A time-varying grey Riccati model based on interval grey numbers for China's clean energy generation predicting

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sandang Guo ◽  
Yaqian Jing
Keyword(s):  
Clean Energy ◽  
Energy Generation ◽  
Random Disturbance ◽  
Time Varying ◽  
The Novel ◽  
Content Type ◽  
Time Varying Parameter ◽  
Number Sequences ◽  
Disturbance Term ◽  
Grey Number

PurposeIn order to accurately predict the uncertain and nonlinear characteristics of China's three clean energy generation, this paper presents a novel time-varying grey Riccati model (TGRM(1,1)) based on interval grey number sequences.Design/methodology/approachBy combining grey Verhulst model and a special kind of Riccati equation and introducing a time-varying parameter and random disturbance term the authors advance a TGRM(1,1) based on interval grey number sequences. Additionally, interval grey number sequences are converted into middle value sequences and trapezoid area sequences by using geometric characteristics. Then the predicted formula is obtained by using differential equation principle. Finally, the proposed model's predictive effect is evaluated by three numerical examples of China's clean energy generation.FindingsBased on the interval grey number sequences, the TGRM(1,1) is applied to predict the development trend of China's wind power generation, China's hydropower generation and China's nuclear power generation, respectively, to verify the effectiveness of the novel model. The results show that the proposed model has better simulated and predicted performance than compared models.Practical implicationsDue to the uncertain information and continuous changing of clean energy generation in the past decade, interval grey number sequences are introduced to characterize full information of the annual clean energy generation data. And the novel TGRM(1,1) is applied to predict upper and lower bound values of China's clean energy generation, which is significant to give directions for energy policy improvements and modifications.Originality/valueThe main contribution of this paper is to propose a novel TGRM(1,1) based on interval grey number sequences, which considers the changes of parameters over time by introducing a time-varying parameter and random disturbance term. In addition, the model introduces the Riccati equation into classic Verhulst, which has higher practicability and prediction accuracy.

scholarly journals A Novel Linear Time-Varying GM(1,N) Model for Forecasting Haze: A Case Study of Beijing, China

2019 ◽  
Vol 11 (14) ◽  
pp. 3832 ◽  
Author(s):  
Pingping Xiong ◽  
Jia Shi ◽  
Lingling Pei ◽  
Song Ding
Keyword(s):  
Linear Time ◽  
Dynamic Change ◽  
Time Varying ◽  
The Novel ◽  
Model Based ◽  
Number Sequences ◽  
The Government ◽  
Grey Number ◽  
Beijing China

Haze is the greatest challenge facing China’s sustainable development, and it seriously affects China’s economy, society, ecology and human health. Based on the uncertainty and suddenness of haze, this paper proposes a novel linear time-varying grey model (GM)(1,N) based on interval grey number sequences. Because the original GM(1,N) model based on interval grey number sequences has constant parameters, it neglects the dynamic change characteristics of parameters over time. Therefore, this novel linear time-varying GM(1,N) model, based on interval grey number sequences, is established on the basis of the original GM(1,N) model by introducing a linear time polynomial. To verify the validity and practicability of this model, this paper selects the data of PM10, SO2 and NO2 concentrations in Beijing, China, from 2008 to 2018, to establish a linear time-varying GM(1,3) model based on interval grey number sequences, and the prediction results are compared with the original GM(1,3) model. The result indicates that the prediction effect of the novel model is better than that of the original model. Finally, this model is applied to forecast PM10 concentration for 2019 to 2021 in Beijing, and the forecast is made to provide a reference for the government to carry out haze control.

