|
on Forecasting |
| By: | Katarzyna Maciejowska; Bartosz Uniejewski; Rafa{\l} Weron |
| Abstract: | Forecasting electricity prices is a challenging task and an active area of research since the 1990s and the deregulation of the traditionally monopolistic and government-controlled power sectors. Although it aims at predicting both spot and forward prices, the vast majority of research is focused on short-term horizons which exhibit dynamics unlike in any other market. The reason is that power system stability calls for a constant balance between production and consumption, while being weather (both demand and supply) and business activity (demand only) dependent. The recent market innovations do not help in this respect. The rapid expansion of intermittent renewable energy sources is not offset by the costly increase of electricity storage capacities and modernization of the grid infrastructure. On the methodological side, this leads to three visible trends in electricity price forecasting research as of 2022. Firstly, there is a slow, but more noticeable with every year, tendency to consider not only point but also probabilistic (interval, density) or even path (also called ensemble) forecasts. Secondly, there is a clear shift from the relatively parsimonious econometric (or statistical) models towards more complex and harder to comprehend, but more versatile and eventually more accurate statistical/machine learning approaches. Thirdly, statistical error measures are nowadays regarded as only the first evaluation step. Since they may not necessarily reflect the economic value of reducing prediction errors, more and more often, they are complemented by case studies comparing profits from scheduling or trading strategies based on price forecasts obtained from different models. |
| Date: | 2022–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2204.11735&r= |
| By: | Blazsek, Szabolcs; Escribano, Álvaro |
| Abstract: | Climate variables are known to be subject to abrupt changes when some threshold levels are surpassed. We use data for the last 798,000 years on global ice volume (Ice), atmospheric carbon dioxide level (CO2), and Antarctic land surface temperature (Temp) to model and measure those longrun nonlinear climate effects. The climate variables have very long and asymmetric cycles, created by periods of upward trends, followed by periods of downward trends driven by exogenous orbital variables. The exogenous orbital variables considered by the Milankovitch cycles are eccentricity of Earth's orbit, obliquity, and precession of the equinox. We show that our new score-driven threshold ice-age models improve the statistical inference and forecasting performance of competing ice-age models from the literature. The drawback of using our 1,000-year frequency observations, is that we cannot measure the nonlinear climate effects of humanity created during the last 250 years, which are known to have generated abrupt structural changes in the Earth's climate, due to unprecedented high levels of CO2 and Temp, and low levels of Ice volume. On the other hand, the advantage of using low-frequency data is that they allow us to obtain long-run forecasts on what would have occurred if humanity had not burned fossil fuels since the start of the Industrial Revolution. These long-run forecasts can serve as benchmarks for the long-run evaluation of the impact of humanity on climate variables. Without the impact of humanity on climate, we predict the existence of turning points in the evolution of the three climate variables for the next 5,000 years: an upward trend in global ice volume, and downward trends in atmospheric CO2 level and Antarctic land surface temperature. |
| Keywords: | Climate Change; Ice-Ages; Global Ice Volume; Atmospheric Co2 Level; Antarctic Land Surface Temperature; Dynamic Conditional Score Models; Generalized Autoregressive Score Models |
| JEL: | C32 C38 C51 C52 C53 Q54 |
| Date: | 2022–05–10 |
| URL: | http://d.repec.org/n?u=RePEc:cte:werepe:34757&r= |
| By: | Tony Chernis; Taylor Webley |
| Abstract: | Assessing the state of the economy in real time is critical for policy-making, and understanding the risks to those assessments is equally important. Policy-makers are typically provided with point forecasts that contain insufficient information about risks. In contrast, predictive densities estimate the entire range of possible outcomes. This provides a method for quantifying not only the current state of the economy but also the degree of uncertainty, the tail risks and the overall balance of risks around that state. Accordingly, this paper extends the framework of Chernis and Sekkel (2018) to produce density nowcasts for Canadian real GDP growth. We compare several methods of combining predictive densities from 98 models representing four popular classes of nowcasting models. The performance of these combinations is then assessed in both real-time and pseudo real-time out-of-sample exercises, with the limited sample real-time simulations reinforcing the importance of data revisions for nowcasting. We demonstrate that the combined densities are reliable and accurate tools for assessing the state of the economy and risks to the outlook. We highlight in particular risks at the start of the COVID-19 pandemic. |
| Keywords: | Econometric and statistical methods |
| JEL: | C C53 E E3 E7 |
| Date: | 2022–05 |
| URL: | http://d.repec.org/n?u=RePEc:bca:bocadp:22-12&r= |