Relationship between selected macroeconomic variables and S&P BSE index: A statistical analysis

The relationship between stock market and various macroeconomic variables has always been discordant. Studies indicate that stock market is influenced by changes in various macroeconomic variables. Some of which affect the stock market positively while others have an adverse impact. This article examines the impact of five macroeconomic variables i.e. Exchange Rate, Inflation, Interest Rate, Money Supply and Consumer Price Index on BSE S & P index of India. It covers a period of 60 months from October, 2014 to September, 2019. Statistical Package for Social Sciences (SPSS) is used to test the Descriptive Statistics, Correlation Matrix and Regression analysis (Goodness of Fit, Variance Inflation Factor, Coefficient of Determination and Durbin – Watson d-Statistic). Analysis results indicate that S & P BSE value has a positive correlation with all independent variables except for Interest Rate; whereas Interest Rate is seen to have a negative correlation with all variables. Money Supply and Consumer Price Index have a positive relation with Exchange Rate and negative correlation with Interest Rate. VIF of all the independent variables, except for Money Supply and Consumer Price Index are within the acceptable limit and influence of independent variables on the S & P BSE value appears to be very strong.

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S&P BSE Value: S&P BSE Value vs ER (USD) vs Inflation vs IR (%) vs M3 (Billions)Close ×

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Introduction

The Indian stock market had seen various up-down since 1991, after the government implemented the LPG, i.e., Liberalization, Privatization and Globalization. According to the Economic Survey 2018-19, India's growth rate is forecasted to be between 7.0 -7.5 percent for the FY19 as compared to 6.80 percent for FY18 and 7.20 in FY17. The government has planned on achieving a fiscal target of 3.40 percent of GDP and 44.50 percent debt to GDP ratio with the 3.40 percent inflation for the FY18. Over the years, India has attracted many investments by different economies and a large consumption base. With this growth rate, India also became the fastest growing economy in FY18. Stock market is a place where investors, whether Indians or foreigners can invest or take the funds for capital appreciation. Their decision to invest or withdraw the funds depends upon the numerous factors. For this cause Indian stock market plays a vital role in the growth of Indian economy and every movement on it puts an impact on the performance of the economy. The stock prices reflect all information about the stocks and also the expectations of the future performances of corporate sector. As a result, if stock prices reflect these assumptions in real, then it should be used as a major indicator for the economic activities (Ray S, 2012) [1] . Hence the dynamic relationship between stock prices and macroeconomic variables contains academic interest as well as policy implications. Macroeconomics is the analysis of the nation's economy as a whole. It scrutinizes the cyclical movements and trends exists in economy such as Gross Domestic Production, Unemployment, Inflation, Money Supply, Budget Deficits, International Trade and Exchange Rate, etc. The various schools of thoughts like Classical theory by Adam Smith, David Ricardo, Thomas Robert Malthus and John Stuart Mill, Keynes theory by John Maynard Keynes, New Classical theory, New Keynesian theory and New Growth theory developed their own views about the role of macroeconomic variables in economy by considering different assumptions. But, they accepted that macroeconomic variables are the most remarkable variables and Government's can't make their policies, rules and regulations without contemplating them.

So, macroeconomic variables are the key indicators to show the prevailing trends in the economy as well as stock market.

Bombay stock exchange (BSE): A bird's eye view

Established in 1875, the BSE (formerly known as Bombay Stock Exchange Ltd.) is Asia's oldest stock exchange was founded by an influential businessman Premchand Roychand, located at Dalal Street, Mumbai. The BSE is the world's 10th largest stock exchange with an overall market capitalization of more than $2.2 trillion. Today, BSE is the world's number one exchange in the world in terms of the number of listed companies (over 4900). It is the world's 5th most active in terms of number of transaction handle through its electronic trading system. The BSE Index, SENSEX, is India's first and most popular stock market benchmark index. Exchange traded funds (ETF) on SENSEX, are listed on BSE and in Hong Kong. SENSEX, first compiled in 1986, was calculated on a "Market Capitalization-Weighted" methodology of 30 component stocks representing large, well-established and financially sound companies across key sectors. SENSEX is being calculated on a free-float market capitalization methodology.

The free-float market capitalization-weighted methodology is a widely followed index construction methodology on which majority of global equity indices are based; all major index providers like MSCI, FTSE, STOXX, S&P and Dow Jones uses the free float methodology.

