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One of the major problems of the empirical economists while building an economic model is the selection of variables which should be included in the true regression model. Conventional econometrics use several model selection criteria to determine the variables. Recent years' developments in Machine Learning (ML) approaches introduced an alternative way to select variables. In this paper, I have an application of ML to select variables to include for a nonlinear relationship between inflation and economic growth. Among ML methodologies, Random Forest (RF; Breiman, 2001) approach is one of the most powerful to capture nonlinear relationships. Therefore, I applied RF and found that both high and low inflation can be the cause of low economic growth which is a major contribution of the paper to economic literature. This observation produces clear suggestions for central bank inflation targeting policies. Moreover, in the paper, as an outcome of RF there are other variables effecting economic growth with an order of importance.

We present four algorithms to perform template matching of an N×N image with an M×M template. The first algorithm is sequential, and the others are based on a SIMD mesh connected computer with N×N processors. In the first three algorithms, both the image and the template are represented by quadtrees, whereas in the last one, the template is represented by a quadtree, and the image is represented by a matrix. The time complexities of the four algorithms are respectively upper-bounded by α1N2M2, α2N+β2M2, α3N+β3M2, and β4M2, where α1, α2, β2, α3, β3, and β4 are constants.

We present two parallel algorithms to perforni template matching of an N x N image with an M x M template on an SIMD mesh connected computer with N xN processors. In the first algorithm, both the image and the template are represented by quadtrees, whereas in the second, the template is represented by a quadtree, and the image is represented by a matrix. The time complexities of the two algorithms are respectively upperbounded by alN+PlM2, and p z M 2 , where a l , P I , and p2 are constants.

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