In this article, we forecast crude oil and natural gas spot prices at a daily frequency based on two classification techniques: artificial neural networks (ANN) and support vector machines (SVM). As a benchmark, we utilize an autoregressive integrated moving average (ARIMA) specification. We evaluate out-of-sample forecast based on encompassing tests and mean-squared prediction error (MSPE).
Using plant-level data on Chilean manufacturing firms for the 1980-2001 period, we estimate and characterize disaggregate total factor productivity. We use these estimates to study the microeconomic sources of aggregate efficiency, a fundamental part of aggregate growth. By decomposing productivity dynamics into production reallocation and within plant efficiency changes, we find that reallocation accounted for