Search

Potential GDP measures the sustained growth that an economy can achieve in the long term through the efficient use of production factors. In particular, if we analyze the potential GDP of the countries of the Pacific Alliance bloc (Chile, Colombia, Mexico and Peru), these have been moderating in recent years, associated with structural factors such as the stagnation in gains in competitiveness and productivity , and cyclical factors that are influenced to a certain extent by economic imbalances.Added to this, the expansion of COVID-19 worldwide would further exacerbate the deterioration of the potential GDP growth of the countries of the Pacific Alliance in 2020. In this sense, the objective of this research is to estimate the potential GDP through the production function methodology, which will allow us to analyze the dynamics of potential growth through its components i) capital stock, ii) labor force, and iii ) total factor productivity.

This research document aims to build a leading indicator of private investment through the use of artificial neural network methodology. The leading indicator consists of the forecast of the growth rate of private investment for the next three quarters based on leading indicators, which are determined through the dynamic correlations of the leading variables with private investment. It is important to mention that the contribution of this research work is to provide a forecasting methodology, since they could be relevant to give signals about the future performance of private investment and allow economic policy makers to make relevant decisions.

Export diversification provides economies with various benefits such as promoting and stabilizing economic growth by reducing the volatility of export earnings and increasing productivity through positive intra- and intersectoral externalities. In this sense, it is relevant to be able to identify what enables the diversification of exports; however, given a disperse theoretical framework, this identification becomes an arduous task. Using the Bayesian Moving Average methodology, this document tries to discover which idiosyncratic factors of each country are relevant to explain greater export diversification in the long term. The results suggest that six elements are the most important for predicting export diversification. These elements are: i) income from natural resources, ii) population, iii) gross capital formation (capital stock), iv) secondary education, v) credit to the private sector, and vi) human capital. These results are in line with the results obtained in the literature.