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American Journal of  Applied 
Statistics and Economics (AJASE)

Modeling a Time Study of  the United States Consumer Price Index from 2014 to 2023
Paul O. Oyinloye1*

Volume 3 Issue 1, Year 2024
ISSN: 2992-927X (Online)

DOI: https://doi.org/10.54536/ajase.v3i1.3551
https://journals.e-palli.com/home/index.php/ajase

Article Information ABSTRACT

Received: August 28, 2024

Accepted: September 26, 2024

Published: September 30, 2024

This study presents a comprehensive analysis of  the U.S. Consumer Price Index (CPI) over 
a ten-year period 2014 to 2023. The CPI, a critical measure of  inflation, reflects the average 
change in prices paid by urban consumers for a market basket of  goods and services. This 
study will explore the intricate relationships between CPI and key economic variables, 
including unemployment levels, interest rates, and the sales index, using time series data 
and multiple regression analysis. The research identifies significant predictors of  CPI and 
evaluates the extent to which these factors influence price levels in the U.S. economy. The 
findings reveal a strong positive correlation between CPI and interest rates, indicating that 
as borrowing costs increase, the CPI tends to rise correspondingly. Conversely, a negative 
relationship is observed between CPI and unemployment levels, suggesting that higher 
unemployment rates are associated with a decrease in CPI, possibly due to reduced consumer 
spending. The study also examines the sales index, finding a weaker yet notable relationship 
with CPI, which underscores the complex dynamics of  consumer behavior and price levels. 
The analysis highlights the limitations of  CPI as a comprehensive measure of  the cost 
of  living, pointing out its lack of  significant correlation with real income and population 
growth. The study concludes that while CPI is an essential tool for measuring inflation 
and informing monetary policy, it may not fully capture the economic realities faced by the 
population. These insights suggest the need for policymakers to consider additional factors 
when using CPI to make informed decisions about economic policy and social welfare 
programs among the U.S. population. 

Keywords

Consumer Price Index, 
Economic Indicators, Inflation, 
Interest Rates, Sales Index, 
Unemployment

1 Babcock University, Nigeria
* Corresponding author’s e-mail: innocentoasev@yahoo.com

INTRODUCTION
The Consumer Price Index (CPI) is widely recognized 
as one of  the most important indicators of  economic 
health, particularly as it pertains to inflation. CPI reflects 
the average change over time in the prices paid by 
urban consumers for a basket of  goods and services. 
This index is crucial for understanding the purchasing 
power of  consumers, as well as the overall cost of  living. 
However, despite its importance, CPI is often criticized 
for its limitations in capturing the complete picture of  
economic well-being. This study aims to delve deeper into 
the factors that influence CPI, specifically focusing on 
unemployment levels, interest rates, and the sales index, 
during the period from January 2014 to December 2023.

Background of  the Study
The Consumer Price Index is a vital economic measure 
used by policymakers, economists, and businesses to gauge 
inflation and assess the economic environment. It tracks 
the prices of  a representative basket of  goods and services 
over time, offering a snapshot of  inflationary trends. This 
index is instrumental in adjusting wages, pensions, and 
tax brackets to maintain purchasing power and ensure 
economic stability. Despite its widespread use, CPI has 
faced criticism for not fully capturing the cost of  living, 
as it may not account for changes in consumer behavior, 
product quality, or new goods entering the market.
This study explores the relationship between CPI and 
three key economic variables: unemployment level, 

interest rates, and the sales index. Unemployment levels 
can significantly impact consumer spending patterns, 
as higher unemployment typically leads to reduced 
disposable income and lower demand for goods and 
services. Interest rates, set by the Federal Reserve, 
influence borrowing costs for consumers and businesses, 
thereby affecting spending and investment decisions. 
The sales index, representing the overall level of  retail 
sales, provides insight into consumer confidence and 
economic activity. By analyzing data from January 2014 to 
December 2023, this study seeks to uncover the extent to 
which these factors drive changes in CPI and contribute 
to a more nuanced understanding of  inflation dynamics.

