[en] Highlights: • We evaluate the impact of daylight saving time on electricity consumption in Mexico. • We use a differences-in-differences econometric approach to evaluate DST. • Our data covers a large period both before and after DST started in Mexico. • Residential savings due to DST represent 0.5% of total electricity consumption. • Savings are larger towards the end of the DST period. -- Abstract: Since the First World War, several countries use daylight saving time (DST). However, evaluations of DST started until the 1970s. Given the difficulties of finding data around the moment in which DST started, econometric studies usually evaluate changes to DST. We have aggregate data on residential electricity consumption in Mexico from 1982 to 2016. DST started in 1996. Therefore, our data covers a large period both before and after DST started. Moreover, DST is in effect only during part of the year (from April to October). Hence, we can evaluate the effects of DST with a differences-in-differences (DID) econometric approach. As far as we know, there is only another DID econometric study using similar data. Such study takes advantage of a short period in which DST was adopted and then repealed in Australia. Since our data covers a larger period, we can control for trends in consumption, calculate savings in different DST months, and run robustness tests. Our estimates indicate that savings due to DST account for 0.5% of total electricity consumption. Nevertheless, the effect of DST is not homogeneous along the period. This is important because estimates based on DST extensions may not reflect the effects of DST itself.

[en] This article reviews a number of forecasting methods that have been applied to uranium prices and compares their relative strengths and weaknesses. The methods reviewed are: (1) judgemental methods, (2) technical analysis, (3) time-series methods, (4) fundamental analysis, and (5) econometric methods. Historically, none of these methods has performed very well, but a well-thought-out model is still useful as a basis from which to adjust to new circumstances and try again

[en] We develop a class of tests for the structural stability of infinite order regression models, when the time of a structural change is unknown. Examples include the infinite order autoregressive model, the nonparametric sieve regression and many others whose dimensions grow to infinity. When the number of parameters diverges, the traditional tests such as the supremum of Wald, LM or LR statistic or their exponentially weighted averages diverge as well. However, we show that a suitable transformation of these tests converges to a proper weak limit as the sample size n and the dimension p grow to infinity simultaneously. In general, this limit distribution is different from the sequential limit, which can be obtained by increasing the order p of the standardized tied-down Bessel process in Andrews (1993). More interestingly, our joint asymptotic analysis discovers that the joint asymptotic distribution depends on a higher order serial correlation. We also establish a weighted power optimality property of our tests under certain regularity conditions. A new result on partial sums of random matrices is established. We examine finite-sample performance in a Monte Carlo study and illustrate the test with a number of empirical examples.

No abstract available

[en] Highlights: • We analyze hedging under minimum-variance and risk frameworks for crude oil. • The optimal hedge ratios under these two criteria are significantly different. • None of the single models performs best in two different frameworks. • Combining hedge ratio from all models results in the most reliable hedging result. -- Abstract: Most of existing studies on crude oil futures hedging aim to minimizing the variance of hedged portfolio. In this paper, we evaluate the hedging performance in a different framework of minimum-risk and try to find the optimal hedge model. We employ a total of ten popular econometric specifications including three constant and seven dynamic hedge ratio models. Our results suggest that none of the models of interest can outperform all competitors in or out of sample for all futures contracts. The constant hedge ratio models perform better than the dynamic hedge ratio models under the min-V framework, but the situation overturns under the min-R framework with the DCC-GARCH performs best. More importantly, the equal-weighted combination of all constant and dynamic hedge ratios results in better out-of-sample performance than the combination of either type of hedge ratios only.

[en] We develop a class of tests for the structural stability of infinite order regression models, when the time of a structural change is unknown. Examples include the infinite order autoregressive model, the nonparametric sieve regression and many others whose dimensions grow to infinity. When the number of parameters diverges, the traditional tests such as the supremum of Wald, LM or LR statistic or their exponentially weighted averages diverge as well. However, we show that a suitable transformation of these tests converges to a proper weak limit as the sample size n and the dimension p grow to infinity simultaneously. In general, this limit distribution is different from the sequential limit, which can be obtained by increasing the order p of the standardized tied-down Bessel process in Andrews (1993). More interestingly, our joint asymptotic analysis discovers that the joint asymptotic distribution depends on a higher order serial correlation. We also establish a weighted power optimality property of our tests under certain regularity conditions. A new result on partial sums of random matrices is established. We examine finite-sample performance in a Monte Carlo study and illustrate the test with a number of empirical examples.