Development of a direct NGM(1,1) prediction model based on interval grey numbers

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ye Li ◽  
Yuanping Ding ◽  
Yaqian Jing ◽  
Sandang Guo
Keyword(s):  
Genetic Algorithm ◽  
Markov Model ◽  
Prediction Model ◽  
Real Number ◽  
Content Type ◽  
Optimal Weight ◽  
Practical Applications ◽  
Number Sequences ◽  
Integral Development ◽  
Grey Number

PurposeThe purpose of this paper is to construct an interval grey number NGM(1,1) direct prediction model (abbreviated as IGNGM(1,1)), which need not transform interval grey numbers sequences into real number sequences, and the Markov model is used to optimize residual sequences of IGNGM(1,1) model.Design/methodology/approachA definition equation of IGNGM(1,1) model is proposed in this paper, and its time response function is solved by recursive iteration method. Next, the optimal weight of development coefficients of two boundaries is obtained by genetic algorithm, which is designed by minimizing the average relative error based on time weighted. In addition to that, the Markov model is used to modify residual sequences.FindingsThe interval grey numbers’ sequences can be predicted directly by IGNGM(1,1) model and its residual sequences can be amended by Markov model. A case study shows that the proposed model has higher accuracy in prediction.Practical implicationsUncertainty and volatility information is widespread in practical applications, and the information can be characterized by interval grey numbers. In this paper, an interval grey numbers direct prediction model is proposed, which provides a method for predicting the uncertainty information in the real world.Originality/valueThe main contribution of this paper is to propose an IGNGM(1,1) model which can realize interval grey numbers prediction without transforming them into real number and solve the optimal weight of integral development coefficient by genetic algorithm so as to avoid the distortion of prediction results. Moreover, the Markov model is used to modify residual sequences to further improve the modeling accuracy.

scholarly journals Construction of China’s financial conditions index in the post-crisis era

2019 ◽  
Vol 2 (2) ◽  
pp. 258-276
Author(s):  
Nan Li ◽  
Liu Yuanchun
Keyword(s):  
Financial Stability ◽  
Design Methodology ◽  
Predictive Power ◽  
Time Varying ◽  
Content Type ◽  
Vector Autoregressive ◽  
Financial Variables ◽  
Financial Conditions ◽  
Time Varying Parameter ◽  
Better Than

Purpose The purpose of this paper is to summarize different methods of constructing the financial conditions index (FCI) and analyze current studies on constructing FCI for China. Due to shifts of China’s financial mechanisms in the post-crisis era, conventional ways of FCI construction have their limitations. Design/methodology/approach The paper suggests improvements in two aspects, i.e. using time-varying weights and introducing non-financial variables. In the empirical study, the author first develops an FCI with fixed weights for comparison, constructs a post-crisis FCI based on time-varying parameter vector autoregressive model and finally examines the FCI with time-varying weights concerning its explanatory and predictive power for inflation. Findings Results suggest that the FCI with time-varying weights performs better than one with fixed weights and the former better reflects China’s financial conditions. Furthermore, introduction of credit availability improves the FCI. Originality/value FCI constructed in this paper goes ahead of inflation by about 11 months, and it has strong explanatory and predictive power for inflation. Constructing an appropriate FCI is important for improving the effectiveness and predictive power of the post-crisis monetary policy and foe achieving both economic and financial stability.

scholarly journals A time-varying approach to analysing fiscal policy and asset prices in South Africa

2014 ◽  
Vol 6 (1) ◽  
pp. 46-63 ◽  
Author(s):  
Rangan Gupta ◽  
Charl Jooste ◽  
Kanyane Matlou
Keyword(s):  
Fiscal Policy ◽  
Stock Prices ◽  
Asset Prices ◽  
Marked Change ◽  
Var Model ◽  
Time Varying ◽  
Content Type ◽  
Vector Autoregressive ◽  
Time Varying Parameter ◽  
The Impact

Purpose – This paper aims to study the interplay of fiscal policy and asset prices in a time-varying fashion. Design/methodology/approach – Using South African data since 1966, the authors are able to study the dynamic shocks of both fiscal policy and asset prices on asset prices and fiscal policy based on a time-varying parameter vector autoregressive (TVP-VAR) model. This enables the authors to isolate specific periods in time to understand the size and sign of the shocks. Findings – The results seem to suggest that at least two regimes exist in which expansionary fiscal policy affected asset prices. From the 1970s until 1990, fiscal expansions were associated with declining house and slightly increased stock prices. The majority of the first decade of 2000 had asset prices increasing when fiscal policy expanded. On the other hand, increasing asset prices reduced deficits for the majority of the sample period, while the recent financial crises had a marked change on the way asset prices affect fiscal policy. Originality/value – This is the first attempt in the literature of fiscal policy and asset prices to use a TVP-VAR model to not only analyse the impact of fiscal policy on asset prices, but also the feedback from asset prices to fiscal policy over time.