Review of Literature

The various researchers did a remarkable work to evaluate the association between changes in macroeconomic variables and stock market returns in India by using different tools and techniques. Hemavathy P & Gurusamy S (2016) [2] , Poornima DS & Ganeshwari M (2016) [3] studied the casual relationship of NSE and exchange rates. By using Granger causality test, exchange rates have a unidirectional casual relationship with stock price in the short run and the author referred as a vibrant connection. But Megaravalli AV & Sampagnaro G (2018) [4] found that Indian stock market have a long tern association with the inflation and bidirectional causality from Indian stock market to exchange rate and the stock market is somewhat dependent on the macroeconomic variables of the economy (Pilinkus D, 2010) [5] . Box-Jenkins ARIMA technique was used by Gay Jr. RD (2016) [6] to analyse both the positive and negative correlation between the stock market with exchange rate and oil price respectively. Benaković D & Posedel P (2010) [7] analyses the strength of association of independent by Arbitrage Pricing Theory (APT). He analysed the market index and proved that, interest rates, oil prices and industrial production have a positive impact. Aanchal (2017) [8] found that all the independent variables and market data were non-stationary in nature. Sometimes it was also found that both the independent and the dependent variable have no relation. Melicher RW (1974) [9] examines link between various risk factors and data of these firms by CAPM model to ascertain the expected returns on any asset with the given sensitivity of the asset and the returns of the market. The sensitivity of any stock is found by dividing the covariance of the asset with the market return; by the variance on market returns. Ramanujam V & Leela L (2011) [10] examines the effect of various macroeconomic variables, using the Wald test and the results were proved that these variables have a long term effect on the stock market. Bhattacharya B and Mukherjee J (2002) [11] , Nath GC and Samantha GP (2002) [12] found the causal relationship between stock prices and macroeconomic factors in India by Toda and Yamamoto. It was found that industrial production affects significantly the stock prices. Similar results are also obtained by Chakravarty S (2005) [13] , but Johansen and Juselius's cointegration test pointed out, at least one cointegration vector and long-run relationships between BSE Sensex with index of industrial production and call money rate (Dasgupta R, 2012) [14] . Positive relationship between industrial production and stock prices was examined by using Granger causality test and observed uni-directionality from industrial production to stock prices in India. The results of Chowan PK et al. (2000) [15] are supported by the similar findings of Sahu NC & Dhiman HD (2011) [16] . They have tried to explore the causal relationship between stock market indicator and macroeconomic variables of India by using correlation, regression and Ganger causality techniques. Sarkar P (2005) [17] examined the relation between growth and capital accumulation exists in case of India. Maken et al. (2012) [18] found that, there is a positive relation between FII and Sensex, call rate and Sensex, whereas exchange rate and Sensex shows a negative relation by using Granger causality test. However, the studies of Kanakaraj A et al. (2008) [19] , Ray S (2012) [1] and Naik PK & Padhi P (2012) [20] and others have found significant relationship between macroeconomic variables and stock returns. They have tried empirically to explore upon and answer that if the recent stock market boom can be explained in the terms of macroeconomic fundamentals and have concluded by recommending a strong relationship between the two. Johansen's approach of co-integration and Toda -Yamamoto Granger causality test was used by Ahmed S (2008) [21] to investigate the relationship between stock prices and the macroeconomic variables using quarterly data whereas, Singh D (2010) [22] attempted to explore the causal relation between the BSE Sensex and three key macroeconomic variables of Indian economy by using correlation, unit root stationarity tests and Granger causality test. Results showed that IIP was having bilateral causal relationship with BSE Sensex, while WPI was found to be related to stock market unidirectional. Srivastava A (2010) [23] demonstrated the effect of macroeconomic variables on the Indian stock market by using Jhonsen's cointegration analysis and Vector Error Correction mechanism. It was concluded that IIP, WPI and Interest Rate are the most effecting variables in the long run (Pal K and Mittal R, 2011 & Naka A, Mukherjee TK and Tufte D, 2001) [25] whereas, macroeconomic variables and BSE 500 were cointegrated with each other and bilateral causality exists during the World Financial Crisis (Hojatallah G and Ramanarayana CS, 2011) [26] . Hosseini MA (2011) [27] investigated the nature of the causal relationships between the Bombay Stock Exchange's stock prices and the key macroeconomic variables. The results of the study revealed both long-run and short-run linkages between the macroeconomic variables and stock market indices by applying Augmented Dickey Fuller (ADF) unit root test, Johansen-Juselius Multivariate Cointegration and Vector Error Correction Model techniques. The study also confirmed the evidence of causality among the variables (Patel S, 2012) [28] . Ljung-Box Q test, Breusch-Godfrey LM test, Unit Root test and Granger Causality test was employed by Tripathy N (2011) [29] to examined the weak form market efficiency and casual relationship between the selected macroeconomic variables and the Indian stock market. Pethe A and Karnik A (2000) [30] have taken monthly data over the period of Apr 1992 to Dec 1997 to conduct a study on relationship between Indian stock market and macroeconomic factors using error correction model and co-integration. Their study showed that the condition of economy and the stock market prices don't show a long run relationship. The review indicated that, the relationship between macroeconomic variables and stock market can be divided into three groups. Some researchers found that there exist a significant relationship between macroeconomic variables and stock market. Some found that, there is no significant relationship between macroeconomic variables and stock market, while other found that some macroeconomic variables are significant and some are not (Barakat MR et al., 2016 and Joshi P, 2015) [32,33] . According to the past researches, it can be concluded that many macroeconomic factors combined affect the stock market and almost every Indian research was done on the two major indices of Indian Stock Market, i.e., CNX Nifty and BSE Sensex. Therefore, it is viable to use BSE Sensex for analysing the effect of macroeconomic variables on the stock market of India. Based on the Literature Review one of the major gaps observed from the studies was the time period of which the stocks were analysed. The study conducted before by various scholars and researchers was limited on the ground of shorter time span which could have been studied for a longer time period in order to have a clear picture and to obtain more precise results to know the impact of the selected macroeconomic variables on the stock prices.

