Suchergebnisse

Tax compliance is an important indicator for the proper functioning of the tax authority, influencing the budget revenue level. In this study, a Vector Error Correction Model (VECM) analysis was developed to identify the long-term relationships between the compliance in individual income taxation (taxpayer's behavior), public trust in politicians (trust in authorities), and rule of law (power of the authorities), using unbalanced panel data for the European Union (EU28) during the 2007–2017 period. The results underline the causality of the long-run relationships between the variables. The results of the VECM analysis underline the need for various support measures for voluntary tax compliance, with the trust variable having an important impact on tax compliance. In addition, a Structural Equation Modeling (SEM) analysis was employed using an improved data set with variables such as the compliance in corporation taxation (taxpayer's behavior), wastefulness of government spending, and quality of the education system. The results of the SEM analysis underline the positive and significant influences of the variables on tax compliance.

Cointegration between government spending and output is rarely considered in fiscal re­search. Motivated by this potential long ­run relationship, the paper focuses on separating temporary from permanent shocks to government spending using a SVECM. In particular,thisdecompositionrevealsthatgovernmentexpendituredataisindeedamixofstabilisationinterventions and responses to economic growth. The interpretation of these shocks is thenusedtoinfertheconsequencesoftemporaryincreasesingovernmentspending. Controlling for cointegration delivers results consistent with existent literature, yet the effects seem to be less persistent as the impact on output rapidly converges to zero.

This research aims to analyze the relationships between causal factors likely to affect future CO2 emissions from the Thai transportation sector by developing the Structural Equation Modeling-Vector Autoregressive Error Correction Mechanism Model (SEM-VECM Model). This model was created to fill information gaps of older models. In addition, the model provides the unique feature of viable model application for different sectors in various contexts. The model revealed all exogenous variables that have direct and indirect influences over changes in CO2 emissions. The variables show a direct effect at a confidence interval of 99%, including per capita GDP (), labor growth (), urbanization rate factor (), industrial structure (), energy consumption (), foreign direct investment (), oil price (), and net exports (). In addition, it was found that every variable in the SEM-VECM model has an indirect effect on changes in CO2 emissions at a confidence interval of 99%. The SEM-VECM model has the ability to adjust to the equilibrium equivalent to 39%. However, it also helps to identify the degree of direct effect that each causal factor has on the others. Specifically, labor growth () had a direct effect on per capita GDP () and energy consumption () at a confidence interval of 99%, while urbanization rate () had a direct effect on per capita GDP (), labor growth (), and net exports () at a confidence interval of 99%. Furthermore, industrial structure () had a direct effect on per capita GDP () at a confidence interval of 99%, whereas energy consumption () had a direct effect on per capita GDP () at a confidence interval of 99%. Foreign direct investment () had a direct effect on per capita GDP () at a confidence interval of 99%, while oil price () had a direct effect on industrial structure (), energy consumption (), and net exports () at a confidence interval of 99%. Lastly, net exports () had a direct effect on per capita GDP () at a confidence interval of 99%. The model eliminates the problem of heteroskedasticity, multicollinearity, and autocorrelation. In addition, it was found that the model is white noise. When the SEM-VECM Model was used for 30-year forecasting (2018–2047), it projected that CO2 emissions would increase steadily by 67.04% (2047/2018) or 123.90 Mt CO2 Eq. by 2047. The performance of the SEM-VECM Model was assessed and produced a mean absolute percentage error (MAPE) of 1.21% and root mean square error (RMSE) of 1.02%. When comparing the performance value with the values of other, older models, the SEM-VECM Model was found to be more effective and useful for future research and policy planning for Thailand's sustainability goals.

