Modelling and forecasting inflation in Egypt: univariate and multivariate approaches

Ahmed, Doaa Akl; Abdelsalam, Mamdouh Abdelmoula M.

doi:10.1080/17938120.2017.1293361

Cited by 3 publications

(2 citation statements)

References 21 publications

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“…Other papers have used León et al. (2005) method to model credit spreads (Alizadeh & Gabrielsen, 2013), exchange rates (Kräussl et al., 2016), inflation (Akl Ahmed & Abdelsalam, 2017), asset returns (Narayan & Liu, 2018; Wu & Xie, 2021) to estimate VaR (Wu et al., 2020), and Wang et al. (2012) who proposed a method similar to León et al.…”

Section: Autoregressive Conditional Density (Arcd) and Autoregressive...mentioning

confidence: 99%

Modeling time‐varying higher‐order conditional moments: A survey

Soltyk

Chan²

2021

Journal of Economic Surveys

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Since the introduction of the Autoregressive Conditional Heteroscedasticity (ARCH) model, the literature on modeling the time‐varying second‐order conditional moment has become increasingly popular in the last four decades. Its popularity is partly due to its success in capturing volatility in financial time series, which is useful for modeling and predicting risk for financial assets. A natural extension of this is to model time variation in higher‐order conditional moments, such as the third and fourth moments, which are related to skewness and kurtosis (tail risk). This leads to an emerging literature on time‐varying higher‐order conditional moments in the last two decades. This paper outlines recent developments in modeling time‐varying higher‐order conditional moments in the economics and finance literature. Using the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) framework as a foundation, this paper provides an overview of the two most common approaches for modeling time‐varying higher‐order conditional moments: autoregressive conditional density (ARCD) and autoregressive conditional moment (ARCM). The discussion covers both the theoretical and empirical aspects of the literature. This includes the identification of the associated skewness–kurtosis domain by using the solutions to the classical moment problems, the structural and statistical properties of the models used to model the higher‐order conditional moments and the computational challenges in estimating these models. We also advocate the use of a maximum entropy density (MED) as an alternative method, which circumvents some of the issues prevalent in these common approaches.

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Section: Autoregressive Conditional Density (Arcd) and Autoregressive...mentioning

confidence: 99%

Modeling time‐varying higher‐order conditional moments: A survey

Soltyk

Chan²

2021

Journal of Economic Surveys

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“…Artificial neural network algorithm has been widely recognized and applied in the HVAC industry as a new research tool [14]. Moreover, in the prediction of energy consumption of public buildings, experts and scholars have not only used one method, but also used a combination of methods to predict energy consumption, and achieved excellent results [15], [16]. Some scholars based on the MLR method to predict and evaluate the power consumption of the office building group, and proposed to effectively reduce the energy consumption of office buildings, which is an important method to reduce energy consumption [17].…”

Section: Introductionmentioning

confidence: 99%

Research on Deep Learning Energy Consumption Prediction Based on Generating Confrontation Network

Fan

2019

IEEE Access

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In the context of increasingly tight energy supply and rising prices, it is of great significance to carry out research on energy consumption prediction models with energy conservation as the goal. In order to improve energy efficiency, it is not only necessary to conduct statistics and analysis on energy historical data, but also to predict future energy data. In this paper, Bicubic interpolation algorithm and convolutional neural network are used to spatially predict energy consumption. The model framework structure for energy consumption prediction is given, and the two models are experimentally analyzed. The results show that the convolutional neural network is better than the Bicubic interpolation method, and the prediction result is closer to the actual value. For the high spatial complexity of energy consumption data, the definition and framework of the RessNetGAN model are first given. Secondly, the specific structure of the generator is given. How to extract the spatial eigenvalue of energy consumption through 3D convolution operation is introduced, and the experiment is carried out by changing the pooling parameters of the energy consumption to be predicted. The results show that the RessNetGAN model with generated confrontation structure has better prediction performance than the convolutional neural network model, and when the input energy consumption data is very low precision, there will be no serious distortion of the prediction result. INDEX TERMS Energy consumption forecasting, deep learning, generating confrontation networks, spatial domain reconstruction.

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Weighting Approaches in Data Mining and Knowledge Discovery: A Review

Hajirahimi

Khashei

2023

Neural Process Lett

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Modelling and forecasting inflation in Egypt: univariate and multivariate approaches

Cited by 3 publications

References 21 publications

Modeling time‐varying higher‐order conditional moments: A survey

Modeling time‐varying higher‐order conditional moments: A survey

Research on Deep Learning Energy Consumption Prediction Based on Generating Confrontation Network

Weighting Approaches in Data Mining and Knowledge Discovery: A Review

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