Optimal Water Allocation of Surface and Ground Water Resources Under Climate Change with WEAP and IWOA Modeling

Water Resources Management - The water evaluation and planning (WEAP) approach and the invasive weed optimization algorithm (IWOA) are herein employed to determine the optimal operating policies in...     

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...     

Methodology for Estimating Streamflow by Water Balance and Rating Curve Methods Based on Logistic Regression

Both water balance (WB) and rating curve (RC) are methods for estimating streamflow. The first is mostly used to estimate reservoir outflows, while the second is usually adopted in hydrometeorological network streamflow gauges. While WB uses hourly collected data, the RC estimates streamflow using current water level and extrapolation techniques. The objective of this study was to analyze variations in the reservoir’s hourly outflow at Queimado Hydroelectric Power Plant (HPP Queimado) and to propose a method to evaluate whether the estimate of the daily outflows, obtained by the WB method, is similar to the flow values obtained at a conventional station. The logistic regression (LR) model was used because it is a method that adopts binary, categorically dependent variables to identify the event of interest. The results showed that the values of streamflow, obtained from an average of two daily readings, were a good representation of the flows in the region. The LR was able to identify atypical data, especially in the rainy season. This means that data consistency analysis can be faster and safer, when adequately employed and considering the proposed conditions, contributing to both management policies and the management of water resources.

     

Analysis of the Efficacy of Public Spending on Research and Development in State Programs

This paper presents an approach to assessing the efficacy of public spending on research and development in state programs. At present, there is a need to develop a unified approach to the analysis and evaluation of the efficacy of such spending from the perspective of participants in state programs (federal executive bodies). The proposed approach does not allow evaluating the efficacy of public spending on the development of research areas assigned to the authors.     

The power of positivity? The influence of positive psychological capital language on crowdfunding performance

We extend the entrepreneurship literature to include positive psychological capital — an individual or organization's level of psychological resources consisting of hope, optimism, resilience, and confidence — as a salient signal in crowdfunding. We draw from the costless signaling literature to argue that positive psychological capital language usage enhances crowdfunding performance. We examine 1726 crowdfunding campaigns from Kickstarter, finding that entrepreneurs conveying positive psychological capital experience superior fundraising performance. Human capital moderates this relationship while social capital does not, suggesting that costly signals may, at times, enhance the influence of costless signals. Post hoc analyses suggest findings generalize across crowdfunding types, but not to IPOs.     

The US financial crisis and corporate dividend reactions: for better or for worse?

Review of Quantitative Finance and Accounting - We examine how changes in dividend policy in 2008 as the financial crisis was unfolding influenced firm risk-adjusted returns in the following years....     

A Real Options Model of Real Estate Development with Entitlement Risk

Most development projects encounter a highly uncertain entitlement process that is largely uncontrollable by developers. In this study, entitlement is modeled as a separate stage within a compound real option, where developers begin with minimal control (maximum risk) and each successful stage increases control (decreases risk). We solve the model analytically, provide three‐dimensional numerical comparisons, and empirically test the model's predictions using hand collected rezoning petitions. Our main result refines the classic development option model: developers first invest early (secure entitlements) in order to obtain the option to subsequently delay investment (construct the optimal building at the optimal time).     

Entry limiting agreements: First‐mover advantage,authorized generics,and pay‐for‐delay deals

During patent litigation, pay‐for‐delay (P4D) deals involve a payment from a patent holder of a branded drug to a generic drug manufacturer to delay entry and withdraw the patent challenge. In return for staying out of the market, the generic firm receives a payment, and/or an authorized licensed entry at a later date, but before the patent expiration. We examine why such deals are stable when there are multiple potential entrants. We combine the first‐mover advantage for the first generic with the ability of the branded manufacturer to launch an authorized generic (AG) to show when P4D deals are an equilibrium outcome. We further show that limiting a branded firm's ability to launch an AG before entry by a successful challenger will deter such deals. However, removing exclusivity period for the first generic challenger will not.     

Bitcoin price and its marginal cost of production: support for a fundamental value

This study back-tests a marginal cost of production model proposed to value the digital currency Bitcoin. Results from both conventional regression and vector autoregression (VAR) models show that the marginal cost of production plays an important role in explaining Bitcoin prices, challenging recent allegations that Bitcoins are essentially worthless. Even with markets pricing Bitcoin in the thousands of dollars each, the valuation model seems robust. The data show that a price bubble that began in the Fall of 2017 resolved itself in early 2018, converging with the marginal cost model. This suggests that while bubbles may appear in the Bitcoin market, prices will tend to this bound and not collapse to zero.