A Durbin–Watson serial correlation test for ARX processes via excited adaptive tracking

Bercu, Bernard; Portier, Bruno; Vázquez, Víctor

doi:10.1080/00207179.2015.1052017

Cited by 5 publications

(6 citation statements)

References 18 publications

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“…Third, the Breusch-Godfrey LM test detected serial correlation problems. Fourth, the Durbin-Watson test was applied for autocorrelation regression measurement in residuals (Bercu et al 2015). Finally, we used the cumulative sum on the recursive residuals (CUSUM) to explain shifts in mean or variance, and CUSUM of square (CUSUMSQ) tests detected rapid changes from a constant regression coefficient (Riaz et al 2011).…”

Section: Stability Checkmentioning

confidence: 99%

Empirical Analysis on Public Expenditure for Education and Economic Growth: Evidence from Indonesia

2021

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The purpose of this paper is to find the relationship between public expenditure in the educational sector and the economic growth in Indonesia since the government decided to spend 20% of the state budget on education. We used time series data from 1988 to 2018 and the Cobb–Douglas production function as an economic theory for measurement. In the methodology, we employed Autoregressive Distributed Lag bound tests to find the relationship between variables. The results show that public expenditure on education has an insignificant relationship in the long- and short-term estimation. However, they both have different directions, which is a positive relationship in long-term and a negative relationship in short-term estimation. Meanwhile, gross fixed capital formation shows a positive relationship, and the labour variable has a negative relationship in the short and long terms. In conclusion, the Indonesian government should manage the education system regarding the relationship between education expenditure and economic growth.

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Section: Stability Checkmentioning

confidence: 99%

Empirical Analysis on Public Expenditure for Education and Economic Growth: Evidence from Indonesia

2021

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“…• the probability associated with each of the first 3 variables is less than 0.05, which highlights the fact that all variables are statistically significant; • the probability associated with the variable Regional_disparities_in_unemployment_rates ate_risk_of_poverty is 0.6484 and is therefore higher than the critical value 0.05, which shows us that it is not statistically significant; • the probability associated with the constant C is 0.0001 < 0.05, so this is also statistically significant; • the R-squared is 0.810229, and the Adjusted R-squared is 0.734320; Thus, based on the analysis of the 3 studied regression models, it is considered that the best model is Model 1 because it is the only one that has statistically significant regression equation coefficients. Moreover, Model 1 also has the Durbin-Watson [55] state value closest to 2, which shows the stability of the model. According to the AIC, SBIC and HQIC criteria, Model 1 would have been in second place at a short distance from Model 2, which, however, cannot be considered because it is not statistically significant.…”

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confidence: 74%

A Statistical Analysis of the Migration Process: A Case Study—Romania

et al. 2022

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The research aims at studying and predicting the migration process in Romania over the last 20 years and at identifying the impact of the COVID-19 pandemic. The study analyzes several models for estimating migration through linear regression, but also a VAR (Vector autoregression) analysis, as the variables can influence each other. Vector autoregression (VAR) is also used to model multivariate time series, and it can analyze the dynamics of a migration process. Therefore, the best model for forecasting the migration process in Romania is Model 1 of linear regression. This phenomenon generates many positive and negative economic, demographic and political effects. The migration process has become particularly important for Romania in the last 20 years, and its socio-economic, political and cultural effects affect the Romanian state. That is why flexible policies are needed in order to be coherent, to have as main purpose keeping specialists in the country in certain basic economic fields, as well to implement measures to determine the return of specialists and students who have left to study abroad.

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“…In Table 12, the Durbin Watson (DW) test is used to check possible autocorrelation in carbon price prediction residuals [32]. Carbon price forecast residuals of single BP, SVM, and ELM models have a positive correlation, which indicates that the single model has poor stability again.…”

Section: Residual Testmentioning

confidence: 99%

Carbon Price Prediction Based on Ensemble Empirical Mode Decomposition and Extreme Learning Machine Optimized by Improved Bat Algorithm Considering Energy Price Factors

Sun

Zhang

2020

Energies

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In response to climate change and environmental issues, many countries have gradually optimized carbon market management and improved the carbon market trading mechanism. Carbon price prediction plays a pivotal role in promoting carbon market management when investors are guided by prediction to conduct rational carbon trading. A novel carbon price prediction methodology is constructed based on ensemble empirical mode decomposition, improved bat algorithm, and extreme learning machine (EEMD-IBA-ELM) in this study. Firstly, the carbon price is decomposed into multiple regular intrinsic mode function (IMF) components by the ensemble empirical mode decomposition, and partial autocorrelation analysis (PACF) is used to find IMF historical data affecting the current value of IMF. Secondly, the improved bat algorithm (IBA) is used to heighten extreme learning machine (ELM) while adaptive parameters are obtained. Finally, EEMD-IBA-ELM was established to predict carbon price. Simultaneously, energy price fluctuation is introduced into the carbon price prediction model. As a consequence, EEMD-IBA-ELM carbon price prediction ability is further improved. In the empirical analysis, the historical carbon price of European Climate Exchange (ECX) and Korea Exchange (KRX) markets are used to examine the effectiveness and stability of the model. Errors of carbon price prediction in ECX and KRX is 2.1982% and 1.1762%, respectively. The results show that the EEMD-IBA-ELM carbon price prediction model can accurately predict carbon price when prediction effect shows strong stability. Furthermore, carbon price prediction accurateness was significantly enhanced by using energy price fluctuation as an influencing factor of carbon price prediction.

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A Durbin–Watson serial correlation test for ARX processes via excited adaptive tracking

Cited by 5 publications

References 18 publications

Empirical Analysis on Public Expenditure for Education and Economic Growth: Evidence from Indonesia

Empirical Analysis on Public Expenditure for Education and Economic Growth: Evidence from Indonesia

A Statistical Analysis of the Migration Process: A Case Study—Romania

Carbon Price Prediction Based on Ensemble Empirical Mode Decomposition and Extreme Learning Machine Optimized by Improved Bat Algorithm Considering Energy Price Factors

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