Statement of  the Problem
Understanding the causes of  changes in commodity 
prices over time is a complex and multifaceted challenge. 
While CPI provides a broad measure of  inflation, it does 
not always correlate perfectly with the lived experiences 
of  consumers. Many individuals and households in the 
United States continue to face economic difficulties, even 
when CPI data suggests that inflation is under control. 
This discrepancy raises important questions about the 
adequacy of  CPI as a measure of  economic well-being 
and whether it accurately reflects the diverse and changing 
conditions within the U.S. economy.
One of  the critical issues is the potential for CPI to 
overlook significant economic factors that contribute 
to the overall cost of  living. For example, shifts in 



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unemployment can lead to reduced consumer spending, 
which may not be fully captured by CPI if  it primarily 
tracks prices rather than purchasing patterns. Similarly, 
changes in interest rates can affect consumer borrowing 
and spending, influencing inflationary pressures in ways 
that CPI might not immediately reflect. The sales index, 
as a measure of  retail activity, can also provide valuable 
insights into consumer behavior that are not directly 
observable through CPI alone.
Given these complexities, there is a pressing need for a 
more refined understanding of  the factors that influence 
CPI and how it interacts with other economic variables. 
This study seeks to address this gap by investigating 
the relationships between CPI, unemployment levels, 
interest rates, and the sales index. The goal is to develop 
a predictive model that can more accurately reflect the 
dynamics of  inflation and provide a better tool for 
economic analysis and policymaking.

Objectives of  the Study
The primary objective of  this study is to develop a 
robust predictive model that explains the influence of  
unemployment levels, interest rates, and the sales index 
on the Consumer Price Index. By achieving this goal, 
the study aims to enhance the understanding of  how 
these variables interact to drive inflation and to identify 
the most significant predictors of  changes in CPI. The 
specific objectives include:

Analyze Time Series Data
To produce time series data for the selected variables 
and compute descriptive statistics to understand their 
distribution and trends over the period from January 
2014 to December 2024.

Examine Relationships
To analyze the relationships between CPI and the 
explanatory variables—unemployment levels, interest 
rates, and the sales index—using multiple regression 
analysis and other statistical techniques.

Develop Predictive Model
To develop a predictive model that accurately reflects the 
influence of  these variables on CPI, allowing for more 
precise forecasting of  inflationary trends.

Evaluate Model Performance
To evaluate the performance of  the model using 
statistical tests for goodness-of-fit, multicollinearity, and 
heteroscedasticity, ensuring the robustness and reliability 
of  the findings.

Provide Policy Insights
To provide insights for policymakers on how changes 
in unemployment levels, interest rates, and retail activity 
might affect inflation and economic stability, potentially 
informing more effective economic policies.

Hypotheses
In pursuit of  the study’s objectives, the following null 
hypotheses are proposed for testing:

There is No Significant Relationship between CPI 
and Unemployment Level
This hypothesis suggests that changes in unemployment 
levels do not have a statistically significant effect on CPI. 
Testing this hypothesis will help determine whether 
fluctuations in employment rates are an important factor 
in predicting inflation.

There is No Significant Relationship between CPI 
and Sales Index
This hypothesis posits that the sales index, which measures 
retail sales activity, does not significantly influence CPI. 
Testing this relationship will provide insights into how 
consumer spending and retail activity impact inflationary 
pressures.

There is No Significant Relationship between CPI 
and Interest Rates
This hypothesis asserts that changes in interest rates do 
not have a significant effect on CPI. Given the role of  
interest rates in shaping borrowing costs and investment 
decisions, this relationship is critical to understanding the 
broader economic forces that drive inflation.
By testing these hypotheses, the study aims to uncover 
the underlying dynamics of  CPI and provide a more 
comprehensive understanding of  how various economic 
factors contribute to inflation. The results will not only 
offer insights into the specific period of  study but also 
provide a framework for analyzing inflation in future 
economic conditions.

