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Mihaela Simionescu

Abstract

The objective of this research is to create a profile of the Romanian eco-consumer with university education. The profile is not limited to the information regarding environmental and economic benefits of recycling, but focuses on ecological behaviour. A detailed statistical analysis was made based on a large representative sample of respondents with secondary and university education. Indeed, the tendency of practical ecobehaviour becomes more pronounced for the people with university education. For people that are more than 30 years old the chance of being aware of the significance of the recycling symbols on the packages decreases, the lowest chance being given to people aged more than 50. The respondents that are interested in environment protection buy products with ecological symbols. However, those people who already know the meaning of these symbols do not buy this type of products for ecological reasons, even if they are interested in the environment protection. This research also offers an extensive description of its results, being an opportunity for the respondents to know more about the meaning of the recycling symbols. The results of this research also provide information being a guideline for consumers. This study achieves two main goals: the ecological component (the eco-consumers were identified and ordinary consumers were attracted through the ecological behaviour) and the economic aspect (the resources allocation will be more efficient and the marketers will be able to address ecoconsumers who have specific characteristics).

Open access

Mihaela Simionescu

Abstract

Considering the potential factors that might generate economic growth, a target for any economy, this paper identified some determinants of economic growth in the countries from Central and Eastern Europe (CEE countries) that are member states of the European Union. The foreign direct investment was the most important determinant of economic growth in most of the countries (Bulgaria, Slovenia, Estonia, Hungary, Romania, Poland, Latvia, Lithuania) in the period 2003-2016, according to Bayesian bridge regressions. The indicators related to the level and the quality of labour resources proved to be insignificant in explaining the economic growth in these countries. Moreover, in Croatia, Estonia, Latvia, Lithuania, and Poland, the government expenditure on education had a negative effect on economic growth.

Open access

Mihaela Simionescu

Abstract

The recent enlargement of the EU (since 2004) and the United Kingdom's decision to leave the European Union have prompted a growing research interest in the political and academic environment because of the causes and consequences of migration between the CEE countries and those in the Western Europe. In this study, the effects of European economic integration on the number of EU-15 immigrants from the newly integrated EU countries were assessed by econometric techniques. According to panel data models, in the period 2000-2015, the number of migrants from the new member states of the EU has increased, in average, with more than 2200 people only due to their EU membership. This result reflects the positive impact of European economic integration on the number of emigrants from the CEE countries that chose the EU-15 states as destination countries. Moreover, according to some ridge Bayesian regressions, during the period 2004-2015, the EU-15 immigrants coming from the EU-13 states did not negatively affect the economic growth of the EU-15 countries.

Open access

Mihaela Simionescu

Abstract

This paper brings to light an economic problem that frequently appears in practice: For the same variable, more alternative forecasts are proposed, yet the decision-making process requires the use of a single prediction. Therefore, a forecast assessment is necessary to select the best prediction. The aim of this research is to propose some strategies for improving the unemployment rate forecast in Romania by conducting a comparative accuracy analysis of unemployment rate forecasts based on two quantitative methods: Kalman filter and vector-auto-regressive (VAR) models. The first method considers the evolution of unemployment components, while the VAR model takes into account the interdependencies between the unemployment rate and the inflation rate. According to the Granger causality test, the inflation rate in the first difference is a cause of the unemployment rate in the first difference, these data sets being stationary. For the unemployment rate forecasts for 2010-2012 in Romania, the VAR models (in all variants of VAR simulations) determined more accurate predictions than Kalman filter based on two state space models for all accuracy measures. According to mean absolute scaled error, the dynamic-stochastic simulations used in predicting unemployment based on the VAR model are the most accurate. Another strategy for improving the initial forecasts based on the Kalman filter used the adjusted unemployment data transformed by the application of the Hodrick-Prescott filter. However, the use of VAR models rather than different variants of the Kalman filter methods remains the best strategy in improving the quality of the unemployment rate forecast in Romania. The explanation of these results is related to the fact that the interaction of unemployment with inflation provides useful information for predictions of the evolution of unemployment related to its components (i.e., natural unemployment and cyclical component).

Open access

Mihaela Simionescu

Abstract

The evaluation and improvement of forecasts accuracy generate growth in the quality of decisional process. In Romania, the most accurate predictions for the unemployment rate on the forecasting horizon 2001-2012 were provided by the Institute for Economic Forecasting (IEF) that is followed by European Commission and National Commission for Prognosis (NCP). The result is based on U1, but if more indicators are taken into consideration at the same time using the multi-criteria ranking, the conclusion remains the same. A suitable strategy for improving the degree of accuracy for these forecasts is represented by the combined forecasts. The accuracy of NCP predictions can be improved on the horizon 2001-2012, if the initial values are smoothed using Holt-Winters technique and Hodrick-Prescott filter. The use of Monte Carlo method to simulate the forecasted unemployment rate proved to be the best way to improve the predictions accuracy. Starting from an AR(1) model for the interest variable, the uncertainty analysis was included, the simulations being made for the parameters. Actually, the means of the forecasts distributions for unemployment are considered as point predictions which outperform the expectations of the three institutions. The strategy based on Monte Carlo method is an original contribution of the author introduced in this article regarding the empirical strategies of getting better predictions.

Open access

Mihaela Bratu Simionescu

Abstract

The GDP forecasting presents a particularity resulted from the fact that this macroeconomic indicator can be analyzed in its quality of aggregate. Therefore, the GDP can be predicted directly using an econometric model with lagged variables represented by the aggregate component. On the other hand, the same GDP can be predicted by aggregating the forecasts of its components. The aim of this study is to find out which strategy generates the most accurate one-step-ahead prediction and if combined forecasts can be a solution of improving the forecasts accuracy. Starting from the GDP oneyear- ahead predictions made for 2009-2011 using the two strategies, measures of accuracy were calculated and the directly predicted GDP are better than those based on aggregating the components using constant and variable weights. Combined forecasts did not improve the accuracy of the predictions based on the mentioned strategies. This research is a good proof for putting the basis of considering the variables aggregation as an important source of uncertainty in forecasting.

Open access

Bratu Mihaela Simionescu

Abstract

Econometric modeling and exponential smoothing techniques are two quantitative forecasting methods with good results in practice, but the objective of the research was to find out which of the two techniques are better for short run predictions. Therefore, for inflation, unemployment and interest rate in the Czech Republic various accuracy indicators were calculated for the predictions based on these methods. Short run forecasts on a horizon of 3 months were made for December 2011-February 2012, the econometric models being updated. For the Czech Republic, the exponential smoothing techniques provided more accurate forecasts than the econometric models (VAR(2) models, ARMA procedure and models with lagged variables). One explication for the better performance of smoothing techniques would be that in the chosen countries the short run predictions were more influenced by the recent evolution of the indicators.