The influence of variables of attitude on collective entrepreneurship

In the current global corporate climate that surrounds us, firms would do well to encourage the talent and creativity of their employees in order to achieve success. This is achieved, not by giving priority to individual talent, but by optimizing the collective as a whole and the firm’s activity based on team work and joint effort. Heads of organizations might benefit from creating a favorable context for the birth and growth of collective internal cooperation, which is understood to be the collective ability to create and innovate on the part of the team, the firm or the organization. Having contrasted our hypotheses through research on Spanish firms, we have concluded that job satisfaction and commitment to the team are factors that have a direct and positive effect on collective entrepreneurship.     

The Environmental Kuznets Curve,Cointegration and Nonlinearity

This paper clarifies some conceptual shortcomings of the empirical environmental Kuznets curve (EKC) literature that arise because of the hitherto inadequate application of unit root and cointegration techniques. The literature to date has ignored the fact, and a fortiori the consequences, that powers of integrated processes are themselves not integrated processes. The paper explains why standard methods should not be applied and discusses some recently proposed viable estimation and testing approaches for cointegrating polynomial regressions. The application to CO₂ and SO₂ emissions data shows that using appropriate methods leads to strongly reduced evidence for a cointegrating EKC compared to typical but conceptually not sound findings. Copyright © 2014 John Wiley & Sons, Ltd.     

Playing the synthesizer with Canadian data: Adding polls to a structural forecasting model

Election forecasting has become a fixture of election campaigns in a number of democracies. Structural modeling, the major approach to forecasting election results, relies on ‘fundamental’ economic and political variables to predict the incumbent’s vote share usually a few months in advance. Some political scientists contend that adding vote intention polls to these models—i.e., synthesizing ‘fundamental’ variables and polling information—can lead to important accuracy gains. In this paper, we look at the efficiency of different model specifications in predicting the Canadian federal elections from 1953 to 2015. We find that vote intention polls only allow modest accuracy gains late in the campaign. With this backdrop in mind, we then use different model specifications to make ex ante forecasts of the 2019 federal election. Our findings have a number of important implications for the forecasting discipline in Canada as they address the benefits of combining polls and ‘fundamental’ variables to predict election results; the efficiency of varying lag structures; and the issue of translating votes into seats.     

Forecasting recovery rates on non-performing loans with machine learning

We compare the performance of a wide set of regression techniques and machine-learning algorithms for predicting recovery rates on non-performing loans, using a private database from a European debt collection agency. We find that rule-based algorithms such as Cubist, boosted trees, and random forests perform significantly better than other approaches. In addition to loan contract specificities, predictors that refer to the bank recovery process — prior to the portfolio’s sale to a debt collector — are also shown to enhance forecasting performance. These variables, derived from the time series of contacts to defaulted clients and client reimbursements to the bank, help all algorithms better identify debtors with different repayment ability and/or commitment, and in general those with different recovery potential.     

Forecasting the volatility of asset returns: The informational gains from option prices

A new class of forecasting models is proposed that extends the realized GARCH class of models through the inclusion of option prices to forecast the variance of asset returns. The VIX is used to approximate option prices, resulting in a set of cross-equation restrictions on the model’s parameters. The full model is characterized by a nonlinear system of three equations containing asset returns, the realized variance, and the VIX, with estimation of the parameters based on maximum likelihood methods. The forecasting properties of the new class of forecasting models, as well as a number of special cases, are investigated and applied to forecasting the daily S&P500 index realized variance using intra-day and daily data from September 2001 to November 2017. The forecasting results provide strong support for including the realized variance and the VIX to improve variance forecasts, with linear conditional variance models performing well for short-term one-day-ahead forecasts, whereas log-linear conditional variance models tend to perform better for intermediate five-day-ahead forecasts.     

Innovation and the circular economy: A systematic literature review

The circular economy emerged as an alternative model to the linear system, which now appears to be reaching its physical limitations. To transition to a circular economy, companies must not only be aware of but also engage in more sustainable practices. For such a transition, companies must rethink and innovate their business models and the ways they propose value to their clients while simultaneously considering environmental and social facets. This systematic literature review sought to map out from the company perspective the key topics interrelated with innovation and the circular economy, describing the internal and external factors to consider in such transition processes. Key lines of research were identified, and suggestions for future research and for facilitating movement toward a circular economy are provided. This work contributes to deepening the literature by identifying the priority areas concerning the circular economy and encouraging future research that meets international standards of excellence.