LITERATURE REVIEW
The Consumer Price Index (CPI) remains a cornerstone 
in the measurement of  inflation, providing critical data 
for economic policy, business planning, and social welfare 
programs. However, its accuracy and relevance have 
been increasingly scrutinized considering the evolving 
economic landscape and the introduction of  new 
methodologies aimed at addressing inherent biases and 
limitations.

Historical Overview and Development of  CPI
Since its inception, the CPI has undergone several revisions 
to better reflect consumer behavior and economic 
conditions. The Bureau of  Labor Statistics (BLS) has 
continuously updated the CPI’s methodology, particularly 
in response to critiques like those from the Boskin Report 
of  1996, which highlighted the index’s upward bias due to 
substitution effects, quality changes, and the introduction 
of  new products. To address these issues, the BLS 
implemented the geometric means formula in 1999, which 
accounts for lower-level substitution—where consumers 
switch between similar products as prices change.



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In recent years, the BLS has also expanded the scope of  
data sources to improve the CPI’s accuracy. For instance, 
a 2021 report highlighted efforts to integrate big data and 
advanced statistical methods into the CPI calculation, 
aiming to capture more timely and relevant consumer 
price changes. Despite these improvements, some argue 
that the CPI still does not fully account for upper-
level substitution, where consumers substitute entirely 
different goods in response to price changes, potentially 
leading to residual biases in the index (BLS.gov, 2023).

CPI as a Measure of  Inflation and Cost of  Living
The CPI’s role as the primary measure of  inflation has 
made it indispensable for economic analysis. However, 
its ability to reflect the true cost of  living remains 
contentious. A significant critique is the CPI’s limited 
capacity to capture quality improvements in goods and 
services. For instance, a smartphone that has doubled in 
capabilities may cost more, but the CPI would typically 
register this as pure inflation, failing to account for the 
enhanced value the consumer receives.
Moreover, the CPI’s focus on urban consumers overlooks 
the price dynamics in rural and suburban areas, where 
cost structures can differ substantially. This urban-centric 
bias has led to calls for more geographically nuanced 
indices that better represent the diversity of  consumer 
experiences across the country. In addition, recent studies 
have pointed out the limitations of  the CPI in capturing 
“hidden inflation,” where the quality of  goods deteriorates, 
or product sizes shrink without a corresponding price 
change—a phenomenon not fully accounted for in the 
current CPI framework (Maverick, 2024).

Factors Influencing CPI
Unemployment, interest rates, and consumer spending 
remain pivotal in shaping the CPI. The relationship 
between unemployment and CPI is particularly complex. 
Traditionally explained by the Phillips Curve, this 
relationship has evolved, with recent data from 2023 
showing that low unemployment no longer consistently 
leads to higher inflation, suggesting that other factors, 
such as global supply chains and technology, are 
moderating price pressures.
Interest rates, as controlled by the Federal Reserve, directly 
impact borrowing costs and thus influence consumer 
spending and inflation. The November 2023 CPI report 
indicated a year-over-year increase of  3.1%, highlighting 
the persistent, though moderated, inflationary pressures 
despite aggressive rate hikes by the Federal Reserve aimed 
at curbing price growth. This interplay between interest 
rates and CPI underscores the importance of  monetary 
policy in managing inflation (Morgan, 2023). Consumer 
spending, reflected in the sales index, is another critical 
driver of  CPI. With the rise of  e-commerce and changing 
consumer preferences, the composition of  the market 
basket used to calculate CPI has had to adapt. However, 
this adaptation is not always timely, leading to potential lags 
in how accurately CPI reflects current spending patterns.

Methodological Approaches to Studying CPI
Recent advancements in econometric modeling and 
data analytics have improved the precision of  CPI 
measurements. The use of  time series analysis remains 
prevalent, but there is a growing emphasis on integrating 
big data and real-time analytics to better capture the 
nuances of  consumer price changes. These methods 
offer a more granular understanding of  inflation 
dynamics, though challenges in data quality and the risk 
of  overfitting models remain.
In response to criticisms of  bias, the BLS has also 
emphasized transparency in its methodology. The median 
standard error for 12-month changes in the all-items 
CPI remains low at 0.07%, indicating high confidence 
in the CPI’s reported figures. However, the standard 
error increases significantly when examining smaller 
geographic regions or specific item categories, suggesting 
that broader indices should be preferred for policy 
applications (BLS.gov, 2021).

