The paper emplyos the Pooled Mean Group (PMG) estimation to investigate empirically the relationship among carbon dioxide (CO2) emissions and potential determinants for a panel of 18 MENA countries during the period 1980-2018. The good properties in terms of consistency and efficiency of the coefficient estimates make the PMG approach very useful for examining the determinants of pollution emissions in the framework of dynamic heterogeneous panel data models over both the long- and short-run. Unlike the extant literature on MENA economies, many determinants are included in the analysis to avoid the bias problem of omitted variables. Three energy sources and two classes of sub-panels according to regional proximities and oil wealth are considered in order to provide a sensitivity check on the findings and to make the analysis more homogeneous. The results reveal long-run relationships between pollution emissions and the selected variables. All determinants are found to be statistically significant for all panels and energy sources over the short-run. However, some variables are not significant determinants over the long-run. The Environment Kuznet's Curve (EKC) hypothesis is supported only for the panel of non-oil countries, which has meaningful implications and reveals the importance of splitting the global panel in order to appropriately examine the EKC hypothesis and conduct policy debates according to the findings of each panel. The obtained results provide important policy implications.
We consider LU and QR matrix decompositions using exact computations. We show that fraction-free Gauß-Bareiss reduction leads to triangular matrices having a non-trivial number of common row factors. We identify two types of common factors: systematic and statistical. Systematic factors depend on the reduction process, independent of the data, while statistical factors depend on the specific data. We relate the existence of row factors in the LU decomposition to factors appearing in the Smith-Jacobson normal form of the matrix. For statistical factors, we identify some of the mechanisms that create them and give estimates of the frequency of their occurrence. Similar observations apply to the common factors in a fraction-free QR decomposition. Our conclusions are tested experimentally.Mathematics Subject Classification (2010). 2010 MSC: 00-01, 99-00. apparent that the columns and rows of the L and U matrices frequently contain common factors, which otherwise pass unnoticed. We consider here how these factors arise, and what consequences there are for the computations.Our starting point is a fraction-free form for LU decomposition [10]: given a matrix A over D,where L and U are lower and upper triangular matrices, respectively, D is a diagonal matrix, and the entries of L, D, and U are from D. The permutation matrices P r and P c ensure that the decomposition is always a full-rank decomposition, even if A is rectangular or rank deficient; see section 2. The decomposition is computed by a variant of Bareiss's algorithm [1]. In section 6, the LD −1 U decomposition also is the basis of a fraction-free QR decomposition.The key feature of Bareiss's algorithm is that it creates factors which are common to every element in a row, but which can then be removed by exact divisions. We refer to such factors, which appear predictably owing to the decomposition algorithm, as "systematic factors". There are, however, other common factors which occur with computable probability, but which depend upon the particular data present in the input matrix. We call such factors "statistical factors". In this paper we discuss the origins of both kinds of common factors and show that we can predict a nontrivial proportion of them from simple considerations.Once the existence of common factors is recognized, it is natural to consider what consequences, if any, there are for the computation, or application, of the factorizations. Some consequences we shall consider include a lack of uniqueness in the definition of the LU factorization, and whether the common factors add significantly to the sizes of the elements in the constituent factors. This in turn leads to questions regarding the benefits of removing common factors, and what computational cost is associated with such benefits.A synopsis of the paper is as follows. After recalling Bareiss's algorithm, the LD −1 U decomposition, and the algorithm from Jeffrey [10] in section 2, we establish, in section 3, a relation between the systematic common row factors of U and the entries in the Smi...
This paper empirically investigates the interdependence between GCC stock market and oil price by considering structural breaks in conditional volatility. The univariate and multivariate GARCH models are extended by including structural breaks which are determined endogenously by using ICSS algorithm proposed by Inclan and Tiao. Empirical results indicate that the inclusion of structural breaks in the model substantially reduces the volatility persistence and the estimated half-life of shocks. Hence, the conditional volatility of oil price and stock market are more affected by their own shocks and volatility when structural breaks are neglected. Likewise, our results are conclusive on conditional dependency between GCC stock market and oil price revealing that the volatility shifts reduce the shocks and volatility spillover effects. For the portfolio management, the empirical results show evidence of sensitivity of the optimal weight and hedge ratios to structural breaks in conditional volatility.
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