Bank Credits. The Effects of Inflation and Population Growth

Table of Contents

Introduction

Literature Review

Theoretical Model and Data Description

Economic Results and Interpretation

Conclusion

References

ABSTRACT

In the following project it will be a brief definition of the econometrics and what is their role. Also, it will be examined in the case of the United States of America which was one of the countries which affected the most from the financial crisis of 2008, how the bank credit is affected by inflation and population growth. In order to do this, it will take place a literature review which will be compared to econometric tests. More precisely, the OLS Method in which will be examined the SRF and the PRF, BPG, White, Chow if it is required, Serial correlation and Ramsey Reset Test. At the end there is a small discussion about the findings from the test and the academic literature.

Introduction

Econometrics are useful tools in the hands of the economists and policymakers. They use that tools in order to put the statistical and mathematical theories of economics in application with the intention to test hypothesis and forecast the future trends. Moreover, as it is mentioned by OuliarisS. (2017) econometrics are economic models which try to explain the relationship between one or more economic variables to other economic variables, but the main purpose of econometrics is to convert qualitative statements into quantitative statements. Furthermore, the relationship between these variables can be either positive or negative. To be more specific, positive relationship is when the one variable increases the other variables increase as well, while negative is when the decrease of the one variable will cause an increase to the other variables.

To continue with, in the following text will be examined how the population growth rate and inflation can affect the bank credit. It is also easily understood that there are and many other factors that should be taken into account before to be able to conclude whether the inflation and population growth rate have a positive or negative impact on the bank credit. Further, there are plenty factors that can influence the bank credit such as interest rates, banking regulations, especially after the economic crisis of 2008, retail banking, etc. In this project will be examined how these variables interact in the example of the United States of America. The data that will be used are from 32 observations, more precisely from the year 1984 to 2015.

Literature Review

To begin with, it would be wise to mention the meaning of bank credit. Bank credit in simple words is the total amount of funds that a bank or a financial institution can provide to an individual person or a business and the agreement between the two parties that the borrower will repay the funds plus the interest. Moreover, according to Havemann J. (2009) the past years the bank credit had grown overly until the financial crisis of 2008 which was the biggest economic disaster since the Great Depression of 2009 and the banking system was ready to collapse. Since then everything changed for the bank credit. The main reason for the changes in the bank credit system according to Maxfield J. (2015) was because some of the main causes led to the crisis were the securitization of the loans which means less monitor to the quality of the underwriting standards and also the removal of the securitized loans by the banks. In the sequence, is the credit default swaps which were financial instruments developed by JP Morgan and proclaim an end at the credit risk, but the truth was that the credit risk replaced by the counterparty risk.

To continue with, as it is mentioned in the previous paragraphs, the project is focused on how the inflation and population growth rate can influence the bank credit. In simple words inflation is the relationship between money value and the purchasing power. Inflation is the rate that the prices of goods and services increased and as a consequence the purchasing power decreased. Central Banks monitor the inflation in order the economy running smoothly. Moreover, according to Pritchard J. (2017) the inflation affects the bank credit. More precisely, inflation pushes interest rates to rise and the bank credit to fall, an explanation for that is because during periods with high interest rates the banks should pay more interest to the investors which means that the total borrowing capacity of the banks is decreasing. Besides that, the inflation have effects and on loans, since inflation makes the long term loans seem more affordable and easier for the borrowers to handle them.

Furthermore, the population growth rate is the last variable at this particular project. The meaning of the population growth rate is to express the change in the size of the population during a specific time period. It is a factor that affects the inflation and as a consequence the bank credit. In addition, as it mentioned by Ozimek A. (2017) in his research justify both with theory and empirical results that inflation and population growth are strongly connected. From the findings comes to the result that population is a useful tool which can be used also as a driver of the inflation but is underappreciated both by economists and policymakers. Moreover, according to the findings of the research a declining population can be the main reason for the creation of deflation pressures on a country. That means that many countries is possible to face abnormal low inflation due to low population growth and a solution for that except from monetary easing and fiscal policy could be the immigration in order to hit inflation targets. To sum up, it is obviously that there are plethora of factors that can affect bank credit and the positive relationship between inflation and population growth, which implies that whenever one of these variables increase will lead to an increase of the other variable and as a consequence a decrease in bank credit.

Theoretical Model and Data Description

To continue with, as it mentioned in the previous paragraphs the topic of this particular project will examine the inflation and population growth rate compared to bank credit. To be more specific, the research focuses the relationship of these variables in the case of the United States of America. The independent variables are the inflation and population growth, while the depend variable is the bank credit. In addition, in order to be more reliable the results and not biased, it will be examined results from 32 observations and more precisely from 1984 to 2015. The data for the research have been taken from the Federal Reserve Bank of St. Louis one of the 12 regional Reserve Banks in United States.

Economic Results and Interpretation

Moreover, as it is already mentioned the paper will examine the relationship between the inflation and population growth rate compared to bank credit of United States. Additionally the Ordinary Least Squares method (OLS) will be used with the purpose to find the equation of Sample Regression Function (S.R.F.) as well as the equation of Population Regression Function (P.R.F.)

Estimated Equation of the Model

Dependent Variable: BANK_CRED

Method: Least Squares

Date: 01/10/18 Time: 22:58

Sample: 1984 2015

Included observations: 32

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Moreover, the table shows that the correlation coefficient of inflation is 0.013276 and population growth is 0.054164. According to Brooks C. (2008) that means that both variables have a positive relationship with the dependent variable, which means that when the prices of the independent variables are increasing there is an increase at the price of bank credit. Also, the results also reveal that the population growth has a more positive relationship with bank credit than inflation.

R-Squared

In the sequence, statistical measure R-Squared or indicates the percentage that the data used are fitted or not to the model. As it is justified by Brooks C. (2008) and Peter H. (2012) it is a statistical measure which can take prices between 0 and 1. The higher the price of R-Squared the best fitted the data to the model. Further, in many cases it is measured in percentage. In this particular model the price of R-Squared is 0.253969 or 25.3969% which means that is closer to 0 and that indicates that the data of the model are not fitted well.

T-Statistic

Furthermore, according to Srinivas D. et al (2012) and Brooks C. (2008) t-test is commonly used with small sample sizes. It is focusing in three major components, which are the t-statistic, the t-distribution and the degrees of freedom. For large sample sizes it would be wise from the side of the statisticians to use other tests than t-test. Moreover, it is crucial to compare the results from the t-test with the t-critical in order to justify whether the Ho hypothesis should be rejected or not reject at specific degrees of freedom and at 0.05 level of significance.

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