Objective of the study

Objective of the study are to determine the relationship between stock market value and selected macroeconomic variables through selected statistical methods. The present study is focused on the following objectives.  To measure the movement of selected macroeconomic variables and stock market values, and  To study the relationship between selected macroeconomic variables and stock market values.

Methodology

The study examines the impact of selected macroeconomic variables on stock market values, while other variables assumed to be constant. Data have been analysed through Descriptive Statistics, Correlation Matrix and Regression analysis (Goodness of Fit, Variance Inflation Factor, Coefficient of Determination and Durbin -Watson d-Statistic).

Variables description

For the purpose of the above study, secondary data has been used to analyse the effect and the influence of macroeconomic variables on stock prices of India. The closing prices of the stock market and macroeconomic variables are gathered on a monthly basis from Bloomberg terminal for the study. The selected macroeconomic variables are taken from various websites and their symbol, unit of measurement and proxy of each variable are illustrated in Table -2.

Exchange rate

An exchange rate is the cost of a country's cash as far as money and has two segments, the country's own money and foreign currencies can be cited either straight forwardly or roundabout way (Garg K & Kalra R, 2018) [33] . The whole procedure of sending out and bringing in procedure of any nation is entirely subject to the exchange rate of money value of the country. Gunasekarage A et al. (2004) [35] and Adam MA & Tweneboah G (2009) used USD (United States Dollar) as a proxy for exchange rate. It is also affect the net exports (Osamwanyi IO & Evbayiro-Osagie EL, 2012) [37] . Vejzagic M & Zafarat H (2013) found that changes in exchange rate would have an impact on firm's competitiveness as it affects the price of foreign currency, leading to changes in the firm's profit and equity which in return will lead to price adjustments in the stock market.

Inflation

Inflation is an ascent in cost of price of a few things over a period of time. Everyone of the index is made in a particular way with a specific year as the base year and they consider the value changes over a year. According to Pearce DK & Roley VV (1985) [39] , it has been calculated from Wholesale Price Index (WPI). Talla JT (2013) [40] used Consumer Price Index is a proxy for inflation. He states that inflation can affect stock market either positively or negatively. He added that unexpected and expected inflation determines the direction of the relationship between stock market and inflation.

Interest rate

The rate of interest is the rate of exchange between future and current consumption (Reilly F & Brown K, 2003) [41] . When borrowing money and receiving on it on their saving difference is accepted by people for accept to pay, which the author referred to as the pure time value of money. Moreover, if the investor expects an increase in price (Rise in inflation), he/she will demand a higher rate of return, that is, pure time value of money plus expected inflation rare. Joseph NL & Vezos P (2006) [34] stated stat, it is the difference between two time factor t and (t-1).

