Predicting general election outcomes: campaigns and changing voter knowledge at the 2017 general election in England

There is a growing literature suggesting that the result for each constituency at British general elections can be predicted using ‘citizen forecasts’ obtained through voter surveys. This may be true for the majority of constituencies where the result at previous contests was a substantial majority for one party’s candidates: few ‘safe seats’ change hands. But is it true in the marginal constituencies, where elections are won and lost? Analysis of such ‘citizen forecast’ data for the Labour-Conservative marginal constituencies in 2017 indicates not. Although respondents were aware of the seats’ relative marginality and of general trends in party support during the campaign, they could not separate out those that were eventually lost by each party from those that were won again, even in seats where the elected party won comfortably.

     

Dose–response modelling for bivariate covariates with and without a spike at zero: theory and application to binary outcomes

In epidemiology and clinical research, there is often a proportion of unexposed individuals resulting in zero values of exposure, meaning that some individuals are not exposed and those exposed have some continuous distribution. Examples are smoking or alcohol consumption. We will call these variables with a spike at zero (SAZ). In this paper, we performed a systematic investigation on how to model covariates with a SAZ and derived theoretical odds ratio functions for selected bivariate distributions. We consider the bivariate normal and bivariate log normal distribution with a SAZ. Both confounding and effect modification can be elegantly described by formalizing the covariance matrix given the binary outcome variable Y. To model the effect of these variables, we use a procedure based on fractional polynomials first introduced by Royston and Altman (1994, Applied Statistics 43: 429–467) and modified for the SAZ situation (Royston and Sauerbrei, 2008, Multivariable model‐building: a pragmatic approach to regression analysis based on fractional polynomials for modelling continuous variables, Wiley; Becher et al., 2012, Biometrical Journal 54: 686–700). We aim to contribute to theory, practical procedures and application in epidemiology and clinical research to derive multivariable models for variables with a SAZ. As an example, we use data from a case–control study on lung cancer.     

Non-causality in bivariate binary time series

In this paper we develop a dynamic discrete-time bivariate probit model, in which the conditions for Granger non-causality can be represented and tested. The conditions for simultaneous independence are also worked out. The model is extended in order to allow for covariates, representing individual as well as time heterogeneity. The proposed model can be estimated by Maximum Likelihood. Granger non-causality and simultaneous independence can be tested by Likelihood Ratio or Wald tests. A specialized version of the model, aimed at testing Granger non-causality with bivariate discrete-time survival data is also discussed. The proposed tests are illustrated in two empirical applications.     

Predicting criminal recidivism using neural networks

Prediction of criminal recidivism has been extensively studied in criminology with a variety of statistical models. This article proposes the use of neural network (NN) models to address the problem of splitting the population into two groups — non-recidivists and eventual recidivists — based on a set of predictor variables. The results from an empirical study of the classification capabilities of NN on a well-known recidivism data set are presented and discussed in comparison with logistic regression. Analysis indicates that NN models are competitive with, and may offer some advantages over, traditional statistical models in this domain.     

2007年我国经济周期波动分析与主要宏观经济指标的预测——利用Logistic回归模型的分析

继2006年GDP增长率达到10.7%后,2007年一季度经济继续升温,国内外需求旺盛,工业结构调整步伐加快,能源等支撑条件改善,居民、企业和财政收入继续提高,人民生活水平进一步改善。然而,在消费、出口较快增长的同时,投资增速虽略显温和,但规模仍然较高,新开工项目居高不下,投资增速     

