Modified Schwarz and Hannan–Quinn information criteria for weak VARMA models

Maïnassara, Yacouba Boubacar; Kokonendji, Célestin C.

doi:10.1007/s11203-015-9123-z

Cited by 14 publications

(9 citation statements)

References 25 publications

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“…We also presented statistical information about the Schwarz criterion (it is a criterion for model selection among a finite set of models), Hannan-Quinn criterion (it is an alternative to the Akaike information criterion) and Durbin-Watson (statistic to detect the presence of autocorrelation at lag 1 in the residuals). This statistical information is according to Maïnassara and Kokonendji (2016).…”

Section: Results Presentation and Analysismentioning

confidence: 99%

Competitors’ cost analysis: a study with companies of the Brazilian sugarcane industry

Baiochi

Severgnini

Batista

et al. 2018

CyA

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The paper's goal is to analyze the main predictors of the cost of goods sold based on the Competitors' Cost Analysis Technique and to verify the influence of capital structure and the capital asset on the cost of the goods sold. We collected data from Economatica and Fundamentus databases. We extracted the data from quarterly Balance Sheets and Income Statements by Brazilians companies, such as Biosev S.A., Cosan S.A. and São Martinho S.A. We used the Cost of Goods Sold (CGS) as the dependent variable in two regression models and for the explanatory variables, we used revenue, capital assets and capital structure measured by the total liability divided by total asset plus the debt-to-equity ratio. We conclude that the variable costs have the greatest influence on the cost structure of Biosev S.A. (68% of CGS) and of Cosan S.A. (79% of CGS). Capital structure and capital assets had an impact on the CGS. The capital assets and the total debt ratio plus the equity ratio presented a direct main effect and the debt-to-equity ratio presented a proportionally inverse effect.

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Section: Results Presentation and Analysismentioning

confidence: 99%

Competitors’ cost analysis: a study with companies of the Brazilian sugarcane industry

Baiochi

Severgnini

Batista

et al. 2018

CyA

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“…Among the three measures, it is reported that the SBC can perform better when dealing with high-dimensional dataset, as presented the proposed SBC index has provided the lowest lag, while the AIC index has resulted in the largest lag among all three criteria [34]. In addition, HQC has offered an average lag in comparison with the SBC and AIC indexes [33,37]. To determine the lag length, the Schwarz-Bayesian model has been used to obliterate the correlation between the residual.…”

Section: Statistical Specification Variablesmentioning

confidence: 93%

“…Determining optimal dependent variables for regression and elimination of the correlation between disturbance terms, among all three criteria (AIC, SBC, and HQC), see [34][35][36][37]. It is likely to obtain different lags which can result in uncommon answers.…”

Section: Determining the Optimal Lagmentioning

confidence: 99%

Evaluation of Energy Price Liberalization in Electricity Industry: A Data-Driven Study on Energy Economics

Tabatabaei

Asef

2021

Energies

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This study aims to evaluate the effects of price liberalization on energy consumption intensity, because price liberalization leads to improved productivity, energy consumption management, and consumption reform. Although the energy consumption of large-scale factories and industries has increased dramatically, and the energies losses have an increment in the power transmission lines, this policy can result in decreasing the energy consumption intensity due to the changes in consumption patterns. If there is a correlation between two variables, the price can be a valid variable to control cost and increase consumption efficiency. The augmented Dickey–Fuller (ADF) and the Chi-squared tests are also employed to investigate the maneuverability of these variables in the first-order contrast. In this case study, the energy consumption intensity response to price changes using the data gathered between 1988–2020, has gained a confidence interval of these reactions at 95%. The proposed vector autoregressive (VAR) model has forecasted the action and reaction of the end-user, to investigate the future shocks between 2020–2050, considering a new price shock, in the Iranian energy market for the first time. The research findings have shown that energy price liberalization leads to the energy intensity improvement, however, the end-user (shocking) reactions should be investigated to implement a more sustainable policy that eases the new energy price rises.

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“…We checked the quality assessment of ARDL models on the following criteria: Akaike criterion (AIC) (Akaike 1981), Bayesian information criterion (BIC) (Watanabe 2013), and the Hannan-Quinn criterion (Mainassara and Kokonendji 2016). It was necessary to evaluate the model with autoregressive terms, taking into account the presence of autocorrelation of errors.…”

Section: Databank Of Thementioning

confidence: 99%

“…The RESET test used one lag, which indicated cointegration between three variables. A measure of the relative quality of the model was assessed using the Akaike criterion (AIC) (Akaike 1981), Bayesian information criterion (BIC) (Watanabe 2013), Hannan-Quinn criterion (Mainassara and Kokonendji 2016), the Engle-Granger cointegration test (Engle and Granger 1987). All tests confirmed the homoscedasticity of the residues (p > 0.05).…”

Section: Databank Of Thementioning

confidence: 99%

Foreign Direct Investment (FDI), Investment in Construction and Poverty in Economic Crises (Denmark, Italy, Germany, Romania, China, India and Russia)

Sukhadolets¹,

Stupnikova

Fomenko

et al. 2021

Economies

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This study aims to examine the impact of foreign direct investment (FDI), investment in construction and poverty in various countries. The Russian Federation invests heavily in construction and it is located both in Europe and Asia. Russia is usually described as a European country (while 70% of its territory is in Northern Asia, 80% of the population resides in Europe). That is why in this document both developed and emerging countries are considered; the former are represented by the EU members of different economic levels and the latter by BRICS countries. We looked at economically different countries to determine the best differentiated data in order to answer the question: “Why does a high level of poverty persist in Russia if Russian officials have repeatedly reaffirmed their commitment to the implementation of the Sustainable Development Goals (SDGs) by investing heavily in construction and attracting FDI?”. For the estimation, we used an autoregressive distributed lag (ARDL), considering cointegration and heteroscedasticity, in which the current values of the series depend both on the past values of this series and on the current and past values of other time series. Having received statistical data, we were able to compare the economic development of countries with some economic growth theories. 4–5% FDI share of the GDP helps to contain the negative impact of financial crises. Investment in construction supports the economies of countries in the long term and maintains or reduces the poverty level by increasing the assets of the population. Empirical data also helped us to evaluate the economic growth patterns and poverty in these seven countries. China and the Russian Federation will find themselves at different “poles”. China uses several theories and models simultaneously for economic development and poverty reduction and the Russian Federation does not keep to an established theory or a model of economic growth.

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Modified Schwarz and Hannan–Quinn information criteria for weak VARMA models

Cited by 14 publications

References 25 publications

Competitors’ cost analysis: a study with companies of the Brazilian sugarcane industry

Competitors’ cost analysis: a study with companies of the Brazilian sugarcane industry

Evaluation of Energy Price Liberalization in Electricity Industry: A Data-Driven Study on Energy Economics

Foreign Direct Investment (FDI), Investment in Construction and Poverty in Economic Crises (Denmark, Italy, Germany, Romania, China, India and Russia)

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