This paper aims to analyze the metrics the United Nations has set and called the Sustainable Development Goals (SDGs) and their association with the gross domestic product (GDP) in emerging economies. SDGs have been identified to measure healthy development, whereas GDP has historically been used to measure economic health and has been prioritized above many other indicators. This research deploys the feasible generalized least squares (FGLS) and the seemingly unrelated regressions (SUR) on panel data consisting of the five BRIC countries spanning 2000 through 2017 to estimate a regression model that shows the association of SDGs with GDP. The paper concludes that targeting GDP may not lead to achieving overall SDGs.

We seek to determine whether a United States President’s job approval rating is influenced by the Misery Index. This hypothesis is examined in two ways. First, we employ a nonlinear model that includes several macroeconomic variables: the current account deficit, exchange rate, unemployment, inflation, and mortgage rates. Second, we employ probit and logit regression models to calculate the probabilities of U.S. Presidents’ approval ratings to the Misery Index. The results suggest that Layton’s model does not perform well when adopted for the United States. Conversely, the probit and logit regression analysis suggests that the Misery Index significantly impacts the probability of the approval of U.S. Presidents’ performances.

Stricken by the events of Sept. 11, 2001, and by increases in fuel prices, a key cost which has spiked upward as the price of oil has risen to more than $130 per barrel, the airlines find themselves, once again, in fragile financial shape. The purpose of this article is to measure the financial condition of the industry, given this situation, using a popular financial stress model, the Altman Model.

This study examines the reaction of four major equity markets of the world to the US equity market fear index, i.e., the Chicago Board of Trade Volatility Index (VIX). The VIX is designed to perform as a leading indicator of the volatility in equity markets. Our paper examines the daily data for the period of 2013 through 2018. We find that during this period there were three significant breaks in the data. Impulse responses from the structural vector autoregressive model estimation show that, in the first and second subperiods that cover from 6/2013 through 5/2016, equity market volatility in the US, UK, France, and Germany responded to structural shocks to the VIX. Nonlinear Granger causality tests confirm these findings. However, in the post Brexit-vote era, equity indices neither react to VIX structural shocks nor are caused by these shocks.