Long-Term Load Forecasting Considering Volatility Using Multiplicative Error Model

Long-term load forecasting plays a vital role for utilities and planners in terms of grid development and expansion planning. An overestimate of long-term electricity load will result in substantial wasted investment in the construction of excess power facilities, while an underestimate of future load will result in insufficient generation and unmet demand. This paper presents first-of-its-kind approach to use multiplicative error model (MEM) in forecasting load for long-term horizon. MEM originates from the structure of autoregressive conditional heteroscedasticity (ARCH) model where conditional variance is dynamically parameterized and it multiplicatively interacts with an innovation term of time-series. Historical load data, accessed from a U.S. regional transmission operator, and recession data for years 1993-2016 is used in this study. The superiority of considering volatility is proven by out-of-sample forecast results as well as directional accuracy during the great economic recession of 2008. To incorporate future volatility, backtesting of MEM model is performed. Two performance indicators used to assess the proposed model are mean absolute percentage error (for both in-sample model fit and out-of-sample forecasts) and directional accuracy.Comment: 19 pages, 11 figures, 3 table

Testing Multiplicative Error Models Using Conditional Moment Tests

We suggest a robust form of conditional moment test as a constructive test for func- tional misspecification in multiplicative error models. The proposed test has power solely against violations of the conditional mean restriction but is not affected by any other type of model misspecification. Monte-Carlo investigations show that an appro- priate choice of weighting function induces high power against various alternatives. We illustrate how to adapt the framework to test also out-of-sample moment restrictions, such as orthogonalities of prediction errors.Robust Conditional Moment Tests, Finite Sample Properties, Multiplicative Error Models, Prediction Errors

A Mixture Multiplicative Error Model for Realized Volatility

A multiplicative error model with time-varying parameters and an error term following a mixture of gamma distributions is introduced. The model is fitted to the daily realized volatility series of Deutschemark/Dollar and Yen/Dollar returns and is shown to capture the conditional distribution of these variables better than the commonly used ARFIMA model. The forecasting performance of the new model is found to be, in general, superior to that of the set of volatility models recently considered by Andersen et al. (2003) for the same data.Mixture model, Realized volatility, Gamma distribution

Forecasting Realized Volatility by Decomposition

Forecasts of the realized volatility of the exchange rate returns of the Euro against the U.S. Dollar obtained directly and through decomposition are compared. Decomposing the realized volatility into its continuous sample path and jump components and modeling and forecasting them separately instead of directly forecasting the realized volatility is shown to lead to improved out-of-sample forecasts. Moreover, gains in forecast accuracy are robust with respect to the details of the decomposition.Mixture model, Jump, Realized volatility, Gamma distribution

The Consequences of Trade Liberalisation on the Australian Passenger Motor Vehicle Industry

This paper is an appraisal of the impact of Australian trade liberalization measures on imports, exports, productivity, and internal demand of the passenger motor vehicle industry. There is clear evidence that this liberalization has increased the volume of trade, imports, exports, and productivity, but reduced the locally produced cars for internal consumption. Various models are constructed and applied. Thus, this paper is an application of the bounds testing procedure based on the ARDL approach to cointegration and the comparison of the latter with the OLS and Johansen’s cointegration methods in the contexts of small samples.Trade liberalization, Australian Passenger motor vehicle industry, ARDL approach

Employing Bayesian Vector Auto-Regression (BVAR) method as an altenative technique for forecsating tax revenue in South Africa

StatisticsM. Sc. (Statistics