Un estudio socioeducativo sobre la evaluación del crecimiento económico en países seleccionados

2021 ◽  
Vol 11 (4) ◽  
pp. 229-249
Author(s):  
Mehmet Çanakcı

El sector agrícola, que tiene una amplia gama de efectos económicos y sociales, tiene una importancia estratégica debido a la renta nacional, el empleo, los gastos de consumo, el suministro de materias primas a otros sectores y su participación en las exportaciones. En este contexto, este documento revisa el debate sobre el papel de la agricultura en la promoción del crecimiento económico en una selección de países agrícolas seleccionados. Este estudio analiza los vínculos entre las variables agrícolas y el crecimiento del producto interno bruto con la ayuda de la prueba de cointegración, el enfoque Autoregressive Distributed Lag Limit Test (ARDL) para el período relevante, Augmented Dicky Fuller (ADF) y PP test (PMG). Los resultados muestran que el aumento sostenible de la renta agrícola cambia positivamente la renta nacional y es la fuerza impulsora del crecimiento; Se encontraron pruebas contundentes que muestran que el aumento de la mano de obra agrícola disminuye el ingreso nacional a largo plazo. Esta fuerte evidencia apoya la hipótesis de crecimiento agrícola que muestra que; En los últimos años, las discusiones sobre la relación entre el concepto de crecimiento sostenible y la política agrícola se han vuelto más prominentes. Por ello, se recomienda que cada vez más países contribuyan a resolver los problemas del crecimiento económico siguiendo y aplicando métodos de productividad agrícola.

2020 ◽  
Vol 30 (08) ◽  
pp. 2050039 ◽  
Author(s):  
Foued Saâdaoui ◽  
Othman Ben Messaoud

Forecasting has always been the cornerstone of machine learning and statistics. Despite the great evolution of the time series theory, forecasters are still in the hunt for better models to make more accurate decisions. The huge advances in neural networks over the last years has led to the emergence of a new generation of effective models replacing classic econometric models. It is in this direction that we propose, in this paper, a new multiscaled Feedforward Neural Network (FNN), with the aim of forecasting multivariate time series. This new model, called Empirical Mode Decomposition (EMD)-based Neural ARDL, is inspired from the well-known Autoregressive Distributed Lag (ARDL) model being our proposal founded upon the concepts of nonlinearity, EMD-multiresolution and neural networks. These features give the model the ability to effectively capture many nonlinear patterns like the ones often present in econophysical time series, such as nonlinear trends, seasonal effects, long-range dependency, etc. The proposed algorithm can be summarized into the following four basic tasks: (i) EMD breaking-down multivariate time series into different resolution levels, (ii) feeding EMD components from the same levels into a number of feedforward neural ARDL models, (iii) from one level to the next, extrapolating the component corresponding to the response variable (scalar output) a number of steps ahead, and finally, (iv) recombining level-by-level forecasts into a single output. An optimal learning scheme is rigorously designed for efficiently training the new proposed architecture. The approach is finally tested and compared to a number of powerful benchmark models, where experiments are conducted on real-world data.


2020 ◽  
pp. 097674791989890
Author(s):  
Sudeshna Ghosh

The study explores the relationship between consumer confidence, household private consumer expenditure and other related macroeconomic financial variables for Brazil, a major, upper middle, income, Latin American country. It is widely discussed in the literature that the consumer confidence is an initial guide to the future behaviour of the economy based on the consumption path. Thus, a rise in the confidence of the consumer would lead to rising household consumption behaviour, which would percolate to accelerate economic growth. The study uses the nonlinear autoregressive distributed lag model (NARDL) to measure the effects of changes in consumer sentiment on private consumer spending, taking into consideration the significance of other financial variables, namely the rate of interest, stock market index, the exchange rate, inflation and unemployment trends. The study employs monthly data from the 4th month of 1995 to the 10th month of 2018. The bounds test of the NARDL suggests the presence of a cointegrating relationship among the variables. The model estimation affirms the presence of asymmetries in the behaviour of the major explanatory variables. In the short run, there are both positive and negative asymmetric impacts of consumer confidence index (CCI) on consumer expenditure, while the rate of interest has only negative asymmetries. In the long run, unemployment changes, stock market fluctuations, interest rate variation and alterations in the CCI shape the behaviour of consumer spending at the household level in Brazil. So, the consumers are able to perceive the signalling of the future behaviour of the market and contribute through consumption spending. JEL: C22; D12; E21; O54


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