TIME SERIES MODELLING OF CONSUMER PRICE INDEX (A CASE STUDY OF DUTSE LOCAL GOVERNMENT FROM 2010 TO 2024)

ABSTRACT

This study examines the trend of the Consumer Price Index (CPI) in Dutse Local Government Area, Jigawa State, from 2010 to 2024 using the Ordinary Least Squares (OLS) method. The main aim of the research is to model the local price movement over time and provide simple forecasts to assist policymakers in understanding inflationary patterns at the grassroots level. The study employed annual CPI data simulated to reflect regional price behaviour, and a simple linear regression model of the form CPIₜ = a + b·t + eₜ was estimated manually. The results show that CPI has consistently increased over the 15-year period, with an estimated annual average rise of approximately 10.5 index points. The R-squared value of 0.99 indicates that the fitted trend explains nearly all variations in the CPI within the period under review. Forecasts for 2025–2027 also suggest a continued upward movement in prices, reflecting persistent inflationary pressure in the local economy. The study concludes that CPI in Dutse LGA follows a strong upward trend, and recommends that local authorities integrate such trend analyses into annual budget planning and inflation control strategies.

Keywords: Consumer Price Index, Trend Analysis, Ordinary Least Squares, Inflation, Dutse Local Government.

TABLE OF CONTENTS

APPROVAL PAGE.. ii

DECLARATION... iii

CERTIFICATION... iv

DEDICATION... v

ABSTRACT.. x

CHAPTER ONE.. 1

INTRODUCTION... 1

1.1 Background of the Study.. 1

1.2 Historical Background of the Study Area (Dutse Local Government Area). 2

1.3 Statement of the Problem... 3

1.4 Aims and Objectives of the Study.. 3

1.5 Research Questions. 4

1.6 Research Hypotheses. 4

1.7 Significance of the Study.. 4

1.8 Scope and Limitation of the Study.. 5

1.9 Definition of Key Terms. 5

CHAPTER TWO... 7

REVIEW OF RELATED LITERATURE.. 7

2.1 Introduction.. 7

2.2 Conceptual Framework.. 7

2.2.1 Importance of CPI in Economic Analysis. 8

2.2.2 Challenges in CPI Measurement 9

2.3 Overview of Time Series Modeling. 10

2.4 Theoretical Framework.. 12

2.4.1 Quantity Theory of Money.. 12

2.4.2 Cost-Push and Demand-Pull Inflation Theories. 14

2.4.3 The Random Walk Theory.. 15

2.4.4 Least square method.. 17

2.5 Empirical Review... 19

2.6 Summary of Literature Review... 21

CHAPTER THREE.. 22

RESEARCH METHODOLOGY... 22

3.1 Introduction.. 22

3.2 Research Design.. 22

3.3 Sources of Data. 22

3.4 Method of Data Collection.. 23

3.5 Method of Data Analysis. 23

3.5.1 Data Visualization.. 23

3.5.2 Stationarity Test 23

3.5.3 Model Identification.. 24

3.5.4 Model Estimation.. 24

3.5.5 Model Validation.. 24

3.5.6 Forecasting. 24

3.6 Model Specification.. 24

3.7 Assumptions of the Model 25

3.8 Ethical Considerations. 26

CHAPTER FOUR.. 27

DATA PRESENTATION, ANALYSIS, AND INTERPRETATION... 27

4.1 Introduction.. 27

4.2 Data Presentation.. 27

4.3 Descriptive Analysis. 28

4.4 Time Series Plot of CPI (2010–2024). 28

4.5 Stationarity Test (ADF Test). 29

4.6 Model Identification (Least Square Method Selection). 29

4.7 Model Estimation Results. 30

4.8 Diagnostic Checking. 30

4.9 Forecasting CPI (2025–2028). 31

4.10 Interpretation of Findings. 31

4.11 Discussion of Findings. 31

CHAPTER FIVE.. 32

SUMMARY, CONCLUSION, AND RECOMMENDATIONS.. 32

5.1 Introduction.. 32

5.2 Summary of Findings. 32

5.3 Conclusion.. 33

5.4 Recommendations. 33

References

CHAPTER ONE

INTRODUCTION

1.1 Background of the Study

The Consumer Price Index (CPI) is a vital economic indicator used to measure the average change over time in the prices paid by consumers for goods and services. It reflects the purchasing power of a country’s currency and serves as a key indicator of inflation (Oladipo & Akinlo, 2019). In Nigeria, the CPI is a fundamental tool used by policymakers, economists, and researchers to assess the cost of living, determine monetary policy, and evaluate the performance of the economy (National Bureau of Statistics [NBS], 2023).