Electricity is the key in most production processes, therefore understanding causality, cointegration, and stationarity between electricity consumption and production is the starting point in the debate on its economic effects. Vector error correction model (VECM) and cointegration models, besides stationarity tests with structural break, were used to examine this relationship in Mexico over the period 1940-2018. Results support the hypothesis but only after to consider the structural change highlighted by the trade opening, since causality, stationarity, and cointegration can only be demonstrated by partitioning the period by 1985, the production per capita breakpoint. In the first stage of the series, causality ran from electricity to product, while in the second stage it was bidirectional. It is recommended to adapt the electricity programs to changes in the political arena. The originality of this contribution lies in the long-term analysis of the energy sector, emphasizing the importance of the breakpoints. Despite of some sensitivity performing the regression analysis, the conclusions recommend a strengthening of the energy sector as a feasible means of recovering the sustained growth achieved by Mexico in other times. ; Causalidad y estacionariedad con cambio estructural en consumo de electricidad y PIB per cápita en MéxicoLa electricidad es clave en la mayoría de los procesos de producción, por tanto, entender causalidad, cointegración y estacionariedad entre consumo de electricidad y producción es un punto de partida en el debate de sus efectos económicos. Modelos de corrección de errores (VECM) y cointegración, junto a pruebas de estacionariedad, examinan esta relación en México durante 1940-2018. Los resultados apoyan esta hipótesis, pero después de considerar el cambio estructural subrayado por la apertura comercial, ya que causalidad, estacionariedad y cointegración solo pueden demostrarse dividiendo el periodo en 1985, fecha de quiebre estimada para producto per cápita. En la primera etapa, la causalidad corrió de electricidad a producto, mientras que en la segunda fue bidireccional. Se recomienda adaptar los programas de electricidad a cambios en la esfera política. La originalidad de esta contribución descansa en el análisis de largo plazo del sector de energía enfatizando la importancia de quiebres estructurales. A pesar de alguna sensibilidad al ejecutar las regresiones, las conclusiones recomiendan fortalecer el sector de energía como medio factible de recuperar el crecimiento sostenido que México alcanzó en otros tiempos.

The current study aims to examine the impact of structural breaks on price discovery efficiency of Indian equity futures market. Global financial crisis, change of Government, demonetization and COVID-19 are identified as significant events. Data is divided into sub-samples of pre and post event period to study the impact of these events on price discovery efficiency of the Indian equity futures market. Unit root test is used to check stationarity of data. Granger causality test, Johansen's cointegration test and Vector error correction methodology (VECM) are used for analysis. During full sample period, it is observed that there is a significant bi-directional causality between cash and futures markets and cash market leads futures market in price discovery. In addition, global financial crisis triggered volatility in Indian equity futures market, which reduced its price discovery efficiency, whereas, after change in Government, bidirectional transmission of information restored between cash market and futures market. Furthermore, futures market played a leading role in absorbing volatility triggered by demonetization. COVID-19 did not significantly affect price discovery efficiency of Indian equity futures market.

The current study aims to examine the impact of structural breaks on price discovery efficiency of Indian equity futures market. Global financial crisis, change of Government, demonetization and COVID-19 are identified as significant events. Data is divided into sub-samples of pre and post event period to study the impact of these events on price discovery efficiency of the Indian equity futures market. Unit root test is used to check stationarity of data. Granger causality test, Johansen's cointegration test and Vector error correction methodology (VECM) are used for analysis. During full sample period, it is observed that there is a significant bi-directional causality between cash and futures markets and cash market leads futures market in price discovery. In addition, global financial crisis triggered volatility in Indian equity futures market, which reduced its price discovery efficiency, whereas, after change in Government, bidirectional transmission of information restored between cash market and futures market. Furthermore, futures market played a leading role in absorbing volatility triggered by demonetization. COVID-19 did not significantly affect price discovery efficiency of Indian equity futures market.

This paper investigates the effects of debts and budgetary deficit on real variables using structural Vector Error Correction Model (VECM) method with long-run restrictions. We compare our estimates of the impulse responses with those based on levels Vector Auto-Regressive (VAR) with standard recursive order restrictions. The test is conducted on the Malaysian data covering the period of 1962-2006. The empirical results do not support the existence of "Ricardian Equivalence" hypothesis. The effects of budgetary deficit and government spending have a significant influence on private consumption and private investment.