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.     

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.     

乌蒙山片区绿色减贫指数分析

[目的]通过分析2012年和2014年乌蒙山片区绿色减贫指数,评价该地区不同年份扶贫效果,这也为今后各项扶贫政策的实施提供理论参考。[方法]文章依据中国绿色减贫指数指标体系,从经济增长绿化度、资源利用与环境保护程度、社会发展能力和扶贫开发与减贫效果4个方面,以人均地区生产总值、单位地区生产总值能耗等27个因子作为评价指标,利用线性加权综合法分析2012年和2014年经济增长绿化度、资源利用与环境保护程度、社会发展能力和扶贫开发与减贫效果指标值,最终确定绿色减贫指数。同时,分析片区内各贫困县波动较大的第三产业增加值比重、单位耕地面积化肥施用量、城乡收入比和农村人均纯收入增长率等指标。[结果]2014年相比2012年,乌蒙山片区经济增长绿化度、资源利用与环境保护程度和扶贫开发与减贫效果指标值上升,社会发展能力指标值降低,绿色减贫指数提高了15.40%。各县第三产业比重均降低,但农村人均纯收入都有提高。四川省10个县单位耕地面积化肥施用量增加。四川省除叙永县和美姑县,贵州省除赤水市,片区内的各贫困县城乡收入比降低。[结论]2014年,乌蒙山区在落实国家扶贫规划上取得了较好的成效。但各县之间差距较大,在今后的扶贫攻坚中,需要从不同地区的实际情况出发,了解贫困类型和贫困程度,考察当地资源储备和环境现状,统筹区域发展。     

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.     

信贷管制、隐性契约与贷款利率变相市场化——现象与解释

本文将视角聚焦于利率市场化改革之前,重点研究金融抑制对银企信贷契约的异化。研究发现,在金融抑制背景下银企之间存在以留存贷款为表现的隐性契约,用以帮助银行在满足管制要求的同时追求利润最大化。本文对这一现象进行了理论分析与实证检验,发现隐性契约的发生与否会随着货币政策、企业特征等因素的改变而有所差异,这在一定程度上起到了利率变相市场化的作用。进一步研究则发现,这一隐性契约对企业价值产生了负向影响。