scholarly journals Time-Varying Effects of Oil Supply Shocks on the US Economy

2013 ◽  
Vol 5 (4) ◽  
pp. 1-28 ◽  
Author(s):  
Christiane Baumeister ◽  
Gert Peersman

Using time-varying BVARs, we find a substantial decline in the short-run price elasticity of oil demand since the mid-1980s. This finding helps explain why an oil production shortfall of the same magnitude is associated with a stronger response of oil prices and more severe macroeconomic consequences over time, while a similar oil price increase is associated with smaller output effects. Oil supply shocks also account for a smaller fraction of real oil price variability in more recent periods, in contrast to oil demand shocks. The overall effects of oil supply disruptions on the US economy have, however, been modest. (JEL E31, E32, Q41, Q43)

2020 ◽  
Vol 16 (2) ◽  
pp. 22
Author(s):  
Nicholas Bitar

Will the US sustain its economy after the tariff war with China, or will the economy regress? This paper offers a conceptual framework, based on the tenets of New-Keynesian theory, to answer this question. I anticipate that the tariff will have a positive effect on the GDP of the US economy in the short run while prices will rise. When adding the most recent reforms of interest cut by the Fed to 1.75% in September (2019) the model concludes a better outcome. Followed by an expansionary monetary policy by reducing the interest rate, the aftermath of the tariff war on China seems to have a positive impact on the US income and productivity. Obviously, some critics to the Trump Administration indeed shed light on the curtailed global and US social welfare that is caused by the inflationary effect of the tariff war, in addition to the deteriorating conditions for some trading sectors in the US which would certainly lead to unemployment. But the benefits to the US economy that are translated by the New-Keynesian theoretical framework show a positive impact on US production, employment, and GDP.


2019 ◽  
Vol 5 (1) ◽  
pp. 1-9
Author(s):  
Francisco J Quevedo ◽  
Andrea Katherine Quevedo-Prince

The Non-Profit Sector contributes almost $1 trillion to the US economy, representing 5.4% of GDP, and generating over 12 million jobs in 2017. Researchers suggest that a better understanding of the factors that affect fundraising would be of great interest to policymakers and fundraisers. However, the workings of the sector are subject of much debate. Some relate its size to the Theory of Government Failure, while others propose that government funding does have a positive effect on revenues. Some have suggested they swing with Gross Domestic Product (GDP), but others contradict this view and contend that macroeconomic variables do not affect short-run dynamics. Some research found that non-profit revenues react more to economic upswings than downturns, but nationwide organizations relate the ups-and-downs to certain events, as they influence public awareness. Predictive modeling overall has focused on big-donor analytics, aimed at identifying potential sponsors. Our research set out instead to define a working model for the US Non-Profit Sector. After an exhausting search, we located complete time series for an emblematic segment, the environmental cause, Factor Analysis allowed us to pinpoint the independent variables. We found that Non-Profit Revenues (NPR) depend largely on Public Awareness, as measured by TV coverage, and on Disposable Personal Income (DPI), specifically: NPR = -4401.542 + 528.327(DPI) +23.121(TVCoverage) + Ɛ We replicated prior research, which sought out relationships between macro-economic variables and NPR. That study had discarded the correlation between GDP and NPR as obvious, but did not explore DPI as the determining factor, and stuck to single variable searches, finding a correlation between the Standard & Poors index and lagged NPR figures, with a correlation coefficient of 0.636. Our model’s Pearson's R came up to 0.935, with perfect significance levels. Confirmatory Factor Analysis reaffirmed the fit of our equation, with an R² of 0.87.


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