Time Series Trend Analysis on Nigerian Food Prices using the Box Jenkins ARIMA Model
1.1 CHAPTER ONE/INTRODUCTION
This chapter deals with the background of the study, problem statement, aim and objectives, scope and limitation of the study, the significance of the study, Time Series Modelling and the Box-Jenkins Approach.
1.2 Background of the study
Nigeria is the largest Nation in Africa, in terms of population and it is predominantly an agriculture economy (Abayomi, 1997). Nigeria has a land area of 98.3million hectares, of which 74million is good for farming. The agriculture sector contributes about 40% of the Gross Domestic product – National Food Security Programme (NFSP 2008). The focus of Agricultural production in Nigeria is largely on the crop subsector because of its contributions to food supply, raw materials supply, and national income. (Ashagidigbi, 2016).
There are six geo-political zones in Nigeria with each zone specializing in different areas of Agricultural production and farming systems. The geo-political zones are the North-West, North-East, North-Central, South-West, South-East and the South-South. This study is mainly carved around two geo-political zones, which are the North-West zone, and the North-Central zone because of the familiarity of the researcher to these two zones and the peculiarity of the four major food items that will be analysed in this study viz: yam, brown beans, tomatoes and onions – reasons being that they are common food items to everybody (both the rich and the poor) and predominantly produced in the mentioned zones.
The North West Zone
The North West zone lies between latitudes 9oN and 14oN and comprises of Kaduna, Katsina, Sokoto, Zamfara, Kano and Kebbi States. The area of the zone is 192,911km2, representing 21.1% of the national land area. According to the 1991 census, the total population of the zone is estimated at 19.9 million with an average density of 103 persons/km2. 80% of the zone lives in rural communities. The major ethnic group in the zone is the Hausa/Fulani; other significant ethnic groups include Kaje, Kataf, Gwari and Mada in Kaduna State and the Zuru in Kebbi State. The zone contributes 79% of the onion, 48% of the tomato, 42% of the pepper and 16% of the leafy vegetables produced by the nation. (Bukar et al., 1996-2010)
North Central Zone
The central zone has a land area of 296,898 km2 representing nearly 32% of the country’s total land area. It includes Benue, Kogi, Kwara, Niger, Nasarawa, Taraba and Plateau States as well as the Federal Capital Territtory, Abuja. Situated between latitudes 6o30’ 11o20’N, the zone has 14.1 million people (1991 census) with an average population density 47 persons/km2, which is less than half of the national average. The rural population constitutes 77% of the population in the zone. The major ethnic groups of the central zone are the Gwari, Basa, Baruba, Bagana, Nupe, Tiv, Yoruba, Igira, Igala, Idoma, Angas, Birom and Jukun. The major crops of the central zone are maize, millet, rice, sorghum, cowpea, groundnut, yam, cassava, melon, mango and orange. The central Zone are the largest rice and groundnut producer, producing well over 40% of national production in each case. The zone also produces 25% cowpea and 64% soybean, in addition to 34% yam and 98% Irish potato of national production. (Bukar, Adamu&Bakshi, 1996 – 2010)
The first essential component of social and economic justice is adequate food production. Even if a nation cannot send cosmonauts to the moon, it should be able to feed her population, only then can it occupy place of pride in the community of nations. Nigeria is a country richly blessed with abundant natural and human resources that if properly harnessed can feed its people and export the surplus to other countries, yet it is experiencing persistent food crisis in terms of both quantity and quality. Cases of malnutrition and under nutrition are growing by the day. The food intake requirements of majority of Nigerians have fallen far below the international standard. Past effort at improving food supply through agricultural production has not yielded any successful results (Otaha, 2013).
What is required of the government is to provide an enabling environment for the agricultural industry to prosper. This is because aside the fact that food prices are on the increase, we have some impediments that need to be removed in order to enhance the inflow of foreign investments and increase the prosperity of local investors.1.2
1.2 Problem Statement
High Cost of food prices:The monthly inflation rates released by the Nigerian Bureau of Statistics measures the percentage at which the prices of products rise or fall. When the data shows a reduction, therefore, it means there was an increase in the price of that product but at a reduced percentage. If there is a reduction in price, it means there was deflation. (Sahara Reporters, 2019). The high agricultural prices are attributable to a combination of factors. A distinction may be made between the effects of supply, demand, policy and other factors.
Seasonal foods: food prices may also vary based on the times of year when the harvest or the flavour of a given type of food is at its peak. For this study, yam, onions and tomatoes are a bit surplus during the raining season because it’s the harvest season while beans is in storage and it’s the planting season.
