RANDOM COEFFICIENT MODELS: THEORY AND APPLICATIONS

Abstract. This paper provides an overview of the rationale behind, and the implementation, and uses of, the random coefficient approach to econometric modelling. A simple random coefficient model is presented, and methods for estimating, testing, and validating such a model are described. A more general model is then presented. The general model is shown to include several fixed-coefficient models as special cases and can be estimated incorporating a variety of judgements concerning simplification. Finally, the paper reviews recent applications of random coefficient estimation.     

Forecasting television ratings

Despite the state of flux in media today, television remains the dominant player globally for advertising spending. Since television advertising time is purchased on the basis of projected future ratings, and ad costs have skyrocketed, there is increasingly pressure to forecast television ratings accurately. The forecasting methods that have been used in the past are not generally very reliable, and many have not been validated; also, even more distressingly, none have been tested in today’s multichannel environment. In this study we compare eight different forecasting models, ranging from a naïve empirical method to a state-of-the-art Bayesian model-averaging method. Our data come from a recent time period, namely 2004-2008, in a market with over 70 channels, making the data more typical of today’s viewing environment. The simple models that are commonly used in industry do not forecast as well as any econometric models. Furthermore, time series methods are not applicable, as many programs are broadcast only once. However, we find that a relatively straightforward random effects regression model often performs as well as more sophisticated Bayesian models in out-of-sample forecasting. Finally, we demonstrate that making improvements in ratings forecasts could save the television industry between $250 and $586 million per year.     

A structural analysis of the correlated random coefficient wage regression model

We estimate a dynamic programming model of schooling decisions in which the log wage regression function is set within a correlated random coefficient model. We show that estimates of the dynamic programming model can be used to obtain a number of treatment effects, including the local average treatment effect (LATE). However, unlike LATE parameters obtained in a standard IV framework, our LATE estimates are obtained without imposing separability between individual specific heterogeneity and schooling choices and are therefore not subject to a “monotonicity” restriction. We find that returns to schooling are characterized by a high degree of dispersion across individuals.     

Random coefficient state-space model: Estimation and performance in M3–M4 competitions

The random coefficient state-space model was first introduced by McKenzie and Gardner (2010). This model is a stochastic combination of simple and double exponential smoothing, a desirable feature for time-series forecasting. This paper provides a simple method to estimate the random coefficient state-space model parameters by exploiting the link between the model’s autocovariance and the Kalman filter. A simulation exercise shows that the proposed estimator has good finite-sample properties. This paper also evaluates the model’s forecasting performance in large-scale empirical applications, which is remarkable. Indeed, this model outperforms all competing (not-combined) benchmarks when using the yearly data from the M3 competition dataset. Furthermore, employing the yearly data from the M4 competition, it continues to beat its competitors, with a performance comparable to that of the Theta method. The predictive performance is assessed using both the MASE/sMAPE metrics and the Model Confidence Set procedure.     

Modelling optimal strategies for the allocation of wealth in multicurrency investments

This paper analyzes rates of return on financial assets denominated in five major currencies and provides a framework for the determination of optimal strategies for the allocation of wealth in multicurrency investments. Three models are estimated: a univariate autoregressive conditional heteroskedasticity (ARCH) model, an extended ARCH model using the random coefficient (RC) procedure, and a pure RC model. A comparison of the forecasts of these models with those generated by a random walk model demonstrates that forecasts based on the RC/extended ARCH procedure are superior to those based on the random walk model and those based on direct ARCH estimation. These results could be useful for both international investors for the allocation of their wealth among fixed-income investment securities and central banks for the management of their external reserve assets.     

大学生创新实验计划实施中形成的教学模式和存在的问题

经过多年的试点和推广,大学生创新性实验计划的实施已经形成了一定教学模式,这种模式为大学生提供了项目申报、项目研究和项目评价等一系列支持和服务,也为大学生组建科研团队、参与科学研究提供了机会。但是,由于推广时间短,刚刚形成的教学模式还存在项目课题设置不合理、缺乏教学科研平台、对现有教学资源利用不足、评价体系不够科学有效等问题。     

Nonparametric estimation and testing of fixed effects panel data models

In this paper we consider the problem of estimating nonparametric panel data models with fixed effects. We introduce an iterative nonparametric kernel estimator. We also extend the estimation method to the case of a semiparametric partially linear fixed effects model. To determine whether a parametric, semiparametric or nonparametric model is appropriate, we propose test statistics to test between the three alternatives in practice. We further propose a test statistic for testing the null hypothesis of random effects against fixed effects in a nonparametric panel data regression model. Simulations are used to examine the finite sample performance of the proposed estimators and the test statistics.     