Money supply

Walter J (1989) [42] argued that monetary aggregates can be used to measure any nation's money stock. He also stated that in order to forecast the changes in economic activities, inflation and interest rate analysts need to study the relationship between the monetary measures and other macroeconomic variables. The components of money supply are drawn from the liability side at the balance sheet of the banking sector. Kumar R (2013) [43] and Barakar MR et al.

(2016) used M3 as a proxy of money supply and stated that there is a strong relationship between money supply and stock prices.

Consumer price index

The Consumer Price Index (CPI) is a measure that examines the weighted average of prices of a basket of consumer goods and services, such as transportation, food, and medical care. It is calculated by taking price changes for each item in the predetermined basket of goods and averaging them. Schwert GW (1981) examined that stock market responds negatively to the announcement of unanticipated inflation in the CPI. He reported that there are at least three theories that predict a positive relation between volatility and volume. First if investors have heterogeneous beliefs, new information causes both the price changes and the trading. Second, if some investors use price movements as information on which to make trading decisions, large price movements cause large trading volume. Finally, if there is short term "price pressure" due to illiquidity in secondary trading markets; large trading volume that is predominantly either buy or sell orders cause price movements. Whereas, using hourly data, Jain PC (1988) [45] found that CPI announcement surprises have significant negative effects on stock prices and trading volume was not associated with surprises in the CPI announcements and the results were consistent with the hypothesis that market participants interpret the surprises in announcements in an analogous manner and do not engage in additional trading. By using monthly data Chen NF, Roll R and Ross SA (1986) [46] found that inflation related variables were highly significant in the 1968-77 periods and insignificant both earlier and later. Smirlock M & Yawitz Y (1985) [47] found a significant positive response of long -term rates to unexpected inflation.

Comparison between macroeconomic variables and stock market return

Many academic researchers, financial and industry analysts and practitioners have tried to establish the relationship between macroeconomic variables and stock market movements from the past decades. They have done several empirical and descriptive studies to check the effect of macroeconomic variables on stock prices or vice versa and the existing relationship between the two. To help those who are interested in the stock market either government officials, investors or academics, a stock market index was invented as a tool in order to evaluate the decision of portfolio managers and investment advisors. According to Rafique A et al. (2013) [48] , the stock performance is measured through movement in the index. The fluctuations in the index are affected by macroeconomic, social, political and the firm's specific variables. In this work, we established some empirical proof relating designated macroeconomic variables to stock market return.

Exchange rate and stock market return

According to portfolio management, any inflow or outflow of foreign capital will be a result of changes in stock prices. When stock prices increases, they will attract towards foreign capital and when price decreases, they will be less appealing to foreign investors, which will lead to a corporate wealth (Vejzagic M & Zarafat H, 2013) [38] . It is also creation of country's external business and straightly related to balance of payment. The balance of payment deficit and the intensity of external reserve frequently influence exchange rate (Oaikhena H.E, 2002) [49] .

Inflation and stock market return

Chandra P (2004) [50] stated the effect of inflation rate towards the corporate sector has been found to be bi-directional. For unexpected inflation, an increase in general price level leads to the increase in cost of living, which forces people to invest less and relocate their resources for consumption. When inflation increases, nominal interest rate also increases. This will reduce the present value of the income generated by firms as the discount rate used to determine the intrinsic values of stocks went up. This will force stock prices to decrease (Amadi SN & Odubo TD, 2002) [51] . If the price elasticity of demand for the firm's product is high, a rise in inflation that leads to an increase in price will cause a decline in a firm's sale and net income and thus its stock price (Fama EF, 1981) [52] .

Interest rate and stock market return

Interest proportion varies with time, inflation and output of capital among others (Chandra P, 2004) [50] . According to Kelvin S (2000) [53] , the interest changes in systematized and synthesized financial segment of the economy are focused within anticipated range over monetary strategy. Alam MN & Uddin MGS (2009) [54] argued that, interest rate on deposit has a negative relationship with stock prices. When rates on deposit in the bank increase, people will redirect their money from capital market to the banks and this will lead to decrease in demand of shares stock prices, and vice versa.

Money supply and stock market return

Monetary aggregates can be used to measure any nation's stock (Walter J, 1989) [42] . In order to forecast the changes in stock prices, inflation and interest rates, researcher need to find out the relationship between the monetary measures and other macroeconomic variables. In the short run disequilibrium, supply and demand of capital might occur and it will change the ease of tightness of capital market (a decrease in the growth rate of money supply will results in decline in supply of capital and an increase in interest rate). Sudden changes in money supply among other variables might be a cause of this disequilibrium through affecting the normal risk free rate (Reilly F & Brown K, 2003) [41] .

