In Part 1, we saw that increasing the observation window changes the results for the occurrence of tails following heads. That raises the question: How does the micro (small observation window) relate to the macro (large observation window)? More specifically, what is the relationship between results from a small observation window (three flips) and those from a large observation window (100 flips)?
Many gambling activities involve betting on events for which the outcomes obey rigid, specified odds. When there is no mechanical bias, roulette wheels have fixed odds, including some that are binary (50:50) such as red vs. black. Betting on the flip of a coin is likewise a binary 50:50 proposition: heads or tails. Why is it then that there is a propensity for some gamblers to place wagers in a pattern conflicting with the known 50:50 odds? For example, after a string of blacks on a roulette wheel, why do some gamblers keep increasing the amounts bet on red with each succeeding black?
Note: There are a number of errors in the analysis. A new article will be posted with the correct analysis. This post will remain. The reason is that this series of blog articles constitutes my “research notebook.” As anyone trained in science or engineering will tell you, a research notebook is a chronological record of work. When errors are made, they are noted but not crossed out or removed. Removing any part invalidates the research record. So Part 13 of this series will be a replacement for Part 12.
In previous posts in this series, we have looked at what can be learned by looking at the time series data for U.S. federal government spending and consumer inflation (CPI). We have failed to find systemic consistency in the correlation between these two data sets. In the most recent effort,1 we isolated those data points belonging to periods of significant inflation and significant disinflation/deflation. Even that partitioning of the data did not yield the systemic patterns sought. This brings us to see what can be learned by studying each period of significant inflation changes individually.
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Note: This article was originally posted on May 7, 2023. The Analysis and Conclusion sections were completed on May 8. The final additions to the Appendix were made on May 9.
Previously,1 encouraging results were obtained when correlation analysis was restricted to periods of significant inflation or disinflation. In this post, we examine what can be learned about the correlation between deficit spending and inflation when timeline adjustment of the data is implemented.
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Previous work has shown that the correlation between US federal government deficit spending and CPI inflation fluctuates widely and wildly over time. Results vary depending on data sample selection. That leads to the present decision to look specifically at each instance of inflation, disinflation, and deflation.
Last week we thought we had wrapped up, for now, our work on timeline shift effects on the correlation between US federal government deficit spending and consumer inflation. However, more ideas have occurred, and this week we will look at further details regarding which comes first, inflation or increased government spending.
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This week we return to the question of whether the inflation-spending correlation is greater when measured before, after, or concurrently with US federal deficit spending. The data for the 108-year period from 1914 to 2022 is used.
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This week we examine take a deep dive into what can be learned from examining the unsmoothed data for correlation of deficit spending and CPI inflation. As before, we are looking at the years 2014-2022.
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Note: This was posted at 4:24 pm on March 26 with some incomplete sections. Updating in was completed at 2:52 am March 28.
In previous parts of this discussion, we have made some qualitative observations about relationships involving the correlations between U.S. federal government spending and inflation in the U.S. economy. For the first time in this series, we will move into the arena of quantitative measurements of government spending and inflation correlations.
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A valuable function not provided by the Microsoft spreadsheet program Excel is calculating the area under a curve. This basic function is needed for many quantitative analysis problems when the data does not fit a defined algebraic function.
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