Cross-Sectional and Time Series Data as the Basis for Panel Modelling: The Case of Kidnappings in México From 2010 to 2019

Juan Bacilio Guerrero Escamilla,Arquímedes Avilés Vargas

doi:10.5539/ijsp.v10n2p68

Abstract

This paper presents the elements entailing the building of a panel data model on the basis of both cross-sectional and time series dimensions, as well as the assumptions implemented for the model application; this, with the objective of focusing on the main elements of the panel data modelling, its way of building, the estimation of parameters and their ratification. On the basis of the methodology of operations research, a practical application exercise is made to estimate the number of kidnapping cases in Mexico based on several economic indicators, finding that from the two types of panel data analyzed in this research, the best adjustment is obtained through the random-effects model, and the most meaningful variables are the Gross domestic product growth and the informal employment rate from the period 2010 to 2019 in each of the states. Thus, it is illustrated that panel data modelling present a better adjustment of data than any other type of models such as linear regression and time series analysis.

Highlights

In the current days, social, economic, financial and biological phenomena, among others, have largely showed complex behaviors mainly due to the structure that data present, which tent to be either cross-sectional data and time-series data, that is, according to Lavado (2012): Cross-sectional data; where i stands for a specific moment in time (1) Time-series data; where t stands for is a specific moment in time (2)Chart 1
This paper presents the elements entailing the building of a panel data model on the basis of both cross-sectional and time series dimensions, as well as the assumptions implemented for the model application; this, with the objective of focusing on the main elements of the panel data modelling, its way of building, the estimation of parameters and their ratification
On the basis of the methodology of operations research, a practical application exercise is made to estimate the number of kidnapping cases in Mexico based on several economic indicators, finding that from the two types of panel data analyzed in this research, the best adjustment is obtained through the random-effects model, and the most meaningful variables are the Gross domestic product growth and the informal employment rate from the period 2010 to 2019 in each of the states

Summary

Introduction

Social, economic, financial and biological phenomena, among others, have largely showed complex behaviors mainly due to the structure that data present, which tent to be either cross-sectional data (evaluation of the phenomenon in a certain period of time) and time-series data (evaluation of the phenomenon through time), that is, according to Lavado (2012): Cross-sectional data; where i stands for a specific moment in time (1) Time-series data; where t stands for is a specific moment in time (2)Chart 1. ; where i stands for a specific moment in time (1) Time-series data. In the view if these phenomena, the main goal of this paper is to present the elements enclosing panel data models, its way of building, the estimation of its parameters and its ratification. To fulfill this goal, it is presented a practical application to estimate the number kidnapping cases in Mexico from 2010 to 2019 taking a frame of reference different economic indicators such as Gross Domestic Product (GDP), economic growth, unemployment rate and employment informality rate in each of the Mexican states

Objectives

Methods

Findings

Conclusion