Identification and information in monotone binary models

This paper considers binary response models where errors are uncorrelated with a set of instrumental variables and are independent of a continuous regressor v$v$, conditional on all other variables. It is shown that these exclusion restrictions are not sufficient for identification and that additional identifying assumptions are needed. Such an assumption, introduced by Lewbel [Semiparametric qualitative response model estimation with unknown heteroskedasticity or instrumental variables. Journal of Econometrics 97, 145–177], is that the support of the continuous regressor is large, but we show that it significantly restricts the class of binary phenomena which can be analysed. We propose an alternative additional assumption under which β$\beta$ remains just identified and the estimation unchanged. This alternative assumption does not impose specific restrictions on the data, which broadens the scope of the estimation method in empirical work. The semiparametric efficiency bound of the model is also established and an existing estimator is shown to achieve that bound. The efficient estimator uses a plug-in density estimate. It is shown that plugging in the true density rather than an estimate is inefficient. Extensions to ordered choice models are provided.     

Reference group influence on binary choices dynamics

The recent literature has analyzed binary choices dynamics providing interesting results. Most of these contributions consider interactions within a single group. Nevertheless, in some situations the interaction takes place not only within a single group but also between different groups. In this paper, we investigate the choice dynamics when considering two populations where one serves as a reference group. Considering this influence effect enriches the dynamics. Although the structurally stable resulting dynamics are attracting cycles only, with any positive integer period, the reference group makes the dynamics of the influenced population much more complex. We considered both the possibility that the reference group has the same or the opposite attitude toward the distribution over the choices. We show how the dynamics and the bifurcation structure are modified under the influence of the reference group. Our results illustrate how the propensity to switch choices in the reference groups may, indirectly, affect choices in the first group.     

Predicting turning points in the housing market

We identify leading indicators of changes in the housing market and compare their performance in predicting turning points. Being able to predict turning points is of importance to the home building industry, homeowners, and makers of housing policy. Our leading indicators include the Wells Fargo/NAHB Housing Market Index, two of its forward looking components, and an index of consumer sentiment regarding purchasing a home. Our comparison tests include Granger causality and a Bayesian predictor of the probability of a turning point. We find that the measure of consumer sentiment performs relatively well compared to the HMI in predicting home permits, housing starts, and new home sales.     

强化大学英语听力训练中的预测能力

本文通过阐明预测能力在听力理解中重要作用,指出教师在课堂教学中,应重视对学生预测能力的培养,并分析了它的概念,影响预测能力的因素及时机问题,最后提出了实际训练师使用的方法,帮助学生提高英语听力水平,促进英语听力课堂的效果。     

Evaluating Pseudo-R2's for binary probit models

Many applied researchers of limited dependent variable models found it disadvantageous that a widely accepted Pseudo-R² does not exist for this type of estimation. The paper provides guidance for researchers in choosing a Pseudo-R² in the binary probit case. The starting point is that R² is best understood in the ordinary least squares (OLS) case with continuous data, which is chosen as the reference situation. It is considered which Pseudo-R² is best able to mimic the OLS-R². The results are surprisingly clear: a measure suggested by McKelvey-Zavoina performs the best under our criterion. However, in the more likely case of low Pseudo-R²'s, a normalization of a measure proposed by Aldrich-Nelson which we suggest is almost as good as the McKelvey-Zavoina, and is in general easier to calculate. We also show that if the underlying R² is predicted using cubic regressions given the Pseudo-R², all measures perform much better.     

Bounds for best response functions in binary games

This paper studies the identification of best response functions in binary games without making strong parametric assumptions about the payoffs. The best response function gives the utility maximizing response to a decision of the other players. This is analogous to the response function in the treatment-response literature, taking the decision of the other players as the treatment, except that the best response function has additional structure implied by the associated utility maximization problem. Further, the relationship between the data and the best response function is not the same as the relationship between the data and the response function in the treatment-response literature. We focus especially on the case of a complete information entry game with two firms. We also discuss the case of an entry game with many firms, non-entry games, and incomplete information. Our analysis of the entry game is based on the observation of realized entry decisions, which we then link to the best response functions under various assumptions including those concerning the level of rationality of the firms, including the assumption of Nash equilibrium play, the symmetry of the payoffs between firms, and whether mixed strategies are admitted.     

