Previous work in this direction resulted in non-existence of posterior moments. A way out is suggested. Interval estimates and tests of hypothesis on performance measures are also presented.     

Quasi-likelihood inference in a generalized linear mixed model for balanced data

For modelling the effect of crossed, fixed factors on the response variable in balanced designs with nested stratifications, a generalized linear mixed model is proposed. This model is based on a set of quasi-likelihood assumptions which imply quadratic variance functions. From these variance functions, deviances are obtained to quantify the variation per stratification. The effects of the fixed factors will be tested, an dispersion components will be estimated. The practical use of the model is illustrated by reanalysing a soldering failures problem.     

Hidden group patterns in democracy developments: Bayesian inference for grouped heterogeneity

We propose a nonparametric Bayesian approach to estimate time‐varying grouped patterns of heterogeneity in linear panel data models. Unlike the classical approach in Bonhomme and Manresa (Econometrica, 2015, 83, 1147–1184), our approach can accommodate selection of the optimal number of groups and model estimation jointly, and also be readily extended to quantify uncertainties in the estimated group structure. Our proposed approach performs well in Monte Carlo simulations. Using our approach, we successfully replicate the estimated relationship between income and democracy in Bonhomme and Manresa and the group characteristics when we use the same number of groups. Furthermore, we find that the optimal number of groups could depend on model specifications on heteroskedasticity and discuss ways to choose models in practice.     

视频资料在高职《食品加工机械与设备》中的运用

本文介绍了视频资料在《食品加工机械与设备》课程教学中的运用,并分析了视频资料在教学过程中的积极作用及出现的问题。     

Estimation and model selection based inference in single and multiple threshold models

This paper evaluates the properties of a joint and sequential estimation procedure for estimating the parameters of single and multiple threshold models. We initially proceed under the assumption that the number of regimes is known á priori but subsequently relax this assumption via the introduction of a model selection based procedure that allows the estimation of both the unknown parameters and their number to be performed jointly. Theoretical properties of the resulting estimators are derived and their finite sample properties investigated.     

Analytical quasi maximum likelihood inference in multivariate volatility models

Quasi maximum likelihood estimation and inference in multivariate volatility models remains a challenging computational task if, for example, the dimension of the parameter space is high. One of the reasons is that typically numerical procedures are used to compute the score and the Hessian, and often they are numerically unstable. We provide analytical formulae for the score and the Hessian for a variety of multivariate GARCH models including the Vec and BEKK specifications as well as the recent dynamic conditional correlation model. By means of a Monte Carlo investigation of the BEKK–GARCH model we illustrate that employing analytical derivatives for inference is clearly preferable to numerical methods.     

Forecasting using variational Bayesian inference in large vector autoregressions with hierarchical shrinkage

Many recent papers in macroeconomics have used large vector autoregressions (VARs) involving 100 or more dependent variables. With so many parameters to estimate, Bayesian prior shrinkage is vital to achieve reasonable results. Computational concerns currently limit the range of priors used and render difficult the addition of empirically important features such as stochastic volatility to the large VAR. In this paper, we develop variational Bayesian methods for large VARs that overcome the computational hurdle and allow for Bayesian inference in large VARs with a range of hierarchical shrinkage priors and with time-varying volatilities. We demonstrate the computational feasibility and good forecast performance of our methods in an empirical application involving a large quarterly US macroeconomic data set.     

Estimation and inference of dynamic structural factor models with over-identifying restrictions

This paper develops a new estimator for the impulse response functions in structural factor models with a fixed number of over-identifying restrictions. The proposed identification scheme nests the conventional just-identified recursive scheme as a special case. We establish the asymptotic distributions of the new estimator and develop test statistics for the over-identifying restrictions. Simulation results show that adding a few more over-identifying restrictions can lead to a substantial improvement in estimation accuracy for impulse response functions at both zero and nonzero horizons. We estimate the effects of a monetary policy shock using a U.S. data set. The results show that our over-identified scheme can help to detect incorrect specifications that lead to spurious impulse responses.     

