scholarly journals Multiplicative error model on northern hemispheric volcanic sulfate and European temperature anomalies

2020 ◽  
Vol 62 (6) ◽  
Author(s):  
Nazario Tartaglione
Keyword(s):  
Error Model ◽  
Temperature Anomalies ◽  
Multiplicative Error ◽  
Northern Hemispheric

scholarly journals Importance of modelling heteroscedasticity of survey index data in fishery stock assessments

2014 ◽  
Vol 72 (1) ◽  
pp. 130-136 ◽  
Author(s):  
Saang-Yoon Hyun ◽  
Mark N. Maunder ◽  
Brian J. Rothschild
Keyword(s):  
Coefficient Of Variation ◽  
Likelihood Function ◽  
Error Model ◽  
Fish Stock ◽  
Additional Parameter ◽  
Traditional Practice ◽  
Multiplicative Error ◽  
Additive Error ◽  
Stochastic Error ◽  
Stock Assessments

Abstract Many fish stock assessments use a survey index and assume a stochastic error in the index on which a likelihood function of associated parameters is built and optimized for the parameter estimation. The purpose of this paper is to evaluate the assumption that the standard deviation for the difference in the log-transformed index is approximately equal to the coefficient of variation of the index, and also to examine the homo- and heteroscedasticity of the errors. The traditional practice is to assume a common variance of the index errors over time for estimation convenience. However, if additional information is available about year-to-year variability in the errors, such as year-to-year coefficient of variation, then we suggest that the heteroscedasticity assumption should be considered. We examined five methods with the assumption of a multiplicative error in the survey index and two methods with that of an additive error in the index: M1, homoscedasticity in the multiplicative error model; M2, heteroscedasticity in the multiplicative error model; M3, M2 with approximate weighting and an additional parameter for scaling variance; M4–M5, pragmatic practices; M6, homoscedasticity in the additive error model; M7, heteroscedasticity in the additive error model. M1–M2 and M6–M7 are strictly based on statistical theories, whereas M3–M5 are not. Heteroscedasticity methods M2, M3, and M7 consistently outperformed the other methods. However, we select M2 as the best method. M3 requires one more parameter than M2. M7 has problems arising from the use of the raw scale as opposed to the logarithm transformation. Furthermore, the fitted survey index in M7 can be negative although its domain is positive.

scholarly journals Long-Term Electricity Load Forecasting Considering Volatility Using Multiplicative Error Model

Energies ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 3308 ◽  
Author(s):  
Swasti Khuntia ◽  
Jose Rueda ◽  
Mart van der Meijden
Keyword(s):  
Load Forecasting ◽  
Error Model ◽  
Economic Research ◽  
Arch Model ◽  
Multiplicative Error ◽  
Directional Accuracy ◽  
Out Of Sample ◽  
Electricity Load ◽  
Electricity Load Forecasting

Long-term electricity 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 on the construction of excess power facilities, while an underestimate of the future load will result in insufficient generation and inadequate demand. As a first of its kind, this research proposes the use of a multiplicative error model (MEM) in forecasting electricity load for the 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, as accessed from a United States (U.S.) regional transmission operator, and recession data, accessed from the National Bureau of Economic Research, are 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. Historical volatility is used to account for implied volatility. To incorporate future volatility, backtesting of MEM is performed. Two performance indicators used to assess the proposed model are: (i) loss functions in terms of mean absolute percentage error and mean squared error (for both in-sample model fit and out-of-sample forecasts) and (ii) directional accuracy.

A Multiplicative Error Model with Heterogeneous Components for Forecasting Realized Volatility

2015 ◽  
Vol 34 (3) ◽  
pp. 209-219 ◽  
Author(s):  
Heejoon Han ◽  
Myung D. Park ◽  
Shen Zhang
Keyword(s):  
Realized Volatility ◽  
Error Model ◽  
Multiplicative Error

scholarly journals Impact of Arctic sea ice variations on winter temperature anomalies in northern hemispheric land areas

2018 ◽  
Vol 52 (5-6) ◽  
pp. 3111-3137 ◽  
Author(s):  
T. Koenigk ◽  
Y. Gao ◽  
G. Gastineau ◽  
N. Keenlyside ◽  
T. Nakamura ◽  
...  
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
Winter Temperature ◽  
Temperature Anomalies ◽  
Arctic Sea ◽  
Northern Hemispheric
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