A binary tree algorithm on change points detection

In this paper, an algorithm using binary trees is developed to detect the change points of a data set in which the data are assumed to be normally distributed. Usual BIC-type criteria are considered in the binary searching procedures when the number of change points is unknown. The algorithm is also extended to the switching regression models. Simulation study confirms that our algorithm is efficient compared with the ML-method. A real data example also verifies that the proposed procedure is appropriate.     

Prediction from binary choice models: A note

The results of Westin (1974) can be obtained, to a satisfactory approximation, without recourse to numerical integration. We also show how to attach a standard error to his point estimates.     

Identification of binary choice models with social interactions

This paper provides a set of results on the econometric identifiability of binary choice models with social interactions. Our analysis moves beyond parametric identification results that have been obtained in the literature to consider the identifiability of model parameters when the distribution of random payoff terms is unknown. Further, we consider how identification is affected by the presence of unobservable payoff terms of various types as well as identification in the presence of certain forms of endogenous group membership. Our results suggest that at least partial identification may be achieved under assumptions that in certain contexts may be plausible.     

A class of non binary unequally replicatedE-optimal designs

In a situation where the given set of parameters (b, k andv) precludes the existence of any known optimal block designs, but an optimal block design is known to exist with parametersb, k andv*>v, a new design is shown to be useful. This (b, k, v) design is obtained from the (b, k, v*) optimal design by collapsing the classes of a suitable paritition of the treatment set (of the latter design) to treatments (of the former). We call the new design a quotient of the original design. Although the quotient is non binary and unequally replicated, it turns out to beE-optimal within the class of all proper and connected designs withb, k andv, provided the replication number of the optimal design we start with is not too large.     

构建一种地方财政收入的预测模型

地方财政收入的分析和预测是有关部门制定合理的财政政策和财政预算、强化地方财政收入监督管理的有效途径.本文试图运用国际经济理论和分析方法,建立一种地方财政收入的预测模型(季节性ARIMA),以期对地方财政收入做出准确、有效的分析和预测.     

构建一种地方财政收入的预测模型

地方财政收入的分析和预测是有关部门制定合理的财政政策和财政预算、强化地方财政收入监督管理的有效途径。本文试图运用国际经济理论和分析方法,建立一种地方财政收入的预测模型(季节性ARIMA),以期对地方财政收入做出准确、有效的分析和预测。     

Predicting the geo-temporal variations of crime and disorder

Traditional police boundaries—precincts, patrol districts, etc.—often fail to reflect the true distribution of criminal activity and thus do little to assist in the optimal allocation of police resources. This paper introduces methods for crime incident forecasting by focusing upon geographical areas of concern that transcend traditional policing boundaries. The computerised procedure utilises a geographical crime incidence-scanning algorithm to identify clusters with relatively high levels of crime (hot spots). These clusters provide sufficient data for training artificial neural networks (ANNs) capable of modelling trends within them. The approach to ANN specification and estimation is enhanced by application of a novel and noteworthy approach, the Gamma test (GT).     

Predicting bankruptcy in the Texas nursing facility industry

Approximately 50% of nursing facilities in Texas petitioned for bankruptcy during the 1998–2004 period. Using a logit regression model tested for robustness, we find nursing facilities that are profit-seekers, chain members, pay higher than average wage rates, accept more intensive-care residents and obtain a larger than average portion of their funding from public sources are highly vulnerable to negative changes in regulatory policy decisions on Medicare and Medicaid reimbursement. Larger facilities having higher than average occupancy rates and quality of care are less susceptible to adverse decisions. The model correctly classifies a facility as either bankrupt or solvent in about 75% of cases. We also examine the duration of bankruptcy using accelerated failure-time models. It appears that the duration of bankruptcy depends on location, out-of-state ownership, length of ownership, volume of resident days supplied, total cost and proportion of revenues from Medicaid.