Gay tourism in a sun and beach destination

Gay tourism is seen as an attractive business opportunity for many destinations. However, there is a lack of research at identifying the resources necessary for success in this type of tourism. This work is aimed at filling this gap by using the premises of the resource based view and transfers them to the analysis of territories to identify the valuable resources that are required for a sun and beach destination to attract gay tourists. To this end, this study has focused on a gay tourism sub niche, tourists lodged in gay-exclusive resorts in Gran Canaria. In order to confirm the validity of this approach, the relationship between the satisfaction of gay tourists and the condition of the valuable resources was studied by means of a robust statistical new method, namely Bayesian model averaging. That method permits the inclusion of uncertainty in the theoretical models that determine destination competitiveness, thus reducing many of the problems that arise in the application of the more conventional statistical methods in this type of analysis.     

基于动态贝叶斯网络的邻近下穿隧道深基坑施工风险分析

为了减小邻近既有下穿隧道深基坑施工风险及灾害损失,科学预防施工安全事故,提出了一种基于动态贝叶斯网络（DBN）的深基坑施工风险分析模型。首先,运用BWM（best worst method）确定准则的权重;其次,基于关联规则挖掘风险因素间的相互关系,并以此构建DBN结构模型;最后,以新建厦门北站地下一层社会连廊深基坑工程为例,对提出的方法进行有效性和适用性检验。结果表明：基坑围护结构的安全度在静态被评为“较高”和“极高”的概率分别为34.6%和36.1%,且此结果随着输入风险证据发生动态变化,运用反向推理也能迅速找出围护桩渗水风险;提出的模型能明确邻近既有下穿隧道深基坑施工风险传递过程中的关键风险点,并能进行动态风险预测以及事故后致因诊断,从而实现邻近既有下穿隧道深基坑施工风险的动态管控。所提出的优化DBN模型对工前风险评估、先验分析和风险诊断有较好的适用性和较高的准确性,可为邻近既有隧道深基坑施工过程中的安全管控提供有效的决策支持,大幅提高风险控制效率。     

Forecasting the UK economy with a medium-scale Bayesian VAR

We estimate a Bayesian VAR (BVAR) for the UK economy and assess its performance in forecasting GDP growth and CPI inflation in real time relative to forecasts from COMPASS, the Bank of England’s DSGE model, and other benchmarks. We find that the BVAR outperformed COMPASS when forecasting both GDP and its expenditure components. In contrast, their performances when forecasting CPI were similar. We also find that the BVAR density forecasts outperformed those of COMPASS, despite under-predicting inflation at most forecast horizons. Both models over-predicted GDP growth at all forecast horizons, but the issue was less pronounced in the BVAR. The BVAR’s point and density forecast performances are also comparable to those of a Bank of England in-house statistical suite for both GDP and CPI inflation, as well as to the official Inflation Report projections. Our results are broadly consistent with the findings of similar studies for other advanced economies.     

Heteroskedasticity-consistent covariance matrix estimators for spatial autoregressive models

In the presence of heteroskedasticity, conventional test statistics based on the ordinary least squares (OLS) estimator lead to incorrect inference results for the linear regression model. Given that heteroskedasticity is common in cross-sectional data, the test statistics based on various forms of heteroskedasticity-consistent covariance matrices (HCCMs) have been developed in the literature. In contrast to the standard linear regression model, heteroskedasticity is a more serious problem for spatial econometric models, generally causing inconsistent extremum estimators of model coefficients. This paper investigates the finite sample properties of the heteroskedasticity-robust generalized method of moments estimator (RGMME) for a spatial econometric model with an unknown form of heteroskedasticity. In particular, it develops various HCCM-type corrections to improve the finite sample properties of the RGMME and the conventional Wald test. The Monte Carlo results indicate that the HCCM-type corrections can produce more accurate results for inference on model parameters and the impact effects estimates in small samples.     

Understanding how heterogeneous agents affect Principal's returns: Perspectives from short-termism and Bayesian learning

We consider a general framework of optimal contract design under the heterogeneity and short-termism of agents. Our research shows that the optimal contract must weigh the agent's information rent, incentive cost, and benefit to overcome the contract's adverse selection and moral hazards. Agents with higher moral levels were more likely to choose higher effort and lower manipulation. Simultaneously, the principal offers lower incentives and receives more significant payoff. We also extend our model to investigate the benefits of Bayesian learning. Furthermore, we compare the principal's returns in general and learning models and find that the learning contract can bring more profit to the principal.     

Term structure and the estimated monetary policy rule in the Eurozone

In this paper we estimate a standard version of the New Keynesian Monetary (NKM) model augmented with term structure in order to analyze two issues. First, we analyze the effect of introducing an explicit term structure channel in the NKM model on the estimated parameter values of the model, with special emphasis on the interest rate smoothing parameter using data for the Eurozone. Second, we study the ability of the model to reproduce some stylized facts such as highly persistent dynamics, the weak comovement between economic activity and inflation, and the positive, strong comovement between interest rates observed in actual Eurozone data. The Sect. 3 implemented is a classical structural method based on the indirect inference principle. We are grateful to Eduardo Ley, two anonymous referees and seminar participants at the XXXI Simposio de Análisis Económico (Oviedo, Spain) and Bank of Spain for their useful comments. Financial support from Ministerio de Ciencia y Tecnología and Universidad del País Vasco (Spain) and Fundación Séneca through projects SEJ2004-04811/ECON, 9/UPV00035.321-13511/2001 and I02937/PHCS/05, respectively, is gratefully acknowledged. The first author also thanks Fundación Ramón Areces for financial support.     

Open economy forecasting with a DSGE-VAR: Head to head with the RBNZ published forecasts

We construct a DSGE-VAR model for competing head to head with the long history of published forecasts of the Reserve Bank of New Zealand. We also construct a Bayesian VAR model with a Minnesota prior for forecast comparison. The DSGE-VAR model combines a structural DSGE model with a statistical VAR model based on the in-sample fit over the majority of New Zealand’s inflation-targeting period. We evaluate the real-time out-of-sample forecasting performance of the DSGE-VAR model, and show that the forecasts from the DSGE-VAR are competitive with the Reserve Bank of New Zealand’s published, judgmentally-adjusted forecasts. The Bayesian VAR model with a Minnesota prior also provides a competitive forecasting performance, and generally, with a few exceptions, out-performs both the DSGE-VAR and the Reserve Bank’s own forecasts.     

电网故障模型中贝叶斯网络和DS证据理论的应用

由于电网的复杂性和多变性,导致电网故障中存在信息不确定性问题,这就需要依靠贝叶斯网络和DS证据理论的自动生成方法。文章主要描述了这两种方法的原理,讨论了贝叶斯网络和DS证据理论在电网故障模型领域应用的可能方式和情况,并运用一些实例证明了这种方法的可靠性。     

Professional forecasters and real-time forecasting with a DSGE model

This paper analyses the real-time forecasting performance of the New Keynesian DSGE model of Galí, Smets and Wouters (2012), estimated on euro area data. It investigates the extent to which the inclusion of forecasts of inflation, GDP growth and unemployment by professional forecasters improve the forecasting performance. We consider two approaches for conditioning on such information. Under the “noise” approach, the mean professional forecasts are assumed to be noisy indicators of the rational expectations forecasts implied by the DSGE model. Under the “news” approach, it is assumed that the forecasts reveal the presence of expected future structural shocks in line with those estimated in the past. The forecasts of the DSGE model are compared with those from a Bayesian VAR model, an AR(1) model, a sample mean and a random walk.