Can environmental innovation facilitate carbon emissions reduction? Evidence from China

Historically, time series forecasts of economic variables have used only a handful of predictor variables, while forecasts based on a large number of predictors have been the province of judgmental forecasts and large structural econometric models. The past decade, however, has seen considerable progress in the development of time series forecasting methods that exploit many predictors, and this chapter surveys these methods. The first group of methods considered is forecast combination (forecast pooling), in which a single forecast is produced from a panel of many forecasts. The second group of methods is based on dynamic factor models, in which the comovements among a large number of economic variables are treated as arising from a small number of unobserved sources, or factors. In a dynamic factor model, estimates of the factors (which become increasingly precise as the number of series increases) can be used to forecast individual economic variables. The third group of methods is Bayesian model averaging, in which the forecasts from very many models, which differ in their constituent variables, are averaged based on the posterior probability assigned to each model. The chapter also discusses empirical Bayes methods, in which the hyperparameters of the priors are estimated. An empirical illustration applies these different methods to the problem of forecasting the growth rate of the U.S. index of industrial production with 130 predictor variables.

Forecasting with Many Predictors

The impact of wind power generation on the electricity price in Germany

Physics shows that energy is necessary for economic production and, therefore, economic growth but the mainstream theory of economic growth, except for specialized resource economics models, pays no attention to the role of energy. This paper reviews the relevant biophysical theory and mainstream, resource economics, and ecological economics models of growth. A possible synthesis of energy-based and mainstream models is presented. This model shows that when energy is scarce it imposes a strong constraint on the growth of the economy but when energy is abundant its effect on economic growth is much reduced. This explains the industrial revolution as a releasing of the constraints on economic growth due to the development of methods of using coal and the discovery of new fossil fuel resources. Time series analysis shows that energy and GDP cointegrate and energy use Granger causes GDP when capital and other production inputs are included in the vector autoregression model. There are, however, various mechanisms that can weaken the links between energy and growth. The empirical literature finds that energy used per unit of economic output has declined in developed and some developing countries, due to both technological change and to a shift from poorer quality fuels such as coal to the use of higher quality fuels, and especially electricity. Substitution of other inputs for energy and sectoral shifts in economic activity play smaller roles.

/pdf/the-role-of-energy-in-economic-growth-10cmao34yv.pdf

The Role of Energy in Economic Growth

Recently developed structural models of the global crude oil market imply that the surge in the real price of oil between mid-2003 and mid-2008 was driven by repeated positive shocks to the demand for all industrial commodities, reflecting unexpectedly high growth mainly in emerging Asia. This note evaluates this proposition using an alternative data source and a different econometric methodology. Rather than inferring demand shocks from an econometric model, we utilize a direct measure of global demand shocks based on revisions of professional real GDP growth forecasts. We show that recent forecast surprises were associated primarily with unexpected growth in emerging economies (and to a lesser extent in Japan), that markets were repeatedly surprised by the strength of this growth, that these surprises were associated with a hump-shaped response of the real price of oil that reaches its peak after 12 to 16 months, and that news about global growth predict much of the surge in the real price of oil from mid-2003 until mid-2008 and much of its subsequent decline.

Did Unexpectedly Strong Economic Growth Cause the Oil Price Shock of 2003-2008?; Parenting Styles and Financial Security: How are children impacted?

On the non-convergence of energy intensities: Evidence from a pair-wise econometric approach

https://halshs.archives-ouvertes.fr/halshs-00405709/document

Options introduction and volatility in the EU ETS

Using daily data from March 2001 to June 2005, we estimate a VAR-BEKK model and find evidence of return and volatility spillovers between the German, the Dutch and the British forward electricity markets. We apply Hafner and Herwartz [2006, Journal of International Money and Finance 25, 719-740] Volatility Impulse Response Function (VIRF) to quantify the impact of shock on expected conditional volatility. We observe that a shock has a high positive impact only if its size is large compared to the current level of volatility. The impact of shocks are usually not persistent, which may be an indication of market efficiency. Finally, we estimate the density of the VIRF at different forecast horizon. These fitted distributions are asymmetric and show that extreme events are possible even if their probability is low. These results have interesting implications for market participants whose risk management policy is based on option prices which themselves depend on the volatility level.

Volatility Transmission and Volatility Impulse Response Functions in European Electricity Forward Markets

https://basepub.dauphine.fr/bitstream/handle/123456789/5450/sevi_volatility.PDF?sequence=1

Volatility transmission and volatility impulse response functions in European electricity forward markets

We empirically reinvestigate the issue of the excess co-movement of commodity prices initially raised in Pindyck and Rotemberg (1990). Excess co-movement appears when commodity prices remain correlated even after adjusting for the impact of fundamentals. We use recent developments in large approximate factor models to consider a richer information set and adequately model these fundamentals. We consider a set of eight unrelated commodities along with 184 real and nominal macroeconomic variables, from developed and emerging economies, from which nine factors are extracted over the 1993–2013 period. Our estimates provide evidenceof time-varying excess co-movement which is particularly high after 2007. Wefurther show that speculative intensity is a driver of the estimated excess comovement, as speculative trading is both correlated across the commodity futures markets and correlated with the futures prices. Our results can be taken as direct evidence of the significant impact of financialization on commodity-price crossmoments.

/pdf/futures-trading-and-the-excess-comovement-of-commodity-4iv5ifqdib.pdf

Yannick Le Pen

Papers

On the non-convergence of energy intensities: Evidence from a pair-wise econometric approach

Options introduction and volatility in the EU ETS

Volatility Transmission and Volatility Impulse Response Functions in European Electricity Forward Markets

Volatility transmission and volatility impulse response functions in European electricity forward markets

Futures trading and the excess comovement of commodity prices