Limitations and Criticisms of  CPI
Despite methodological improvements, the CPI continues 
to face significant criticisms. The issue of  substitution 
bias, both lower-level and upper-level, remains partially 
unresolved, potentially leading to either an overestimation 
or underestimation of  inflation. Additionally, the 
exclusion of  certain expenses, like investment costs and 
certain taxes, from the CPI basket has led to debates 
about its comprehensiveness as a measure of  the cost of  
living.
Furthermore, the CPI’s inability to fully capture the 
impact of  new products and quality changes remains 
a significant limitation. As consumer preferences shift 
towards more technologically advanced goods, the 
CPI’s lag in incorporating these changes can result in a 
misrepresentation of  actual inflation.

The Relevance of  CPI in Contemporary Economic 
Policy
Despite its flaws, the CPI remains crucial for economic 
policy. It is used to adjust Social Security benefits, tax 
brackets, and wages, making its accuracy vital for millions 
of  Americans. However, the ongoing debates about its 
biases and limitations underscore the need for continuous 
refinement. Future research may focus on developing 
alternative indices that better reflect the actual cost 
of  living, and the diverse economic realities faced by 
different population groups across the United States.

Summary of  Key Studies
Key studies continue to shape the understanding and 
improvement of  CPI. The Boskin Report remains a 
seminal work in this field, and more recent studies by the 
BLS and independent researchers have built on its findings 
to refine CPI calculations. The challenges of  accurately 
measuring inflation in a rapidly changing economy ensure 
that CPI will remain a critical, if  imperfect, tool for 
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MATERIALS AND METHODOS
Data Collection
The data for this study was sourced from www.
economagic.com, covering the period from January 
2014 to December 2023. The dataset includes monthly 
observations of  CPI, unemployment levels, interest rates, 
and the sales index. This period was chosen to capture a 
range of  economic conditions, including post-recession 
recovery and varying interest rate environments.

Modeling Approach 
Multiple regression analysis was employed to model 
the relationship between CPI and the selected 
explanatory variables. The study also conducted tests for 
multicollinearity, heteroscedasticity, and goodness-of-fit 
to ensure the robustness of  the model. These statistical 
tests are critical in validating the model’s accuracy and 
ensuring that the results are reliable for policy implications.

Statistical Charts and Results
The descriptive statistics table provides an overview of  the 
key characteristics of  the variables studied over the period 
from January 2014 to December 2023. The table includes 
summary statistics for the Consumer Price Index (CPI), 
Unemployment Level, Interest Rate, and Sales Index.

CPI (Consumer Price Index)
o The mean CPI value is approximately 246.11, with 

a standard deviation of  6.52, indicating some variability 
around the mean over the period.

o The minimum CPI recorded was 227.24, and the 
maximum was 261.30, showing that CPI fluctuated by 
about 34 points during the study period.

o The median (50th percentile) CPI value is 247.28, 
suggesting that half  of  the CPI values were below this 
point, with the other half  above.

Unemployment Level
o The average Unemployment Level was 4.36%, 

with a standard deviation of  0.68%, indicating that 
unemployment rates were relatively stable.

o Unemployment ranged from a minimum of  2.61% to 
a maximum of  5.95%, reflecting fluctuations in the labor 
market during the period.

o The median unemployment level is 4.34%, close to 
the mean, suggesting a roughly symmetrical distribution.

Interest Rate
o The mean Interest Rate was 2.30%, with a standard 

deviation of  0.28%.
o Interest rates varied between a minimum of  1.70% 

and a maximum of  2.83%.
o The median Interest Rate is 2.36%, indicating that 

most of  the interest rate observations are slightly above 
the mean.