[en] Highlights: • Market liquidity as well as surprise volume shocks are priced in the oil market. • Lower levels of lagged market liquidity relate to above average conditional oil market returns. • Surprise volume shocks are associated with lower conditional oil market returns and higher conditional return volatility. • Surprise volume—as a proxy of private information flow—is not related to a set of standard oil market liquidity proxies. -- Abstract: We investigate oil market price dynamics in the context of the Mixture of Distributions Hypothesis (MDH). Our econometric model addresses autoregressive properties in returns, the impact of surprise volume and conditional oil market return volatility as well as oil market liquidity in the conditional return equation. Surprise volume as a proxy of private information flow is shown to be unrelated to a set of standard market liquidity proxies. Oil return heteroscedasticity is found to be partly explained by surprise volume, a finding that is consistent with the MDH. Our findings further show that both oil market liquidity as well as surprise volume shocks are priced in the oil market. As such, lower levels of lagged market liquidity relate to above average conditional returns. Surprise volume shocks are associated with lower conditional oil market returns jointly with higher contemporaneous conditional return volatility. Lagged market liquidity dominates conditional volatility in predicting conditional oil price returns.

[en] Highlights: • We explore the spatial distribution of industrial resource allocation efficiency and carbon emissions. • The paper identifies the evolution mechanism of carbon emissions in China's industrialization. • Analyzing the relationship between resource allocation and carbon emissions from a spatial econometric perspective. • The improvement of industrial resource allocation can reduce carbon emissions on the national level. • Industrial resource allocation can significantly reduce carbon emissions in the eastern region. Analyzing the relationship between industrial resource allocation and carbon emissions from the regional level will promote cross-regional environmental coordinated governance. Based on the panel data of 30 provinces from 2007 to 2016, this paper explores the spatial distribution of industrial resource allocation, spatial autocorrelation of carbon emissions, and the relationship from a spatial econometric perspective. The results show that compared with other provinces, Jilin, Zhejiang, and Guangdong have relatively higher industrial resource allocation efficiency. The provinces with higher carbon emissions are spatially adjacent, and the provinces with lower carbon emissions are also spatially adjacent. On a national level, the improvement of industrial resource allocation can reduce carbon emissions. On a regional level, the impact of industrial resource allocation efficiency on carbon emissions is somewhat different. Industrial resource allocation can significantly reduce carbon emissions in the eastern region. However, it is not clear whether the improvement in the industrial resource allocation efficiency can reduce carbon emissions in the central, western, and northeast regions.

[en] In early 2018 Bitcoin prices peaked at US$ 20,000 and, almost two years later, we still continue debating if cryptocurrencies can actually become a currency for the everyday life or not. From the economic point of view, and playing in the field of behavioral finance, this paper analyses the relation between Bitcoin prices and the search interest on Bitcoin since 2014. We questioned the forecasting ability of Google Bitcoin Trends for the behavior of Bitcoin price by performing linear and nonlinear dependency tests, and exploring performance of ARIMA and Neural Network models enhanced with this social sentiment indicator. Our analyses and models are founded upon a set of statistical properties common to financial returns that we establish for Bitcoin, Ethereum, Ripple and Litecoin.

[en] Highlights: • This paper investigated the effect of liquified petroleum gas use on the learning outcome of primary school children. • We used the Ghana Living Standards Survey and different econometric techniques in our analysis. • Our results show that LPG use marginally improves children’s ability to read, write, and solve simple mathematics problems. • Additional analyses show that reduced time on fuel collection is a potential channel of the observed effect. • Our results imply that the adoption of cooking gas could improve human capital development in developing countries. Children in Sub-Saharan Africa spend a non-trivial amount of their time cooking and collecting fuel for domestic use. This is particularly the case in rural areas where access to efficient energy is low, and children’s academic performance is poor. This paper argues that households' use of cooking gas could reduce the time spent doing domestic chores, increase learning time, and improve children's school performance. We investigate this proposition using the Ghana Living Standards Survey data. We employ different instrumental variable estimations techniques to deal with the possible endogeneity problem. The results show that cooking with gas marginally improves the learning outcome of children in rural Ghana. Our results imply that the adoption of cooking gas could enhance human capital development in developing countries.