Consumer price index and stock market return

Fama EF, (1981) [52] stated that negative stock returns with inflation are defined by the positive connection between stock returns and basic determinants like cost of capital, the average real rate of return capital and productivity of a firm. Fieldstein MS (1980) [54] also confirmed the effect of inflation on stock prices through corporate income taxation, cost depreciation and taxation of normal capital gains, since the depreciation is determined based on historical cost which is not affected by the increase in inflation rate. The depreciation is less then it is supposed to drive the real taxable earnings to go up (Hong H, 1977) [56] .

Analysis and interpretation 5.1. Descriptive statistics

Various descriptive statistics are calculated of the variables under study in order to describe the basic characteristics of these variables. The deviation is not regarded significant at 5% level of significance. It also enables us to determine the probable limits within which the population parameter may be expected to lie. So the descriptive statistics shows that the values are not normally distributed about its mean and variance or in other word we can says no randomness in data and therefore, is sensitive to periodic change and speculation.

Correlation matrix

It is found that S & P BSE value has a positive correlation with all independent variables except for IR (%). According to the correlation matrix obtained in the Table -4, it can be seen that various independent variables are inter-correlated to each other but some are very significant and can be used in further analysis. and negative correlation with IR (%).

Regression analysis 5.3.1 Goodness of fit

Levin RI & David SR (2005) [57] states that in multiple regression, F-test explains the level of influence of independent variable on dependent variable. If the value of significance is higher the significance F then it indicates that Independent variable has influence on dependent variable and vice versa. The output of Table-5 presents an analysis of variance (ANOVA). A good regression model generates high level of F value and very low level of F-significance value. In line with the thumb rule, the F-value is very high (250.00) and its significance value is the lowest (0.00) at 5 percent level of significance. This signals the goodness of fit of the model.

Variance inflation factor

Multicollinearity has been considered as a critical factor while execution of multiple regression analysis. Correlation among independent variables is considered as the main reason behind existence of multicollinearity. Results of the analysis would be biased, if we ignore the multicollinearity in the research data. VIF (Variance Inflation Factor) has been used as a multicollinearity diagnostic technique. VIF indicates whether independent variables have any correlation among themselves or not. If the value of VIF is more than 10 then it indicates the existence of multicollinearity in the data and if the value of VIF is less than 10 then it indicates that data is free from multicollinearity.

These coefficients indicate the direction of relationship between independent and dependent variables. These coefficients also tell us to what degree each predictor affects the outcome when the effects of all other predictors are held constant. The signs of coefficients of all the independent variables except IR (%) and CPI are as per theoretical predictions. Among the five independent macroeconomic variables IR (%) and CPI appears to have very high influence on S & P BSE value. As the IR (%) and CPI are increases by 1 unit, the S & P BSE value increases by 5005.8 and 533.0 units respectively, subject to the condition that, other variables are constant. This positive relationship appears to be unusual. The negative sign implies that stock index performances would decrease by 691.79 and 20.41 respectively for every unit increase in ER (USD) and Inflation subject to other variables are constant.

Coefficient of determination & Durbin -Watson dstatistic

The impact of macroeconomic variables on the S & P BSE value has been captured statistically by the regression model. From the model summery presented in Table - The combined influence of independent variables on the S & P BSE value appears to be very strong. Field A (2013) [58] states that R 2 represent the percentage of variance in the outcome that are explained by the independent variables or predictors. As shown in Table-7, the R 2 = 0.959, which implies that the macroeconomic variables account for 95.9% variation in the S & P BSE value? However, in a multiple regression model adjusted R 2 is the more reliable explanator of dependent variable thanR 2 . In the above model, adjusted R 2 is 0.955, which implies that 95.5% of the variation in S & P BSE is explained by the five macroeconomic variables, viz. CPI, Inflation, ER (USD), IR (%) and M3 (Billions). Only 4.5% changes in S & P BSE is caused by the external factors outside the model. Thus, S & P BSE is highly sensitive to the variations in CPI, Inflation, ER (USD), IR (%) and M3 (Billions).