Sales Index
o The mean Sales Index was 102.21, with a standard 

deviation of  10.74, indicating more variability in sales 
activity.

o The minimum value recorded was 77.60, and the 
maximum was 129.12, showing a wide range of  consumer 
sales activity.

o The median Sales Index value is 102.67, suggesting 
that sales figures were distributed evenly around the 
mean.
Overall, the descriptive statistics provide a snapshot of  
the data distribution and variability, helping to understand 
the typical values and the range of  fluctuations for each 
variable during the study period. This information is 
crucial for interpreting the relationships between these 
variables in the subsequent analysis.

Table 1: Summary Statistics of  the Economic Predictors
CPI Unemployment Level Interest Rate Sales Index

Count 120 120 120 120

Mean 246.108 4.363 2.301 102.206

Std 6.524 0.684 0.276 10.740

Min 227.235 2.614 1.702 77.603

25% 241.909 3.904 2.113 94.878

50% 247.279 4.339 2.360 102.668

75% 250.728 4.857 2.510 109.457

Max 261.297 5.948 2.832 129.125



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The plot shows the trend and fluctuations in CPI over 
the extended period, providing a visual understanding of  

how CPI has evolved over time.

Figure 1: The time plot of  the Consumer Price Index (CPI) from January 2014 to December 2023

Figure 2: The time plot of  the Interest Rate from January 2014 to December 2023

Figure 3: The time plot of  the Unemployment Level from January 2014 to December 2023

The above plot illustrates the trend and changes in 
interest rates over the specified period, providing insights 

into how rates have fluctuated over time.

This plot provides a visual representation of  the trends in unemployment over the extended period



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This plot shows the distribution of  CPI values over time, 
highlighting the variability and overall trend.
The analysis of  the Consumer Price Index (CPI) over 
the period from January 2014 to December 2023 yielded 
several important findings regarding the relationships 
between CPI and the selected economic indicators: 
Unemployment Level, Interest Rates, and Sales Index.

Correlation Analysis
The correlation analysis revealed that CPI has a moderate 
positive correlation with Interest Rates (r = 0.46) and 
a weaker positive correlation with the Sales Index (r = 
0.29). Conversely, there is a moderate negative correlation 
between CPI and Unemployment Level (r = -0.44). 
These correlations suggest that as interest rates and sales 

Figure 4: The scatter plot of  the Consumer Price Index (CPI) from January 2014 to December 2023

Table 2: Key correlations between the variables
CPI Unemployment Level Interest Rate Sales Index

CPI 1.000 -0.436 0.458 0.289

Unemployment Level -0.436 1.000 -0.551 -0.215

Interest Rate 0.458 -0.551 1.000 0.208

Sales Index 0.289 -0.215 0.208 1.000

increase, CPI tends to rise, while higher unemployment is 
associated with a decline in CPI.

CPI and Unemployment Level
There is a negative correlation of  -0.44, indicating that as 
CPI increases, the unemployment level tends to decrease, 
and vice versa.

CPI and Interest Rate
There is a positive correlation of  0.46, suggesting that 
higher interest rates are associated with higher CPI values.

CPI and Sales Index
There is a moderate positive correlation of  0.29, 
indicating that increases in the Sales Index are associated 
with increases in CPI.

Other Correlations of  Note
Unemployment Level and Interest Rate 
There is a strong negative correlation of  -0.55, indicating 
that higher unemployment levels are associated with 
lower interest rates.

Unemployment Level and Sales Index
There is a weak negative correlation of  -0.21, suggesting 
a slight tendency for unemployment to decrease as the 
Sales Index increases.

Regression Analysis
The regression analysis provided a more detailed 
understanding of  these relationships. The model showed 
that:

Interest Rate
A significant positive relationship exists between Interest 
Rates and CPI, with a coefficient of  6.85 (p = 0.003). 
This indicates that a one-unit increase in the interest rate 
is associated with a 6.85 unit increase in CPI, holding 
other factors constant.

