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on Forecasting |
| By: | Philip Beran; Arne Vogler (Chair for Management Sciences and Energy Economics, University of Duisburg-Essen) |
| Abstract: | Forecasting hourly electricity prices and their characteristic properties is a core challenge for energy generation companies and trading houses. The short-term marketing and purchase of electricity is usually managed with standardized products traded on different markets and with specific temporal resolution and maturity. The size and scope of the electricity price forecasting literature has grown significantly in recent years, with the majority of studies focused on short-term (intraday and day-ahead) or long-term (investment decisions) periods. However, the literature for forecasting the period beyond the day-ahead horizon, which is relevant for trading the aforementioned products or for managing assets over several days, is rather scarce. Our paper fills this gap by developing individual forecasting models covering horizons from the day ahead up to a week ahead. We introduce hybrids of a parsimonious fundamental model and various popular econometric models. In a case study for the German day-ahead market in 2016 we test and compare the different model settings by carefully considering realistic available data and limiting the calculation time to fit typical trading time constraints. We find that the best models across the individual horizons and across all horizons jointly are hybrid model approaches. They combine the strengths of autoregressive models in terms of capturing daily - even non-linear-structures with the immediate reactions of fundamental models to short-term events or fundamental changes in the market. |
| Keywords: | Electricity markets, Electricity Price Forecasting, Hybrid Modeling, Fundamental Modeling, Econometric Modeling, German Day-Ahead Market |
| JEL: | C13 C22 C51 Q41 Q47 |
| Date: | 2021–10 |
| URL: | http://d.repec.org/n?u=RePEc:dui:wpaper:2102&r= |
| By: | Juan C. Méndez-Vizcaíno; Alexander Guarin; César Anzola-Bravo; Anderson Grajales-Olarte |
| Abstract: | Since July 2021, Banco de la República strengthened its forecasting process and communication instruments, by involving predictive densities in the projections of its models, PATACON and 4GM. This paper presents the main theoretical and empirical elements of the predictive density approach for macroeconomic forecasting. This model-based methodology allows to characterize the balance of risks of the economy, and to quantify their effects through a joint probability distribution of forecasts. We estimate this distribution based on the simulation of DSGE models, preserving the general equilibrium relationships and their macroeconomic consistency. We also illustrate the technical criteria used to represent prospective factors of risk through the probability distributions of shocks. **** RESUMEN: Desde julio de 2021, el Banco de la República fortaleció su proceso de pronóstico y sus instrumentos de comunicación al incorporar densidades predictivas en las proyecciones de sus modelos, PATACON y 4GM. Este artículo presenta los principales elementos teóricos y empíricos del enfoque de densidad predictiva para los pronósticos macroeconómicos. Esta metodología basada en modelos permite caracterizar el balance de riesgos de la economía y cuantificar sus efectos mediante una distribución de probabilidad conjunta de los pronósticos. Esta distribución se estima mediante la simulación de los modelos DSGE, preservando las relaciones de equilibrio general y la coherencia macroeconómica. También se ilustran los criterios técnicos utilizados para representar los factores de riesgo prospectivos a través de las distribuciones de probabilidad de los choques. |
| Keywords: | Macroeconomic forecasts, balance of risks, uncertainty, Bayesian forecasting, monetary policy models, Pronósticos macroeconómicos, balance de riesgos, incertidumbre, pronósticos bayesianos, modelos de política monetaria |
| JEL: | C11 C53 E17 E52 |
| Date: | 2021–10 |
| URL: | http://d.repec.org/n?u=RePEc:bdr:borrec:1178&r= |
| By: | Yayi Yan; Jiti Gao; Bin Peng |
| Abstract: | Vector autoregressive (VAR) models are widely used in practical studies, e.g., forecasting, modelling policy transmission mechanism, and measuring connection of economic agents. To better capture the dynamics, this paper introduces a new class of time-varying VAR models in which the coefficients and covariance matrix of the error innovations are allowed to change smoothly over time. Accordingly, we establish a set of theories, including the impulse responses analyses subject to both of the short-run timing and the long-run restrictions, an information criterion to select the optimal lag, and a Wald-type test to determine the constant coefficients. Simulation studies are conducted to evaluate the theoretical findings. Finally, we demonstrate the empirical relevance and usefulness of the proposed methods through an application to the transmission mechanism of U.S. monetary policy. |
| Keywords: | multivariate dynamic time series, time-varying impulse response, testing for parameter stability |
| JEL: | C14 C32 E52 |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:msh:ebswps:2021-17&r= |
| By: | Juan Antolín-Díaz; Ivan Petrella; Juan F. Rubio-Ramírez |
| Abstract: | A long tradition in macro-finance studies the joint dynamics of aggregate stock returns and dividends using vector autoregressions (VARs), imposing the cross-equation restrictions implied by the Campbell-Shiller (CS) identity to sharpen inference. We take a Bayesian perspective and develop methods to draw from any posterior distribution of a VAR that encodes a priori skepticism about large amounts of return predictability while imposing the CS restrictions. In doing so, we show how a common empirical practice of omitting dividend growth from the system amounts to imposing the extra restriction that dividend growth is not persistent. We highlight that persistence in dividend growth induces a previously overlooked channel for return predictability, which we label “dividend momentum.” Compared to estimation based on OLS, our restricted informative prior leads to a much more moderate, but still significant, degree of return predictability, with forecasts that are helpful out-of-sample and realistic asset allocation prescriptions with Sharpe ratios that out-perform common benchmarks. |
| Date: | 2021–11 |
| URL: | http://d.repec.org/n?u=RePEc:fda:fdaddt:2021-14&r= |