Impact of Lending Rate, Money Supply and Exchange Rate on Real GDP Growth

STATISTICS
STUDENT ID:
[Pick the date]

Abstract
Through the given multiple regression analysis, an attempt has been made to highlight the
nature of relationship between real GDP (lgdp) and three independent variables namely
lending rate (LP), nominal effective exchange rate (LER) and money supply (LM). The
resultant multiple regression model presents a good fit considering that the model is
significant and has a coefficient of determination of nearly 1. Further, all the slope
coefficients are found to be significant. Besides, these slope coefficients are all positive
which is indicative that higher values tend to support higher real GDP. However, the
underlying correlation between the independent variables is of significance and impacts the
validity of the regression model. Going forward, it is imperative that the economists and
Federal Reserve should be mindful of the given model so as to ascertain a sustainable
recovery in US economy and provide a stable economy to businesses,
Introduction
The objective of the given assessment is to analyse the impact of the key variables such as
lending rate, money supply and nominal effective exchange rate on the real GDP growth. The
given research gains importance in the wake of the recent interest rate tightening that is
visible in the US owing to improving economic conditions. However, going ahead, it makes
sense that the Federal Reserve needs to be mindful of the impact of the different variables on
the real GDP growth of the nation. From the business perspective, this research can provide
valuable insight into the future decisions by the Federal Reserve and the expected interest rate
trajectory that could be witnessed in the near to medium future.
Empirical Results
The descriptive statistics of the four variables chosen for analysis are shown below.
It is apparent from the above descriptive statistics that neither of the variables is normally
distributed owing to non-zero values of skew and kurtosis. Skew is particularly significant in

case of lp variable where positive skew to the extent of 0.5 is present. This is a significant
observation considering the fact that one of the key requirements of a linear regression
analysis is that the underlying variables must be normally distributed. With regards to three
variables namely lgdp, LM, ler the variation in the data seems to be quite limited which is on
expected lines considering that these tend to represent key macroeconomic data which for a
country like US would not show very significant fluctuation. However, the variation for LP
variable which captures the lending rate tends to be highly variable.
Additionally, the following correlation matrix between the given variables is also of
significance.
A key aspect to be notices is that the two independent variables LM and LER tend to show a
significantly high amount of correlation or dependence which adversely would impact the
validity of the regression model.
Method
The key tool that has been used in the given analysis if OLS regression which has been
conducted using Excel as the enabling tool. This regression analysis allows the prediction of
a linear relationship where lgdp is the dependent variable while all the other three variables
are independent variables. Hence, this linear equation can be then used to estimate the lgdp
for differing values of the independent variables and most importantly understand the
respective impacts of the various independent variables by analysing the underlying
regression coefficients. The regression output obtained from Excel is illustrated below.