An empirical note about estimation and forecasting Latin American Forex returns volatility: the role of long memory and random level shifts components

Portuguese Economic Journal - A set of RLS-type models with ARMA and ARFIMA dynamics is estimated and compared in a forecasting exercise with ARFIMA, GARCH and FIGARCH models. It is an extension of...     

An application of two-stage quantile regression to insurance ratemaking

Two-part models based on generalized linear models are widely used in insurance rate-making for predicting the expected loss. This paper explores an alternative method based on quantile regression which provides more information about the loss distribution and can be also used for insurance underwriting. Quantile regression allows estimating the aggregate claim cost quantiles of a policy given a number of covariates. To do so, a first stage is required, which involves fitting a logistic regression to estimate, for every policy, the probability of submitting at least one claim. The proposed methodology is illustrated using a portfolio of car insurance policies. This application shows that the results of the quantile regression are highly dependent on the claim probability estimates. The paper also examines an application of quantile regression to premium safety loading calculation, the so-called Quantile Premium Principle (QPP). We propose a premium calculation based on quantile regression which inherits the good properties of the quantiles. Using the same insurance portfolio data-set, we find that the QPP captures the riskiness of the policies better than the expected value premium principle.     

CAUSAL INFERENCE ON EDUCATION POLICIES: A SURVEY OF EMPIRICAL STUDIES USING PISA,TIMSS AND PIRLS

The identification of the causal effects of educational policies is the top priority in recent education economics literature. As a result, a shift can be observed in the strategies of empirical studies. They have moved from the use of standard multivariate statistical methods, which identify correlations or associations between variables only, to more complex econometric strategies, which can help to identify causal relationships. However, exogenous variations in databases have to be identified in order to apply causal inference techniques. This is a far from straightforward task. For this reason, this paper provides an extensive and comprehensive overview of the literature using quasi‐experimental techniques applied to three well‐known international large‐scale comparative assessments, such as PISA, PIRLS or TIMSS, over the period 2004–2016. In particular, we review empirical studies employing instrumental variables, regression discontinuity designs, difference in differences and propensity score matching to the above databases. Additionally, we provide a detailed summary of estimation strategies, issues treated and profitability in terms of the quality of publications to encourage further potential evaluations. The paper concludes with some operational recommendations for prospective researchers in the field.     

Unraveling determinants of inferred and stated attribute nonattendance: Effects on farmers’ willingness to accept to join agri-environmental schemes

Attribute nonattendance (ANA) has received very little attention in the context of willingness to accept (WTA), although an increasing number of studies analyze the preferences of ecosystem service providers toward incentive-based schemes. We add to the understanding of ANA behavior by analyzing stated and inferred ANA in a choice experiment investigating farmers’ WTA for participating in agri-environmental schemes (AES) in southern Spain. We use mixed logit models, following Hess and Hensher for the inferred ANA approach. Evidence is found of ANA behavior for both stated and inferred approaches, with models accounting for ANA clearly outperforming those that do not account for it; however, we produce no conclusive results as to which ANA approach is best. WTA estimates are only moderately affected, which to some extent is consistent with the low level of non-attendance found for the monetary attribute. Stated and inferred approaches show very similar WTA estimates. Additionally, we investigate sources of observed heterogeneity related to ANA behavior by using a sequence of bivariate probit models for each attribute. Overall, our results hint at a positive relationship between ease of scheme adoption and nonattendance to attributes. However, further research is still needed in this field.     

Evolution of indigenous tourism among the Lacandon of Chiapas: an application of Weaver’s model

ABSTRACT

In this paper, Weaver’s six-stage indigenous tourism model is applied to the Lacandon Maya (Hach Winik) of Chiapas, Mexico. Based on a comprehensive review of the anthropological and historical literature on this indigenous group, combined with longitudinal ethnographic and collaborative research performed with tourism entrepreneurs, the Lacandon tourism experience is assessed from the pre-European period until present. By analysing a case study of indigenous tourism in Mexico, a developing country in another geographical region and with a different colonial past, this work supplements Weavers’ perspective. The results show that the fourth and fifth stages of Weaver’s model coincide in this case study, while the sixth stage is still incomplete. Although the Lacandon case has its peculiarities and bearing in mind that several different factors should be considered in the Latin American context, the model proves to be an interesting tool for indigenous tourism analysis in developing countries.     

Trauma epidemiology in Puerto Rico: in-hospital morbidity and mortality from 2002 to 2011

This study aimed to describe the distribution of injury mechanisms and to assess the impact of those mechanisms on the morbidity and mortality of trauma. All patients admitted to Puerto Rico Trauma Hospital (2002–2011) for road-traffic collisions (RTCs, 5,371), gunshot wounds (GSWs, 2,946), falls (2,319), pedestrian accidents (1,652), and stab wounds (SWs, 1,073) were selected. Gunshot victims were 1.19 (95%CI: 1.07–1.33) times as likely as road-traffic victims to have an ISS ≥25. Pedestrians were 1.76 (95%CI: 1.49–2.09) times more likely to have a GCS ≤8 than road-traffic victims were. The risk of dying was 2.64 (95%CI: 2.20–3.16) times higher for gunshot victims and 1.51 (95%CI: 1.23–1.86) times higher for pedestrians compared to patients who had had RTCs. Gunshot victims and pedestrians had the worst clinical outcomes. Accordingly, these patients should receive the most aggressive clinical management. Furthermore, it is imperative to develop public health campaigns on trauma prevention.     

A spatial approach to the FDI-growth nexus in Spain: Dealing with the headquarters effect

This paper examines the effects of inward FDI on economic growth across the Spanish regions over the period 1996–2013, paying particular attention to the ‘headquarters effect’, namely that FDI is not always registered where it is effectively made but in the region in which the firm’s headquarters is located. By estimating a panel Spatial Durbin Model to allow for the detection of spatial spillovers, two main findings are reported. First, FDI does foster economic growth. Second, only when the headquarters effect is properly addressed do spatial spillovers arise. Hence, this effect is masking the impact of regional FDI spillovers on growth, which affects the reliability of the results and, consequently, FDI policies choice. Importantly, the results are: a) robust to the way of computing the headquarters effect; b) independent of the spatial weight matrix specification; and c) confirmed when splitting FDI into different industrial and service branches.     

A Stochastic Model with Penalized Coefficients for Spatial Price Comparisons: An Application to Regional Price Indexes in Italy

This paper focuses on a new strand of research that uses stochastic approach for making spatial price comparisons. We propose a novel method to account for the presence of spatial dependencies in consumer prices and consequently in price indexes by imposing penalization conditions on the estimation of traditional CPD models leading to the spatially-penalized country-product-dummy (SP-CPD) model. The paper proposes an appropriate estimation strategy, which enables us to simultaneously estimate all the parameters in the model, including the smoothing parameter of the penalization term instead of determining it externally. In order to estimate spatial price indexes for areas lacking in price data, we suggest applying the kriging methodology to the price indexes obtained from the SP-CPD model. This new approach is applied to official Italian CPI data for constructing regional spatial price indexes for 2014. The results show that price levels are higher in the Northern-Central regions than in the South.