Bootstrap Determination of the Co‐Integration Rank in Vector Autoregressive Models

This paper discusses a consistent bootstrap implementation of the likelihood ratio (LR) co‐integration rank test and associated sequential rank determination procedure of Johansen (1996). The bootstrap samples are constructed using the restricted parameter estimates of the underlying vector autoregressive (VAR) model that obtain under the reduced rank null hypothesis. A full asymptotic theory is provided that shows that, unlike the bootstrap procedure in Swensen (2006) where a combination of unrestricted and restricted estimates from the VAR model is used, the resulting bootstrap data are I(1) and satisfy the null co‐integration rank, regardless of the true rank. This ensures that the bootstrap LR test is asymptotically correctly sized and that the probability that the bootstrap sequential procedure selects a rank smaller than the true rank converges to zero. Monte Carlo evidence suggests that our bootstrap procedures work very well in practice.     

The price for information about probabilities and its relation with risk and ambiguity

In this article, ambiguity attitude is measured through the maximum price a decision maker is willing to pay to know the probability of an event. Two problems are examined in which the decision maker faces an act: in one case, buying information implies playing a lottery, while, in the other case, buying information gives also the option to avoid playing the lottery. In both decision settings, relying on the Choquet expected utility model, we study how the decision maker??s risk and ambiguity attitudes affect the reservation price for ambiguity resolution. These effects are analyzed for different levels of ambiguity of the act. Operating instructions for the elicitation of the reservation price for ambiguity resolution in an experimental setting are provided at the end of the article.     

The TRIPs agreement and technological innovation

In this paper we attempt to evaluate the possible spill-over of the international agreement on Trade-Related Aspects of Intellectual Property rights (TRIPs for short), underwritten in 1994, regarding economic growth for both wealthy and developing countries. We find that the TRIPs convention has prompted, at the same time, innovation in developing countries and a rise in the per capita income for all the economies involved in international commerce. As a by-product of our research we find that, despite the strong growth of resident patents application after 1995 (the year in which TRIPs came into force), most of the increase in the gross domestic product per capita in developing countries is attributable to the international transfer of technologies, via foreign direct investments.     

Are there electoral cycles of emigration? An empirical investigation based on European data

Economic literature suggests that economic factors and the availability of amenities act as determinants of migration choices together with socio‐demographic factors. Migration has also been found to be the consequence of political instability. This article argues that specific political events, i.e., democratic elections, may be linked to migration flows. By using European data over the 1999‐2012 time period, our system GMM estimations reveal that there is an emigration political cycle across European democracies and across the young democracies of Central and Eastern European countries. We observe that regular elections tend to diminish emigration ratios, whereas endogenous elections have the opposite effect. These results suggest special challenges for governments and oppositions, which are also discussed.     

Strategies of Italian Firms in Romania: Evidence from Selected Case Studies

The aim of this paper is to analyze the internationalization of Italian firms in Romania, operating in the sectors of footwear, furniture and industrial refrigeration. After describing and quantifying the internationalization process between Veneto and Romania, we discuss for each of the sectors, the changes that occurred in the organisation of the production processes within the firms, and particularly how such processes have been fragmented. This article draws on numerous case studies, posits different models of value chain governance, and discusses their implications for regional development and sustainability.     

On the Use of Shadow Prices for Sustainable Well-Being Measurement

Social Indicators Research - This paper tries to answer the following research question: can multidimensional concepts like well-being or sustainability be measured by using a single metric such as...     

Choosing Aggregation Rules for Composite Indicators

From a formal point of view, a composite indicator is an aggregate of all dimensions, objectives, individual indicators and variables used for its construction. This implies that what defines a composite indicator is the set of properties underlying its mathematical aggregation convention. In this article, I try to revise the theoretical debate on aggregation rules by looking at contributions from both voting theory and multi-criteria decision analysis. This cross-fertilization helps in clarifying many ambiguous issues still present in the literature and allows discussing the key assumptions that may change the evaluation of an aggregation rule easily, when a composite indicator has to be constructed.     

Behavioural Addictions in Adolescents and Young Adults: Results from a Prevalence Study

Our study aims to assess the prevalence of behavioural addictions in an adolescent population, evaluating the effects of gender and age, and to assess the correlations among different behavioural addictions. 2853 high school students were assessed in order to evaluate the prevalence of behavioural addictions such as Pathological Gambling (PG), Compulsive Buying (CB), Exercise Addiction (EA), Internet Addiction (IA), and Work Addiction (WA), in a population of Italian adolescents. The South Oaks Gambling Screen-Revised Adolescent (SOGS-RA), the Compulsive Buying Scale (CBS), the Exercise Addiction Inventory (EAI), the Internet Addiction Test (IAT), and the Work Addiction Risk Test (WART), were compiled anonymously by the students. Overall prevalence was 7.0% for PG, 11.3% for CB, 1.2% for IA, 7.6% for WA, 8.5% for EA. PG and EA were more common among boys, while gender had no effect on the other conditions. CB was more common among younger (<18 years old) students. The scores of all of these scales were significantly correlated. The strong correlation among different addictive behaviours is in line with the hypothesis of a common psychopathological dimension underlying these phenomena. Further studies are needed to assess personality traits and other clinical disorders associated with these problems behaviours.     

Lag Length Selection for Unit Root Tests in the Presence of Nonstationary Volatility

A number of recent papers have focused on the problem of testing for a unit root in the case where the driving shocks may be unconditionally heteroskedastic. These papers have, however, taken the lag length in the unit root test regression to be a deterministic function of the sample size, rather than data-determined, the latter being standard empirical practice. We investigate the finite sample impact of unconditional heteroskedasticity on conventional data-dependent lag selection methods in augmented Dickey–Fuller type regressions and propose new lag selection criteria which allow for unconditional heteroskedasticity. Standard lag selection methods are shown to have a tendency to over-fit the lag order under heteroskedasticity, resulting in significant power losses in the (wild bootstrap implementation of the) augmented Dickey–Fuller tests under the alternative. The proposed new lag selection criteria are shown to avoid this problem yet deliver unit root tests with almost identical finite sample properties as the corresponding tests based on conventional lag selection when the shocks are homoskedastic.     

Bayesian adaptive dose‐escalation designs for simultaneously estimating the optimal and maximum safe dose based on safety and efficacy

The main purpose of dose‐escalation trials is to identify the dose(s) that is/are safe and efficacious for further investigations in later studies. In this paper, we introduce dose‐escalation designs that incorporate both the dose‐limiting events and dose‐limiting toxicities (DLTs) and indicative responses of efficacy into the procedure. A flexible nonparametric model is used for modelling the continuous efficacy responses while a logistic model is used for the binary DLTs. Escalation decisions are based on the combination of the probabilities of DLTs and expected efficacy through a gain function. On the basis of this setup, we then introduce 2 types of Bayesian adaptive dose‐escalation strategies. The first type of procedures, called “single objective,” aims to identify and recommend a single dose, either the maximum tolerated dose, the highest dose that is considered as safe, or the optimal dose, a safe dose that gives optimum benefit risk. The second type, called “dual objective,” aims to jointly estimate both the maximum tolerated dose and the optimal dose accurately. The recommended doses obtained under these dose‐escalation procedures provide information about the safety and efficacy profile of the novel drug to facilitate later studies. We evaluate different strategies via simulations based on an example constructed from a real trial on patients with type 2 diabetes, and the use of stopping rules is assessed. We find that the nonparametric model estimates the efficacy responses well for different underlying true shapes. The dual‐objective designs give better results in terms of identifying the 2 real target doses compared to the single‐objective designs.