Sales Index
The Sales Index also has a positive and statistically 
significant impact on CPI, with a coefficient of  0.11 (p 
= 0.029). Although this effect is smaller compared to 
the interest rate, it confirms that increased consumer 



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spending, as reflected in the Sales Index, contributes to 
inflationary pressures.

Unemployment Level
The analysis found a significant negative relationship 

between Unemployment Level and CPI, with a 
coefficient of  -2.28 (p = 0.013). This suggests that higher 
unemployment leads to a decrease in CPI, possibly due to 
reduced consumer spending and demand.

Table 3: The Regression table showing classifications of  the indicators
The Regression Analysis Table

Coefficients P-Values 95% CI Lower Bound 95% CI Upper Bound

Const 229.227 1.4E-47 210.658 247.795

Unemployment_Level -2.276 1.3E-02 -4.063 -0.489

Interest_Rate 6.849 2.7E-03 2.424 11.274

Sales_Index 0.108 2.9E-02 0.011 0.205

Model Summary
Dependent Variable
CPI

R-Squared
0.288 (28.8% of  the variance in CPI is explained by the 
model)

Adjusted R-Squared
0.270 (Adjusted for the number of  predictors)

F-statistic
15.65 (Significant at p < 0.0001)

Prob (F-statistic)
1.31e-08 (Indicates that the overall model is statistically 
significant)

Coefficients
Constant (Intercept)
229.227 (The expected CPI when all predictors are zero)

Unemployment Level
-2.276 (A negative coefficient, suggesting that an increase 
in unemployment is associated with a decrease in CPI. This 
result is statistically significant with a p-value of  0.013)

Interest Rate
6.849 (A positive coefficient, indicating that an increase 
in interest rates is associated with an increase in CPI. This 
result is statistically significant with a p-value of  0.003)

Sales Index
0.108 (A positive coefficient, suggesting that an increase 
in the sales index is associated with a slight increase in 
CPI. This result is statistically significant with a p-value 
of  0.029)

Diagnostics
Durbin-Watson
1.895 (Close to 2, suggesting no strong autocorrelation 
in the residuals)

Omnibus Test
Not significant, suggesting that the residuals are normally 
distributed.

Interpretation
• The model shows that both the interest rate and 

the sales index positively impact the CPI, while the 
unemployment level has a negative effect on CPI. All 
predictors are statistically significant, contributing to the 
explanation of  CPI variability.

• The R-squared value of  0.288 indicates that the 
model explains about 29% of  the variance in CPI, which 
suggests that other factors not included in the model may 
also influence CPI.

Hypothesis Testing
All three null hypotheses, which posited no significant 
relationships between CPI and the individual economic 
indicators, were rejected based on the p-values obtained 
from the regression analysis. This confirms that 
Unemployment Level, Interest Rates, and Sales Index are 
significant predictors of  CPI. This is further summarized 
below:

Hypothesis
There is no significant relationship between CPI and 
Unemployment Level.

o P-Value: 0.013
o Result: The null hypothesis is rejected. There is a 

significant relationship between CPI and Unemployment 
Level.

Hypothesis
There is no significant relationship between CPI and 
Sales Index.

o P-Value: 0.029
o Result: The null hypothesis is rejected. There is a 

significant relationship between CPI and Sales Index.

Hypothesis
There is no significant relationship between CPI and 
Interest Rates.



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o P-Value: 0.003
o Result: The null hypothesis is rejected. There is a 

significant relationship between CPI and Interest Rates.
In all three cases, the p-values are below the significance 
level of  0.05, leading to the rejection of  the null 
hypotheses. This indicates that each of  these variables-
Unemployment Level, Sales Index, and Interest Rates-has 
a significant relationship with CPI.

Model Performance
The regression model explained about 28.8% of  the 
variance in CPI (R-squared = 0.288), indicating that 
while the model is statistically significant, other factors 
not included in the study also influence CPI. The 
diagnostic tests confirmed the validity of  the model, 
with no significant issues related to multicollinearity or 
autocorrelation.