Scarcity: Shortage of foods may happen when not enough food is produced, such as when crops fail due to drought, pests, or too much moisture. But the problem can also result from the uneven distribution of natural resource endowment for a country, and by human institutions, such as government and public policies. Other factors that can bring about scarcity are cost of production, time and efficiency.
1.3 Aim and Objectives of the Project
To provide the trend of prices of Nigerian foods.
To compare and forecast the prices of some selected food items within two geo-political zones in Nigeria (the North-west zone and the North-central zone).
1.4 Scope and Limitation of the study
This work is limited to two Geo-political zones in Nigeria, the North West and the North Central and four different types of food itemsare considered,viz: Yam, Brown Beans, tomatoes and Onions, which are grown mostly from these two Northern geo-political zones. The study will also consider only four states of each of the zones for two consecutive years – 2017 and 2018. The study will also forecast the future prices of these food items via the comparison of the previous years. The study will expose the reasons why food demand varies in those various geo-political zones in Nigeria which could be based on good climate, cost of farming/transportation and population. The study will be done by analysing the raw Data produced by the Nigeria Bureau of Statistics (NBS) using the Box Jenkins Arima model in Time Series and Trend Analysis
1.5 Significance of the study
The findings of this study will redound to the benefit of the society considering the fact that food is very important in human’s life. The lower the price of food the more it is made available and accessible to people which will thereby bring improvement to their standard of living. This research will also be beneficial to the future researcher because they can get some information needed in their research and some of their questions could possibly be answered by this work.
1.6 Time Series Modelingand the Box-Jenkins Approach
‘Time’ is the most important factor, which ensures success in a business. It is difficult to keep up with the pace of time. But, technology has developed some powerful methods using which one can ‘see things’ ahead of time. These are the methods of prediction & forecasting. One such method, which deals with time based data is Time Series Modeling. As the name suggests, it involves working on time (years, days, hours, and minutes) based data, to derive hidden insights to make informed decision-making. Time series models are very useful models when you have serially correlated data. Most of business houses work on time series data to analyze sales number for the next year, website traffic, competition position and much more. (Analytics Vidhya Network, 2015, para. 1)
Time series modeling is a dynamic research area which has attracted attentions of researchers community over last few decades. The main aim of time series modeling is to carefully collect and rigorously study the past observations of a time series to develop an appropriate model which describes the inherent structure of the series. This model is then used to generate future values for the series, i.e. to make forecasts. Time series forecasting thus can be termed as the act of predicting the future by understanding the past. Due to the indispensable importance of time series forecasting in numerous practical fields such as business, economics, finance, science and engineering, etc., proper care should be taken to fit an adequate model to the underlying time series. It is obvious that a successful time series forecasting depends on an appropriate model fitting. A lot of efforts have been done by researchers over many years for the development of efficient models to improve the forecasting accuracy. As a result, various important time series forecasting models have been evolved in literature. One of the most popular and frequently used stochastic time series models is the Autoregressive Integrated Moving Average (ARIMA) model. The basic assumption made to implement this model is that the considered time series is linear and follows a particular known statistical distribution, such as the normal distribution. ARIMA model has subclasses of other models, such as the Autoregressive (AR), Moving Average (MA) and Autoregressive Moving Average (ARMA) models. For seasonal time series forecasting, Box and Jenkins had proposed a quite successful variation of ARIMA model, viz. the Seasonal ARIMA (SARIMA). The popularity of the ARIMA model is mainly due to its flexibility to represent several varieties of time series with simplicity as well as the associated Box-Jenkins methodology for optimal model building process. But the severe limitation of these models is the pre-assumed linear form of the associated time series which becomes inadequate in many practical situations. To overcome this drawback, various non-linear stochastic models have been proposed in literature; however from implementation point of view these are not so straight-forward and simple as the ARIMA models
Classical time series model in form of Autoregressive Integrated Moving Average (ARIMA) was developed by Box and Jenkins, often refers to as Box-Jenkins Approach, it is considered as one of the best forecasting model. This model has recorded significantly in financial and other academic researches during the last three decades. The method is suitable for short term forecasting a non-stationary series and seasonal time series data with large sample observations (Bashir, 2017).ARIMA stands for autoregressive integrated moving average, which is also known as the Box-Jenkins method. According to (Ruslana, 2017), ARIMA models are a popular and flexible class of forecasting model that utilize historical information to make predictions. This type of model is a basic forecasting technique that can be used as a foundation for more complex models. The steps in achieving an ARIMA model includes: Examine your data, Decompose your data, Test for Stationarity, Autocorrelations and choosing model order, Fit an ARIMA model, Evaluate and iterate.