Financing energy efficiency retrofits in Chilean households: The role of financial instruments,savings and uncertainty in energy transition

High firewood consumption for heating produces high levels of pollution in cities in central and southern Chile, with serious consequences for health and quality of life. Energy efficiency measures (EEMs) have been identified as the best strategy to reduce air pollution and maximize social benefits. However, their adoption has been slow. The objective of this article is to investigate household preferences for financial incentives needed to promote private investments in EEMs in Central-Southern Chilean households and study the role of energy savings and the uncertainty about potential savings in the investment decision, with the aim of finding solutions to increase the adoption of these technologies. We use a choice experiment to explore the trade-off between the investment costs, financial instruments, energy savings, and the uncertainty about achieving the theoretical savings provided by engineering and architectural models. The results show that financial instruments play the most important role in this decision, followed by the savings achieved by the retrofit. Householders prefer to finance their investments with a mix of their personal resources and medium-term credits, trying to avoid long-term commitments. Although uncertainty was found to be a significant variable, it seems to play a small role in the investment decision.     

Accounting practice,fiscal decentralization and corruption

In prior studies, accounting and decentralization corruption solutions have so far been analysed in isolation. In this article, we connect these two strands of literature on corruption. Understanding this connection is important because weak financial accounting and reporting systems can inhibit monitoring incentives and thus reduce decentralization benefits in countering corruption. We argue that the effectiveness of decentralization as an anti-corruption barrier is complemented by the quality of the accounting practice in a country. Using multiple sources of data, we find that decentralization has a positive and increasing effect on reducing corruption among countries with a high-quality accounting practice. In contrast, decentralization has a negative and decreasing effect on reducing corruption among countries with weak-quality accounting practices. These findings are robust to alternative measures of accounting, decentralization and corruption and to endogeneity tests. Our findings demonstrate the crucial information role of accounting in enhancing decentralization monitoring mechanisms and in thereby reducing corruption.     

Financial development and economic growth in a microfounded small open economy model

The global financial crisis since 2008 revived the debate on whether or not and to what extent financial development contributes to economic growth. This paper reviews different theoretical schools of thought and empirical findings on this nexus, building on which we aim to develop a unified, microfounded model in a small open economy setting to accommodate various theoretical possibilities and empirical observations. The model is then calibrated to match some well-documented stylized facts. Numerical simulations show that, in the long run, the welfare-maximizing level of financial develop is lower than the growth-maximizing level. In the short run, the price channel (through world interest rate) dominates the quantity-channel (through financial productivity), suggesting a vital role of international cooperation in tackling systemic risk of the global financial system.     

产业集聚、市场集中对区域创新能力的作用——基于京津冀、长三角两大经济圈制造业的比较

产业集聚和市场集中是影响区域创新能力的重要因素。本文考察了京津冀、长三角两大经济圈制造业产业集聚与市场集中对区域技术创新能力的作用。实证分析结果表明：两大经济圈制造业集聚与市场集中均在一定程度上促进了区域创新能力的提升，但由于两大区域体制机制条件、创新环境、产业组织形态等不同，制造业产业集聚影响区域技术创新的程度、特征与路径也存在一定差别。两大经济圈应采取差异化集群发展方式，加快推进制造业集群升级，特别是应抢抓第四次工业革命发展机遇，提升制造业智能化水平，提升区域创新能力水平。     

城镇群空间结构与要素集聚

依据城镇化发展过程中要素空间集聚理论，展开区域经济发展的空间经济结构研究。通过城市引力、克鲁格曼指数计算，比较分析城镇群内部城市吸引力和城市产业分布的差异性，通过人口与生产空间分布集中度指标和不一致系数的测算，分析了山西省城镇群空间要素集聚效应。研究表明，山西省的城镇群规划与要素空间集聚趋势整体一致，城市间产业分工体系基本形成，城镇群的要素集聚效应明显，但人口与生产分布仍存在不一致性。研究认为，山西省城镇群发展应该继续明确各城市的产业分工和特色，引导劳动力和资本要素的合理流动，实现人口与生产空间分布的一致。     

Risk spillover between Bitcoin and conventional financial markets: An expectile-based approach

In order to challenge the existing literature that points to the detachment of Bitcoin from the global financial system, we use daily data from August 17, 2011–February 14, 2020 and apply a risk spillover approach based on expectiles. Results show reasonable evidence to imply the existence of downside risk spillover between Bitcoin and four assets (equities, bonds, currencies, and commodities), which seems to be time dependent. Our main findings have implications for participants in both the Bitcoin and traditional financial markets for the sake of asset allocation, and risk management. For policy makers, the findings suggest that Bitcoin should be monitored carefully for the sake of financial stability.     

Improving forecasting performance using covariate-dependent copula models

Copulas provide an attractive approach to the construction of multivariate distributions with flexible marginal distributions and different forms of dependences. Of particular importance in many areas is the possibility of forecasting the tail-dependences explicitly. Most of the available approaches are only able to estimate tail-dependences and correlations via nuisance parameters, and cannot be used for either interpretation or forecasting. We propose a general Bayesian approach for modeling and forecasting tail-dependences and correlations as explicit functions of covariates, with the aim of improving the copula forecasting performance. The proposed covariate-dependent copula model also allows for Bayesian variable selection from among the covariates of the marginal models, as well as the copula density. The copulas that we study include the Joe-Clayton copula, the Clayton copula, the Gumbel copula and the Student’s $t$-copula. Posterior inference is carried out using an efficient MCMC simulation method. Our approach is applied to both simulated data and the S&P 100 and S&P 600 stock indices. The forecasting performance of the proposed approach is compared with those of other modeling strategies based on log predictive scores. A value-at-risk evaluation is also performed for the model comparisons.     

Is it U.S. GAAP or IFRS? Understanding how R&D costs affect ratio analysis

While financial reporting standards under U.S. GAAP and IFRS are fundamentally similar, differences do exist that may affect our analysis of company financial statements. This is particularly true when comparing a U.S. company following U.S. GAAP to a firm that uses IFRS. To illustrate, we compare research and development (R&D) accounting methods under both sets of standards and illustrate how they affect the analysis of financial results of firms in a specific industry—automotive manufacturers. Our results provide insight into settings in which differences in R&D accounting may have the greatest impact on financial analysis.     

Does bank competition promote financial inclusion? A cross-country evidence

The idea of financial inclusion has recently been discussed as one of the key strategies to enhance economic development (World Bank 2014). We move this discussion forward by examining if bank competition is a crucial component enforcing financial inclusion, which is arguably a finance supply-side driven process. In this study, we compute the financial inclusion index built on Sarma (2008) approach to better reflect banks’ willingness and capability to provide financial services. Applying System Generalized Method of Moments to the panel of 93 countries, we find that bank competition promotes financial inclusion.     

A multivariate distance nonlinear causality test based on partial distance correlation: a machine learning application to energy futures

This paper proposes a multivariate distance nonlinear causality test (MDNC) using the partial distance correlation in a time series framework. Partial distance correlation as an extension of the Brownian distance correlation calculates the distance correlation between random vectors X and Y controlling for a random vector Z. Our test can detect nonlinear lagged relationships between time series, and when integrated with machine learning methods it can improve the forecasting power. We apply our method as a feature selection procedure and combine it with the support vector machine and random forests algorithms to study the forecast of the main energy financial time series (oil, coal, and natural gas futures). It shows substantial improvement in forecasting the fuel energy time series in comparison to the classical Granger causality method in time series.