Fall24 Final Diffusion Confusion Econ-6635-07
Diffusion Index and graph in via a ggplot
Three US economic variables were used to produce the Diffusion Index for the US economy: the unemployment rate (UNRATE), the average hourly earnings of all employees (CES0500000003), and • CPIAUCSL, or the Consumer Price Index for All Urban Consumers
To enable straightforward interpretation, the data is then condensed for the years 2010–2024 at monthly intervals.
To gauge the changes over time, the initial differences were computed for every series. After that, the variations were transformed into up (1), down (-1), or no change (0). This conversion aids in trend analysis and Diffusion Index computation.
The following formula was used to determine the Diffusion Index: Diffusion Index=(Positives/Total−Negatives/Total) In order to improve interpretability, we scaled the time series of monthly Diffusion Index values from -1 to 1 to -100 to 100.
The graph displays multiple economic performance cycles.
For example, there are times when the index stays positive for a long time, which denotes steady economic expansion.
Long stretches below zero, on the other hand, indicate protracted economic downturns.
Significant changes in policy or the economy may be reflected in large index spikes or dips.
2010–2012: The years after the 2008 financial crisis, probably indicating a recovery.
2013–2015: Stable economic expansion periods.
2020: A notable decline that may have been brought on by the COVID-19 pandemic.
2021–2023: Post-pandemic economic rebound.
Diffusion Index and graph in via a ggplot with a built in smoother or you create the smoother.
The Smoother represents the time frame.
2010–2012: During this time, values are rising and remaining positive, indicating a recovery phase from the 2008 financial crisis.
2013–2015: Stable economic conditions are shown by relative stability, with less variation around the zero line.
2020: A steep drop that most likely reflects the COVID-19 pandemic’s economic effects.
2021–2023: A period of recovery during which the index resumes its upward trajectory.
Here, the Smoother Line averages out short-term volatility to help visualize the underlying trend.
Additionally, the confidence interval is indicated by the gray shaded area, which displays the range that the actual trend most likely lies within. Greater confidence in the trend estimate is indicated by a narrower band.
Chicago Fed National Activity Index: Diffusion Index (CFNAIDIFF)
After that, I added a smoother to the data for the Chicago Fed National Activity Index: Diffusion Index over the same time frame (2010–2024) as my US Economic Diffusion Index. It is simple to comprehend by saying that the index generally goes lower between 2010 and 2015, indicating times of slower economic activity. The indicator exhibits greater volatility between 2015 and 2020, but it also shows a minor upward tendency, suggesting a moderate improvement in the economy. As a result of the COVID-19 pandemic, there is a noticeable peak around 2020 and a sharp fall after that year.
- calculating the correlation coefficient
## unemp avghr cpi cfnaid
## unemp 1.0000000 -0.59759267 -0.62428677 0.19477706
## avghr -0.5975927 1.00000000 0.98786874 -0.04384157
## cpi -0.6242868 0.98786874 1.00000000 -0.07225501
## cfnaid 0.1947771 -0.04384157 -0.07225501 1.00000000In general, there is little correlation between my economic indicators and the Chicago Fed National Activity Index: Diffusion Index. This suggests that although there are a few small correlations, other factors probably have a greater impact on the Chicago Fed National Activity Index: Diffusion Index.
In particular:
There is a small but marginally positive link between the unemployment rate and CFNAIDIFF (0.19477706).
The CPI (-0.07225501) and average hourly wages (-0.04384157) have very little effect on CFNAIDIFF.
- a ggplot of the two series side by side.
This graph compares two important economic indicators: the Chicago Fed National Activity Diffusion Index (CFNAIDIFF) from 2010 to 2024 and the US Economy Diffusion Index, which I developed. Both indices track shifts in the state of the economy over time.
The blue line represents the US Economy Diffusion Index, which fluctuates sharply and frequently. This suggests a very unstable economic climate where things might shift drastically very quickly. A more steady pattern of economic activity is suggested by the CFNAIDIFF, which is represented by the orange line and has smoother and fewer dramatic variations.
The US Economy Diffusion Index shows more noticeable ups and downs between 2010 and 2015, indicating faster economic shifts. The CFNAIDIFF, meantime, continues its more steady, slow trend. The COVID-19 pandemic’s economic effects are probably reflected in the CFNAIDIFF’s notable peak and subsequent drop around 2020. Significant activity is also shown in the blue line throughout this time, suggesting notable changes in the economy. Both indicators’ graphs exhibit ups and downs following the 2020 era, although the US Economic Diffusion primarily remains in the positive region with just one dip to a negative region. In contrast to my US Economic Diffusion Index, which has some positive development towards the end, the Chicago Fed National Activity Diffusion Index (CFNAIDIFF) frequently slips to the negative region and displays a deteriorating tendency.
Conclusion:
All things considered, this comparison shows how the two indices differ in terms of volatility and stability. The Chicago Fed National Activity Diffusion Index (CFNAIDIFF) offers a more stable view of economic patterns, whereas the US Economy Diffusion Index indicates more abrupt and significant changes in the economy. We can better grasp the subtleties of how many economic indicators represent the general state and activity of the economy with the aid of this research.