The Influence of Geography on the Spatial Agglomeration of Production in the European Union

Abstract

Small regions and towns often experience problems such as high out-migration and unemployment. In these situations there is an urgent need to do something positive. Infrastructure development projects, partially financed by the national government, are a popular suggestion. We ponder the relevance of these actions in this paper in a context of project evaluation. We introduce computable general equilibrium (CGE) modelling as a complement of the other quantitative evaluation techniques. We will use the town of Lapua in South Ostrobothnia, Finland, which is planning to implement two large-scale infrastructure projects, as an example. Our simulation tool is the Finnish multi-sector and interregional CGE RegFin model. We consider the regional short-run effects of the construction phase, the long-run effects of new business activity and the so-called secondary effects based on the commuting and trade patterns of the households.     

基于改进的灰色模型对货物周转量的预测——以云南省货物周转量为例

现代物流业作为中国现代服务业的重要组成部分,对提高国民经济运营质量、优化经济流程、调整经济结构、扩大内需、增进社会福利等都具有全局性的积极影响。它也逐渐被认为是继降低物资消耗、提高劳动生产率之后的又一重要的利润源泉。货物周转量的衡量与计算比较容易,而货物周转量与物流量之间的关系,使得对于物流周转量的预测显得十分必要。     

Testing for heteroskedasticity and serial correlation in a random effects panel data model

This paper considers a panel data regression model with heteroskedastic as well as serially correlated disturbances, and derives a joint LM test for homoskedasticity and no first order serial correlation. The restricted model is the standard random individual error component model. It also derives a conditional LM test for homoskedasticity given serial correlation, as well as, a conditional LM test for no first order serial correlation given heteroskedasticity, all in the context of a random effects panel data model. Monte Carlo results show that these tests along with their likelihood ratio alternatives have good size and power under various forms of heteroskedasticity including exponential and quadratic functional forms.     

Strong Consistency of the Maximum Likelihood Estimator in Generalized Linear and Nonlinear Mixed-Effects Models

Generalized linear and nonlinear mixed-effects models are used extensively in biomedical, social, and agricultural sciences. The statistical analysis of these models is based on the asymptotic properties of the maximum likelihood estimator. However, it is usually assumed that the maximum likelihood estimator is consistent, without providing a proof. A rigorous proof of the consistency by verifying conditions from existing results can be very difficult due to the integrated likelihood. In this paper, we present some easily verifiable conditions for the strong consistency of the maximum likelihood estimator in generalized linear and nonlinear mixed-effects models. Based on this result, we prove that the maximum likelihood estimator is consistent for some frequently used models such as mixed-effects logistic regression models and growth curve models.     

层次分析法在图书馆图书效益评价中的应用

本文针对图书馆图书效益评价中的指标确立和合理量化问题,根据图书的借阅状况建立相应图书效益指标评价体系,应用层次分析法构建综合评价模型,给出综合评价分值计算公式,举例说明图书效益评价作用,以便指导管理部门对图书投资和采购,提高图书的实际效益。     

高校教学质量评价指标体系分析

教学质量评价是高校教学管理过程中必不可少的重要方面。文章根据教学质量评价原则建立一套评价教师教学质量的指标体系,并采用定性与定量相结合的模糊综合评价方法对教师教学质量进行综合评价。目的是为了探索一种科学合理的教学质量评价体系,使教学质量评价工作变得更科学、合理、公正。     

中国金融发展与全要素生产率——基于时间序列的经验证据

全要素生产率问题是研究中国经济可持续增长的核心问题。本文利用界限检验法、基于ARDL法的协整系数估计、向量误差修正模型及其Granger因果关系检验等计量技术,考察了全要素生产率与金融发展等变量的关系。实证结果表明,全要素生产率与金融发展、外商直接投资、经济自由度三个变量存在长期均衡关系,并且从长期来看,金融发展、外商直接投资、经济自由度皆是全要素生产率变动的原因;而从短期来看,只有经济自由度才是全要素生产率变动的Granger原因。