On uniform convergence of undiscounted optimal programs in the Mitra–Wan forestry model: The strictly concave case

In this paper, we show that the 1986 Mitra–Wan result establishing asymptotic convergence of maximal programs to the unique golden-rule forest in the case of undiscounted, strictly concave felicity functions can be strengthened, in the same setting, to the  uniform  asymptotic convergence of  optimal  programs to the unique golden-rule forest. We work with a notationally reformulated version of the model that may have independent interest.     

“To Own or Not to Own?” A Study on the Determinants and Consequences of Alternative Intellectual Property Rights Arrangements in Crowdsourcing for Innovation Contests

Firms are increasingly engaging in crowdsourcing for innovation to access new knowledge beyond their boundaries; however, scholars are no closer to understanding what guides seeker firms in deciding the level at which to acquire rights from solvers and the effect that this decision has on the performance of crowdsourcing contests. Integrating property rights theory and the problem‐solving perspective while leveraging exploratory interviews and observations, we build a theoretical framework to examine how specific attributes of the technical problem broadcast by firms affect the seekers’ choice between alternative intellectual property rights (IPR) arrangements that call for acquiring or licensing‐in IPR from external solvers (i.e., with high and low degrees of ownership, respectively). Each technical problem differs in the knowledge required to be solved as well as in the stage of development of the innovation process and seeker firms pay great attention to such characteristics when deciding about the IPR arrangement they choose for their contests. In addition, we analyze how this choice between acquiring and licensing‐in IPR, in turn, influences the performance of the contest. We empirically test our hypotheses analyzing a unique dataset of 729 challenges broadcast on the InnoCentive platform from 2010 to 2016. Our results indicate that challenges related to technical problems in later stages of the innovation process are positively related to the seekers’ preference toward IPR arrangements with a high level of ownership, while technical problems involving a higher number of knowledge domains are not. Moreover, we found that IPR arrangements with a high level of ownership negatively affect solvers’ participation and that IPR arrangement play a mediating role between the attributes of the technical problem and the solvers’ self‐selection process. Our paper contributes to the open innovation and crowdsourcing literature and provides practical implications for both managers and contest organizers.     

Debt ceiling and fiscal sustainability in Brazil: A quantile autoregression approach

In this paper we investigate fiscal sustainability by using a quantile autoregression (QAR) model. We propose a novel methodology to separate periods of nonstationarity from stationary ones, allowing us to identify various trajectories of public debt that are compatible with fiscal sustainability. We use such trajectories to construct a debt ceiling, that is, the largest value of public debt that does not jeopardize long-run fiscal sustainability. We make an out-of-sample forecast of such a ceiling and show how it could be used by Policy makers interested in keeping the public debt on a sustainable path. We illustrate the applicability of our results using Brazilian data.     

The optimal harvesting problem with a land market: a characterization of the asymptotic convergence

We study the asymptotic behavior of the optimal harvesting policies for a multiple species forest with a land market, i.e., any fraction of the land can be traded at any time stage. We prove the existence of sustainable states and we discuss the conditions under which any optimal trajectory converges in the long run towards one of these states or towards the set of optimal periodic cycles. We also discuss briefly a more general problem that includes costs of converting land between the different species.      

CONSTRUCTING OPTIMAL DENSITY FORECASTS FROM POINT FORECAST COMBINATIONS

Decision makers often observe point forecasts of the same variable computed, for instance, by commercial banks, IMF and the World Bank, but the econometric models used by such institutions are frequently unknown. This paper shows how to use the information available on point forecasts to compute optimal density forecasts. Our idea builds upon the combination of point forecasts under general loss functions and unknown forecast error distributions. We use real‐time data to forecast the density of US inflation. The results indicate that the proposed method materially improves the real‐time accuracy of density forecasts vis‐à‐vis those from the (unknown) individual econometric models. Copyright © 2013 John Wiley & Sons, Ltd.     

The Cost of Living and Terror: Does Consumer Price Volatility Fuel Terrorism?