Structural change and economic growth in India: a comparative study of Punjab

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Anirban Sanyal ◽  
Nirvikar Singh
Keyword(s):  
Economic Growth ◽  
Structural Change ◽  
Structural Changes ◽  
Green Revolution ◽  
State Level ◽  
Productivity Change ◽  
Time Varying ◽  
Content Type ◽  
Panel Regressions ◽  
Time Varying Parameter

Purpose The Green Revolution transformed agriculture in the Indian State of Punjab, with positive spillovers to the rest of India, but recently the state’s economy has fallen dramatically in rankings of per capita state output. Understanding the trajectory of Punjab’s economy has important lessons for all of India. Economic development is typically associated with changes in economic structure, but Punjab has remained relatively reliant on agriculture rather than shifting economic activity to manufacturing and services, where productivity growth might be greater. Design/methodology/approach The authors empirically examine structural change in the Punjab economy in the context of structural change and economic growth across the States of India. The authors calculate structural change indices and map their pattern over time. The authors estimate panel regressions and time-varying parameter regressions, as well as performing productivity change decompositions into within-sector and structural changes. Findings Panel regressions and time-varying-coefficient regressions suggest a significant positive influence of structural change on state-level growth. In addition, growth positively affected structural change across India’s states. The relative lack of structural change in Punjab’s economy is implicated in its relatively poor recent growth performance. Comparisons with a handful of other states reinforce this conclusion: Punjab’s lack of economic diversification is a plausible explanation for its lagging economic performance. Originality/value This paper performs a novel empirical analysis of structural change and growth, simultaneously using three different approaches: panel regressions, time-varying parameter regressions and productivity decompositions. To the best of the authors’ knowledge, it is the only paper we are aware of that combines these three approaches.

A NEW PHYSICAL EFFECT MODELED BY AN IKEDA MAP DEPENDING ON A MONOTONICALLY TIME-VARYING PARAMETER

1999 ◽  
Vol 09 (06) ◽  
pp. 1111-1120 ◽  
Author(s):  
JOHN ARGYRIS ◽  
CORNELIU CIUBOTARIU
Keyword(s):  
Magnetic Field ◽  
Electromagnetic Wave ◽  
Charged Particle ◽  
Longitudinal Component ◽  
Time Varying ◽  
The Novel ◽  
A Value ◽  
Time Varying Parameter ◽  
Consecutive Phase ◽  
Constant External Magnetic Field

In this paper we study the dynamics of a charged particle in a constant external magnetic field and the field due to a polarized electromagnetic wave. We observe a new phenomenon, the chaotic gun effect, which appears in the case of a sufficiently large amplitude of the wave. We show that the longitudinal component of the momentum undergoes oscillations with a chaotic modulation of the amplitude which increases suddenly to a value which remains constant between two consecutive phase Larmor circles. The novel effect may be modeled by a time-regressive system associated with an Ikeda map depending on a monotonically time-varying parameter.

Forecasting smog in Beijing using a novel time-lag GM(1,N) model based on interval grey number sequences

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jia Shi ◽  
Pingping Xiong ◽  
Yingjie Yang ◽  
Beichen Quan
Keyword(s):  
Time Lag ◽  
Upper And Lower Bounds ◽  
Simulation Accuracy ◽  
Content Type ◽  
Model Based ◽  
Proposed Model ◽  
Lag Effect ◽  
Number Sequences ◽  
The Government ◽  
Grey Number

