Author:
(1) Laurence Francis Lacey, Lacey Solutions Ltd, Skerries, County Dublin, Ireland. Editor's Note: This is Part 3 of 7 of a study on how changes in the money supply, economic growth, and savings levels affect inflation. Read the rest below. Table of Links Abstract and 1 Introduction


Methods


2.1 Statistical Methodology
2.2 US Time Series Data
2.3 Hyperinflation Model


Results


3.1 Characterisation of Price Inflation
3.2 Characterisation of Hyperinflation in the Weimar Republic (1922 to 1923)
4. Discussion
5. Conclusion, Supplementary materials, Acknowledgements, and References

Appendix 2.2 US Time Series Data The sources of the annual US time series data, over the period 2001 to 2019, are: CPI [8], BMS [9] real GDP [10]. The annual savings data are those estimated for American households. These savings data were reported for the years 2001, 2004, 2007, 2010, 2013, 2016, 2019 only [11]. Where required, the annual savings estimates for the years not available were imputed using the regression fit obtained using the available years of data. The time series data, over the time period 2001 to 2019, with 2001 as the reference year (time = 0), were expressed relative to the value given in year 2001. The rate-constant (λ) estimates for each time series, together with the 95% confidence intervals, and coefficients of determination (R2 ) of the regression fits were obtained from a linear regression (intercept = 0) of the natural log-transformed time series data versus time, with time = 0 for the reference year 2001, using Python (version 3.9.2) statsmodels package [12]. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Author: (1) Laurence Francis Lacey, Lacey Solutions Ltd, Skerries, County Dublin, Ireland. Author: Author: (1) Laurence Francis Lacey, Lacey Solutions Ltd, Skerries, County Dublin, Ireland. Editor's Note: This is Part 3 of 7 of a study on how changes in the money supply, economic growth, and savings levels affect inflation. Read the rest below. Editor's Note: This is Part 3 of 7 of a study on how changes in the money supply, economic growth, and savings levels affect inflation. Read the rest below. Editor's Note: This is Part 3 of 7 of a study on how changes in the money supply, economic growth, and savings levels affect inflation. Read the rest below. Editor's Note: This is Part 3 of 7 of a study on how changes in the money supply, economic growth, and savings levels affect inflation. Read the rest below. Table of Links Abstract and 1 Introduction Methods 2.1 Statistical Methodology 2.2 US Time Series Data 2.3 Hyperinflation Model Results 3.1 Characterisation of Price Inflation 3.2 Characterisation of Hyperinflation in the Weimar Republic (1922 to 1923) 4. Discussion 5. Conclusion, Supplementary materials, Acknowledgements, and References

Appendix Abstract and 1 Introduction Abstract and 1 Introduction Methods Methods Methods 2.1 Statistical Methodology 2.1 Statistical Methodology 2.2 US Time Series Data 2.2 US Time Series Data 2.3 Hyperinflation Model 2.3 Hyperinflation Model Results Results Results 3.1 Characterisation of Price Inflation 3.1 Characterisation of Price Inflation 3.2 Characterisation of Hyperinflation in the Weimar Republic (1922 to 1923) 3.2 Characterisation of Hyperinflation in the Weimar Republic (1922 to 1923) 4. Discussion 4. Discussion 5. Conclusion, Supplementary materials, Acknowledgements, and References Appendix 5. Conclusion, Supplementary materials, Acknowledgements, and References Appendix Appendix 2.2 US Time Series Data The sources of the annual US time series data, over the period 2001 to 2019, are: CPI [8], BMS [9] real GDP [10]. The annual savings data are those estimated for American households. These savings data were reported for the years 2001, 2004, 2007, 2010, 2013, 2016, 2019 only [11]. Where required, the annual savings estimates for the years not available were imputed using the regression fit obtained using the available years of data. The time series data, over the time period 2001 to 2019, with 2001 as the reference year (time = 0), were expressed relative to the value given in year 2001. The rate-constant (λ) estimates for each time series, together with the 95% confidence intervals, and coefficients of determination (R2 ) of the regression fits were obtained from a linear regression (intercept = 0) of the natural log-transformed time series data versus time, with time = 0 for the reference year 2001, using Python (version 3.9.2) statsmodels package [12]. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. available on arxiv

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