A Robust Bayesian Dynamic Linear Model for Latin-American Economic Time Series: "The Mexico and Puerto Rico Cases"

TL;DR

This paper demonstrates how Robust Bayesian Dynamic Models effectively handle outliers and structural breaks in Latin-American economic time series, providing insights into historic economic changes in Mexico and Puerto Rico.

Contribution

It introduces the application of RBDMs to Latin-American economic data, capturing structural breaks and outliers without requiring data transformation for stationarity.

Findings

RBDMs detect historic inflation changes in Mexico.

The models account for recession periods in Puerto Rico.

Structural breaks are effectively modeled in economic indicators.

Abstract

The traditional time series methodology requires at least a preliminary transformation of the data to get stationarity. On the other hand, Robust Bayesian Dynamic Models (RBDMs) do not assume a regular pattern or stability of the underlying system but can include points of statement breaks. In this paper we use RBDMs in order to account possible outliers and structural breaks in Latin-American economic time series. We work with important economic time series from Puerto Rico and Mexico. We show by using a random walk model how RBDMs can be applied for detecting historic changes in the economic inflation of Mexico. Also, we model the Consumer Price Index (CPI), the Economic Activity Index (EAI) and the total number of employments (TNE) economic time series in Puerto Rico using local linear trend and seasonal RBDMs with observational and states variances. The results illustrate how the model accounts the structural breaks for the historic recession periods in Puerto Rico.