PurposeSmog seriously affects the ecological environment and poses a threat to public health. Therefore, smog control has become a key task in China, which requires reliable prediction.Design/methodology/approachThis paper establishes a novel time-lag GM(1,N) model based on interval grey number sequences. Firstly, calculating kernel and degree of greyness of the interval grey number sequence respectively. Then, establishing the time-lag GM(1,N) model of kernel and degree of greyness sequences respectively to obtain their values after determining the time-lag parameters of two models. Finally, the upper and lower bounds of interval grey number sequences are obtained by restoring the values of kernel and degree of greyness.FindingsIn order to verify the validity and practicability of the model, the monthly concentrations of PM2.5, SO2 and NO2 in Beijing during August 2017 to September 2018 are selected to establish the time-lag GM(1,3) model for kernel and degree of greyness sequences respectively. Compared with three existing models, the proposed model in this paper has better simulation accuracy. Therefore, the novel model is applied to forecast monthly PM2.5 concentration for October to December 2018 in Beijing and provides a reference basis for the government to formulate smog control policies.Practical implicationsThe proposed model can simulate and forecast system characteristic data with the time-lag effect more accurately, which shows that the time-lag GM(1,N) model proposed in this paper is practical and effective.Originality/valueBased on interval grey number sequences, the traditional GM(1,N) model neglects the time-lag effect of driving terms, hence this paper introduces the time-lag parameters into driving terms of the traditional GM(1,N) model and proposes a novel time-lag GM(1,N) model.

MGM(1,m) model based on interval grey number sequence and its applications

2017 ◽  
Vol 7 (3) ◽  
pp. 310-319 ◽  
Author(s):  
Pingping Xiong ◽  
Yue Zhang ◽  
Bo Zeng ◽  
Tian-Xiang Yao
Keyword(s):  
Prediction Models ◽  
Development Trend ◽  
Upper And Lower Bounds ◽  
Number Sequence ◽  
Content Type ◽  
Model Based ◽  
Grey Forecasting ◽  
Number Sequences ◽  
Grey Number ◽  
Predicted Values

Purpose Aiming at the traditional multivariate grey forecasting model only considers the modelling of real numbers; therefore, the purpose of this paper is to construct an MGM(1, m) model based on the interval grey number sequences according to the grey modelling theory. Design/methodology/approach First, the multivariable grey number sequences are transformed into the kernel and grey radius sequences which are two feature sequences of interval grey number sequences. Then the MGM(1, m) model for kernel sequences and grey radius sequences are established, respectively. Finally, the simulation and prediction of the upper and lower bounds of the interval grey number sequences are realized by the reductive calculation of the predicted values of the kernel and grey radius. Findings The model is applied to the prediction of visibility and relative humidity, the identification factors of the haze. The results show that the model has high accuracy on the simulation and prediction of multivariable grey number sequences, which is reasonable and practical. Originality/value The main contribution of this paper is to propose a method to simulate and forecast the multivariable grey number sequence that is to establish the prediction models for the whitening sequences of multivariable grey number sequences which are kernel and grey radius sequences and extend the possibility boundary of kernel by grey radius. The model can reflect the development trend of multivariable grey number sequence accurately. When the grey information is continuously complemented, the multivariable grey number prediction model is transformed into the traditional MGM(1, m) model. Therefore, the MGM(1, m) model based on interval grey number sequence is the generalisation and expansion of the traditional MGM(1, m) model.

scholarly journals Does Investor Sentiment Affect Clean Energy Stock? Evidence from TVP-VAR-Based Connectedness Approach

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3442
Author(s):  
Tiantian Liu ◽  
Shigeyuki Hamori
Keyword(s):  
Natural Gas ◽  
Crude Oil ◽  
Investor Sentiment ◽  
Clean Energy ◽  
Var Model ◽  
Time Varying ◽  
Parameter Vector ◽  
Vector Autoregressive ◽  
Time Varying Parameter ◽  
Rolling Window

We investigated the connectedness of the returns and volatility of clean energy stock, technology stock, crude oil, natural gas, and investor sentiment based on the time-varying parameter vector autoregressive (TVP-VAR) connectedness approach. The empirical results indicate that the average total connectedness is higher in the volatility system than in the return system. The investor sentiment has a weak impact on clean energy stock. Our results show that the dynamic total connectedness across assets in the system varies with time. Furthermore, the dynamic total connectedness increases significantly during financial turmoil. Dynamic total volatility connectedness is more sensitive to financial turmoil. By comparing the connectedness estimated by the TVP-VAR model with the rolling-window VAR model, we find the dynamic total return connectedness of the TVP-VAR model is similar to the estimated results of a 200 day rolling-window VAR model.

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