Threshold estimation for continuous three-phase polynomial regression models with constant mean in the middle regime

This paper considers a continuous three-phase polynomial regression model with two threshold points for dependent data with heteroscedasticity. We assume the model is polynomial of order zero in the middle regime, and is polynomial of higher orders elsewhere. We denote this model by ${ℳ}_2$, which includes models with one or no threshold points, denoted by ${ℳ}_1$ and ${ℳ}_0$, respectively, as special cases. We provide an ordered iterative least squares (OiLS) method when estimating ${ℳ}_2$ and establish the consistency of the OiLS estimators under mild conditions. When the underlying model is ${ℳ}_1$ and is $(d_0-1)$th-order differentiable but not $d_0$th-order differentiable at the threshold point, we further show the $O_p(N^{-1/(d_0+2)})$ convergence rate of the OiLS estimators, which can be faster than the $O_p(N^{-1/(2d_0)})$ convergence rate given in Feder when $d_0\ge 3$. We also apply a model-selection procedure for selecting ${ℳ}_{\kappa }$; $\kappa =0,1,2$. When the underlying model exists, we establish the selection consistency under the aforementioned conditions. Finally, we conduct simulation experiments to demonstrate the finite-sample performance of our asymptotic results.     

中部六省承接东部地区加工贸易产业转移比较研究——基于引力模型的分析

产业转移是促进区域经济发展的有力工具,也是产业转移双方的双赢选择.本文在综述产业转移相关文献的基础上,将引力模型引入产业转移的研究中,给出了产业转移力度的测算模型.并具体以加工贸易产业为例,选取了具有代表性的六个东部省(市)以及中部六省为研究对象,对其2006-2008年间加工贸易产业转移趋势进行了测算和分析,这对定量...     

岩溶地区工程勘察及地基分析处理探讨

贵州以碳酸盐岩喀斯特岩溶地貌为主,岩溶的出现对建筑会产生稳定性方面的影响,地基基础处理不当使建筑物失稳,造成经济损失和危害。本文就岩溶地区工程勘察所遇到的各类地基问题以及实际处理措施作一些分析,以期待今后对岩溶地区工程勘察起指导作用。     

美国高校财务困境预警研究述评

本文对20世纪70年代以来美国关于高校财务困境预警研究方面的文章进行分类统计,系统评述了美国高校财务预警研究的轨迹,在此基础上得出若干启示。     

Optimization in a Dynamic Leontief Model with Restricted Value Growth

The purpose of this paper is to develop a dynamic Leontief model of an msector economic system in which the production of all goods requires one time period and one primary factor, but no capital stocks of any good, and in which the total value of outputs from all sectors is required to grow at a specified rate in each period. The requirement of a fixed rate of total value growth is less restrictive than the familiar condition of balanced growth across all sectors, and it permits the definition and analysis of interesting finite-period optimization problems. Specific results of the paper include the following: (1) the proof that a value-added maximization problem with an unrestricted initial state will experience consumption in exactly one sector in each time period, and willyield an optimal value function which is linear in the variables that describe the terminal state of the system; (2) the development of an efficient Dantzig-Wolfe procedure for analysis of the total value-added maximization problem where both the initial and terminal states are specified; (3) the derivation of testable properties that will guarantee the attainability of a specified target state from a specified initial state of the system; (4) a formal comparison of some basic characteristics of total value growth and balanced value growth.     

冬季大体积混凝土施工的问题及其处理

本文在多年实践研究的基础上,针对冬季大体积混凝土施工方面的主要问题,提出了在混凝土中加入早强剂,在水泥中加入防冻剂,用矿渣水泥时采用蒸汽养护等解决办法。     

Tracking world trade and GDP in real time

This paper proposes a simple procedure for obtaining monthly assessments of short-run perspectives for quarterly world GDP and trade. It combines high-frequency information from emerging and advanced countries so as to explain quarterly national accounts variables through bridge models. The union of all bridge equations leads to our world bridge model (WBM). The WBM approach of this paper is new for two reasons: its equations combine traditional short-run bridging with theoretical level-relationships, and it is the first time that forecasts of world GDP and trade have been computed for both advanced and emerging countries on the basis of a real-time database of approximately 7000 time series. Although the performances of the equations that are searched automatically should be taken as a lower bound, our results show that the forecasting ability of the WBM is superior to the benchmark. Finally, our results confirm that the use of revised data leads to models’ forecasting performances being overstated significantly.     

我国城镇居民医疗保健的需求预测

运用灰色预测模型分别定量预测我国城镇居民在今后一段时期内医疗保健消费支出和城镇居民家庭平均每人全年消费性支出的增长情况,并做出相关分析和结论。     

Measures in DEA with an Application to the Malmquist Index

This paper shows the importance of goal vectors G in measuring and dealing with DEA inefficiencies. It emphasizes the advantages of the family of additive relative to the traditional oriented DEA models and shifts the primary emphasis to measuring inefficiency rather than efficiency. This new (raw) inefficiency measure RIN incorporates both the traditional DEA efficiency and the DEA slacks and provides the background for a new approach to the Malmquist Index. The final section points out some deficiencies in existing computational procedures for selecting G and calls for continued research on the selection process, as well as showing a role for G in returns to scale studies.