Over the years, changes in consumer prices have significantly influenced economic stability, employment, and income distribution in Nigeria. The CPI provides insight into how the prices of essential commodities such as food, housing, transportation, and healthcare affect households and the general economy. Fluctuations in the CPI can influence interest rates, wage negotiations, and fiscal decisions (Adewale, 2021).

Time series modeling is a powerful statistical technique that helps in understanding and forecasting such economic variables. It involves using historical data to identify trends, seasonal patterns, and random fluctuations over time (Gujarati & Porter, 2010). By applying time series analysis to CPI data, researchers can develop models that help predict future inflationary trends and guide economic planning.

In Dutse Local Government Area of Jigawa State, as in many parts of Nigeria, the prices of goods and services have experienced consistent fluctuations between 2010 and 2024. These changes have been influenced by multiple factors such as agricultural output, fuel prices, government policies, and exchange rate volatility. Therefore, a time series modeling approach to the CPI of Dutse LGA is important to understand the pattern of inflation, predict future changes, and provide insights for both local and national economic planning.

1.2 Statement of the Problem

Inflation has remained one of Nigeria’s most persistent macroeconomic problems. The frequent rise in consumer prices erodes the purchasing power of citizens, increases poverty levels, and creates economic uncertainty (Okonkwo & Uche, 2018). Despite the existence of national data on inflation, there is limited empirical research focusing on local-level analysis of CPI trends, especially in Dutse Local Government Area.

The lack of localized CPI modeling makes it difficult for policymakers and local authorities to plan effectively. Without understanding the time-based dynamics of consumer prices, economic strategies such as wage adjustments, poverty alleviation, and agricultural interventions may not achieve the desired outcomes. Moreover, many previous studies on inflation and CPI in Nigeria have focused mainly on national aggregates, neglecting the regional variations that may exist (Nwokoma, 2020).

This study, therefore, seeks to model the time series behavior of the Consumer Price Index in Dutse Local Government from 2010 to 2024, identifying patterns and forecasting future trends.

1.3 Aims and Objectives of the Study

The main aim of this research is to model trend of the Consumer Price Index (CPI) in Dutse Local Government Area from 2010 to 2024 using the

Least Squares Method.

The specific objectives of the study are to:

1.      To examine the trend of CPI in Dutse from 2010 to 2024.

2.      To estimate a linear trend model for CPI using the Least Squares Method.

3.      To forecast CPI values for the years 2025–2027.

1.4 Research Questions

To guide the study, the following research questions are posed:

1.      What is the trend of CPI in Dutse from 2010 to 2024?

2.      What linear trend model best fits the CPI data?

3.      What will be the forecasted CPI values for 2025–2027?

      1.5 Research Hypotheses

To empirically address the research questions, the following hypotheses are formulated:

·         H₀₁: There is no significant trend in the Consumer Price Index of Dutse Local Government between 2010 and 2024.

·         H₀₂: The time series model does not significantly explain the variation in CPI values.

·         H₀₃: CPI changes have no significant impact on the socio-economic conditions of residents in Dutse.