Do fluctuations of consumer prices prompt terrorist activity? A latent assumption among conflict scholars is that price volatilities for basic consumer goods produce hardships for people that increase popular grievances, damage government legitimacy, and raise the chances for terrorism. In this study, I use a series of regression estimations to test volatility in consumer price indices for energy, housing, and foods as predictors of domestic and transnational terrorism in a cross section of countries. I produce several findings. First, food price fluctuations are significant predictors of multiple measures of terrorism, while energy and housing prices are not. Second, rapid food price increases, not decreases, promote terrorist attacks. Third, the relationship between food price volatility and terrorism is most consistently present in nondemocratic and “hybrid” political regimes and in medium human development countries rather than in democracies or in countries characterized by very high or very low economic development.     

About optimal harvesting policies for a multiple species forest without discounting

We study the optimal harvesting of a mixed forest composed of multiple species, each one having a different maturity age, where only mature trees can be harvested. We prove the existence of an optimal program and the equivalence of maximal, optimal and minimal value-loss programs. We characterize the unique golden rule stock and prove that it is sustainable, i.e., it is invariant along the optimal program. Furthermore, we also prove that along any good program from any initial condition there is convergence of the forest’s state to this sustainable state. Finally, we define a value function in the set of forest states and define a pre-order that provides an alternative way of characterizing the golden rule stock and may potentially have independent interest.     

Applying a microfounded-forecasting approach to predict Brazilian inflation

We investigate whether combining forecasts from surveys of expectations is a helpful empirical strategy for forecasting inflation in Brazil. We employ the FGV–IBRE Economic Tendency Survey, which consists of monthly qualitative information from approximately 2000 consumers since 2006, and also the Focus Survey of the Central Bank of Brazil, with daily forecasts since 1999 from roughly 250 professional forecasters. Natural candidates to win a forecast competition in the literature of surveys of expectations are the (consensus) cross-sectional average forecasts (AF). We first show that these forecasts are a bias-ridden version of the conditional expectation of inflation using the no-bias tests proposed in Issler and Lima (J Econom 152(2):153–164, 2009) and Gaglianone and Issler (Microfounded forecasting, 2015). The results reveal interesting data features: Consumers systematically overestimate inflation (by 2.01 p.p., on average), whereas market agents underestimate it (by 0.68 p.p. over the same sample). Next, we employ a pseudo out-of-sample analysis to evaluate different forecasting methods: the AR(1) model, the Granger and Ramanathan (J Forecast 3:197–204, 1984) forecast combination (GR) technique, a Phillips-curve based method, the Capistrán and Timmermann (J Bus Econ Stat 27:428–440, 2009) combination method, the consensus forecast (AF), the bias-corrected average forecast (BCAF), and the extended BCAF. Results reveal that: (i) the MSE of the AR(1) model is higher compared to the GR (and usually lower compared to the AF); and (ii) the extended BCAF is more accurate than the BCAF, which, in turn, dominates the AF. This validates the view that the bias corrections are a useful device for forecasting using surveys. The Phillips-curve based method has a median performance in terms of MSE, and the Capistrán and Timmermann (2009) combination method fares slightly worse.     

Do European trade unions foster social solidarity? Evidence from multilevel data in 18 countries

Trade unions have been analysed quantitatively primarily in their role as vested interest organisations, attempting to quantify the excludable benefits they provide to members rather than examine their wider impact in an institutional context. Power resource theory acknowledges unions as social agents but assumes the willingness to oppose neoliberalism is constant, limited only by scarce power resources. Whilst true in general terms, this fails to explain trends of increasing labour market dualism in resource‐rich industrial relations regimes. This article examines social solidarity as a union power resource, measuring the impact of trade union membership on social attitudes of solidarity. Data were collected from the 2016 European Social Survey for 18 countries, grouped into five distinct industrial relations regimes. The findings suggest that, at European level, union membership still has a significant effect on all dimensions of social solidarity, but these relationships vary significantly across industrial relations regimes.