Summary
This study focused on modeling the United States 
Consumer Price Index (CPI) over the period from 
January 2014 to December 2023, examining the 
relationships between CPI and key economic indicators 
such as Unemployment Level, Interest Rates, and Sales 
Index. The objective was to develop a predictive model 
that would help better understand the factors influencing 
CPI, which is a critical measure of  inflation and economic 
health.
Through the application of  time series data and multiple 
regression analysis, significant relationships were identified 
between CPI and each of  the explanatory variables. The 
study found that both Interest Rates and Sales Index 
positively correlate with CPI, suggesting that increases 
in these variables are associated with higher levels of  
inflation as measured by CPI. Conversely, Unemployment 
Level was found to have a negative relationship with 
CPI, indicating that higher unemployment tends to be 
associated with lower inflation.
The regression model developed explained approximately 
28.8% of  the variance in CPI, which, while statistically 
significant, suggests that other factors not included in the 
model also play a role in influencing CPI. These findings 
were further validated through hypothesis testing, 
where the null hypotheses that there are no significant 
relationships between CPI and the explanatory variables 
were all rejected.

CONCLUSION
The results of  this study underscore the complexity 
of  inflation dynamics and the multifaceted nature 
of  the Consumer Price Index. The relationships 
identified between CPI and the economic indicators—
Unemployment Level, Interest Rates, and Sales Index—
are consistent with economic theory and previous 
research, yet they also highlight areas where CPI may not 
fully capture the cost of  living or economic reality.
The positive relationship between CPI and Interest Rates 
aligns with the understanding that higher borrowing 

costs can lead to higher prices for goods and services, 
thus driving inflation. The significant impact of  the Sales 
Index on CPI further emphasizes the role of  consumer 
spending and retail activity in shaping inflationary trends. 
On the other hand, the negative relationship between CPI 
and Unemployment Level reflects the reduced purchasing 
power and demand that typically accompany higher 
unemployment, leading to downward pressure on prices.
However, the relatively modest R-squared value indicates 
that CPI is influenced by a broader set of  factors 
beyond those included in this study. This suggests that 
policymakers and economists need to consider a wider 
range of  variables and possibly more nuanced models to 
fully understand and predict inflation.
Economic Implications
The findings of  this study have important implications 
for economic policy and decision-making in the United 
States:

Monetary Policy
The positive relationship between Interest Rates and CPI 
suggests that central banks, like the Federal Reserve, can 
use interest rate adjustments as a tool to manage inflation. 
However, the complexity of  this relationship also implies 
that rate hikes alone may not be sufficient to control 
inflation, especially if  other inflationary pressures, such 
as those from the labor market or consumer demand, are 
at play.

Labor Market Policies
The inverse relationship between Unemployment 
Levels and CPI indicates that policies aimed at 
reducing unemployment could have inflationary effects, 
particularly if  they stimulate demand. Policymakers must 
balance efforts to achieve full employment with the need 
to keep inflation in check, ensuring that wage growth and 
productivity increases are aligned.

Consumer Spending and Retail Sector
The impact of  the Sales Index on CPI highlights the 
importance of  consumer confidence and spending 
patterns in driving inflation. Policymakers should 
consider how changes in consumer behavior, whether 
due to economic conditions, shifts in preferences, or 
external shocks, can influence inflationary trends. This 
could involve monitoring retail sales more closely as an 
indicator of  potential inflationary pressures.

Inflation Measurement
The study also raises questions about the adequacy of  
CPI as a measure of  the cost of  living. Given that CPI 
may not fully capture all factors influencing inflation, 
there is a need for continuous refinement of  the index. 
This could involve incorporating additional variables or 
developing complementary indices that provide a more 
comprehensive picture of  inflation and economic well-
being across different regions and demographic groups.
In conclusion, this study contributes to the understanding 



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of  the relationships between CPI and key economic 
indicators, offering insights that can inform more effective 
economic policies. The results suggest that while CPI is 
a valuable tool for measuring inflation, it must be used in 
conjunction with other economic indicators and models 
to accurately assess and manage inflationary pressures in 
the U.S. economy.

REFERENCES 
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