1.7 Significance of the Study

This study is significant in several ways. Firstly, it contributes to the existing literature on inflation and price analysis in Nigeria by focusing on a local government area, which is often neglected in national studies. Secondly, the findings will provide valuable insights to policymakers and economic planners in Jigawa State to understand inflationary pressures at the grassroots level.

Thirdly, the study offers a practical application of time series modeling techniques, such as least square method, to real-world economic data. This will serve as a reference for students, researchers, and government agencies interested in economic forecasting. Lastly, the research will help residents and business owners in Dutse to better anticipate price changes and plan their expenditure accordingly (Adewumi, 2022).

1.7 Scope and Limitation of the Study

The study focuses on the Consumer Price Index (CPI) of Dutse Local Government Area from 2010 to 2024. The analysis employs time series modeling techniques to describe and forecast CPI trends. The scope is limited to secondary data obtained from the National Bureau of Statistics (NBS) and other reliable economic sources.

The study is limited by data availability and quality at the local level, as CPI figures are often reported nationally rather than by local government. To address this limitation, simulated data reflecting realistic CPI trends for Dutse will be used. The study also assumes that external shocks such as pandemics or policy changes do not drastically alter the CPI trend beyond the observed period.

1.8 Historical Background of the Study Area (Dutse Local Government Area)

Dutse is the capital of Jigawa State, located in the North-Western region of Nigeria. The name “Dutse” is derived from the Hausa word for “rock,” referring to the rocky hills that surround the town and define its landscape. The area is historically significant as one of the ancient Hausa settlements that developed as a small trading and agricultural community long before the colonial era (Adamu, 2012).

Dutse Local Government Area was officially created in 1976 following the local government reforms that reorganized administrative divisions across Nigeria. When Jigawa State was carved out of Kano State in 1991, Dutse was chosen as the state capital due to its central location, accessibility, and relatively developed infrastructure at the time (Usman, 2018).

The area lies approximately between latitude 11° 42′ N and longitude 9° 20′ E, covering an estimated 1,300 square kilometers. It shares boundaries with Kiyawa, Jahun, and Birnin Kudu Local Government Areas, and is easily accessible by road from Kano, Hadejia, and Kazaure.

The population of Dutse LGA, according to the National Population Commission (NPC, 2006) census, was about 153,000, and it is estimated to have exceeded 250,000 by 2024, due to natural population growth and rural-urban migration. The area is ethnically homogeneous, dominated by the Hausa and Fulani people, with Islam as the predominant religion.

Economically, Dutse serves as a commercial and administrative hub of Jigawa State. The major occupations include farming, trading, and livestock rearing. The fertile soil supports the cultivation of crops such as millet, sorghum, maize, and groundnut. Over the years, Dutse has also witnessed infrastructural development driven by both public and private investment, including educational institutions such as Jigawa State Polytechnic, Federal University Dutse, and several government ministries and parastatals.

In recent years, Dutse has experienced a steady rise in consumer activities due to urbanization, income growth, and increased government presence. This growth has consequently influenced price dynamics and cost of living in the area, making it an ideal case study for analyzing Consumer Price Index (CPI) trends and inflation patterns.

Thus, the choice of Dutse Local Government as the study area provides a balanced perspective on how price movements and inflation affect urbanizing communities in Northern Nigeria.

1.9 Definition of Key Terms

·         Consumer Price Index (CPI): A measure that examines the weighted average of prices of a basket of consumer goods and services, such as transportation, food, and medical care (NBS, 2023).

·         Inflation: The rate at which the general level of prices for goods and services rises, leading to a decrease in purchasing power.

·         Time Series Analysis: A statistical technique that deals with time-ordered data points to identify trends, seasonal effects, and other patterns (Gujarati & Porter, 2010).

·         Forecasting: The process of making predictions about future events based on historical data.

·         Least Square Method Model: A commonly used statistical model for analyzing and forecasting time series data, combining autoregression and moving average components (Box & Jenkins, 2016).