Conclusion

The prime aim of the study was to examine factors influencing S & P BSE behaviour through selected macroeconomic variables. We expected a relationship between selected macroeconomic variables and stock indices performances as proxy for Indian Capital Market. The relationship was tested by using Descriptive Statistics, Correlation Analysis and Regression Analysis (Goodness of Fit, Variance Inflation Factor, Coefficient of Determination and Durbin -Watson d-Statistic) with the past 60 months data and we found that stock market indices are influenced by selected macroeconomic variables. In line with this, the grades also suggest the need for policy makers to design policies that will help to curtail rapid growth in money supply. There is also the need to formulate policies that are capable of enhancing the national income of the country. Export policies should be encouraged, as they rise to issue of balance of payments, their deficit or surplus and also responsible for the appreciation or otherwise of a nation's foreign exchange.

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Summarizes the previous studied that reviewed relationship between macroeconomic variables with countries under investigation,

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Sl. No.	Variable	Symbol	Data Source	Unit of Measurement	Proxy
1	Exchange Rate	ER	www.rbi.org.in	In Rs.	USD
2	Inflation	Inflation	www.mospi.gov.in	Rs, (%)	WPI
3	Interest Rate	IR	www.rbi.org.in	Rs. (%)	Deposit
4	Money Supply	M3	www.bankbazaar.com	Rs in Billions	M3
5	Consumer Price Index	CPI	www.rbi.org.in.com	In Rs.	Combined

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Macroeconomic variables used in the analysis

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Descriptive Statistics	—	S&P BSE Value	ER (USD)	Inflation	IR (%)	M3 (Billions)	CPI
Valid	N	60	60	60	60	60	60
Mean		31255.046	66.591	1.075	6.513	127647.889	132.258
S.E of Mean		595.785	0.375	0.413	0.079	2106.668	0.967
Median		29769.450	66.666	1.850	6.375	127334.705	131.300
S.D		4614.933	2.904	3.198	0.616	16318.181	7.489
Variance		21297605.483	8.432	10.231	0.380	266283029.475	56.083
Skewness		0.319	0.322	-0.543	0.920	0.209	-0.134
Kurtosis		-1.244	-0.517	-0.760	0.448	-1.010	-1.055
Range		16712.200	12.290	11.820	2.600	55349.800	26.400
Minimum		23002.000	61.340	-6.140	5.400	101150.650	119.400
Maximum		39714.200	73.630	5.680	8.000	156500.450	145.800
Sum		1875302.750	3995.460	64.490	390.800	7658873.330	7935.500

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Descriptive Statistics of Variables

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Correlation Analysis	—	S&P BSE Value	ER (USD)	Inflation	IR (%)	M3 (Billions)	CPI
Pearson Correlation	S&P BSE Value	1.00	0.52	0.65	-0.57	0.90	0.86
	ER (USD)	0.52	1.00	0.45	-0.55	0.75	0.74
	Inflation	0.65	0.45	1.00	-0.61	0.69	0.73
	IR (%)	-0.57	-0.55	-0.61	1.00	-0.81	-0.86
	M3 (Billions)	0.90	0.75	0.69	-0.81	1.00	0.98
	CPI	0.86	0.74	0.73	-0.86	0.98	1.00

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Correlation Matrix

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ANOVAa	—	—	—	—	—	—
Model		Sum of Squares	df	Mean Square	F	Sig.
1	Regression	1204523765.18	5	240904753.04	250.00	0.00b
	Residual	52034958.29	54	963610.34
	Total	1256558723.47	59

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ANOVA

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VIF Table	—	Unstandardized Coefficients	—	Standardize Coefficients	t	Sig.	Co linearity Statistics	—
		B	S.E	Beta			Tolerance	VIF
1	(Constant)	-59085.51	11370.47		-5.196	0.000
	ER (USD)	-691.79	68.63	-0.435	-10.080	0.000	0.411	2.432
	Inflation	-20.41	60.25	-0.014	-0.339	0.736	0.440	2.274
	IR (%)	5005.80	434.28	0.668	11.527	0.000	0.228	4.386
	M3 (Billions)	0.26	0.040	0.923	6.451	0.000	0.037	26.716
	CPI	533.00	107.47	0.865	4.959	0.000	0.025	39.662

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Variance Inflation Factor

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Model	R	𝐑𝟐	Adj. 𝐑𝟐	S.E of Estimate	Change Statistics	—	—	—	—	Durbin Watson
					𝐑𝟐 Change	F Change	df1	df2	Sig. F Change
1	0.979a	0.959	0.955	981.636	0.959	250.002	5	54	0.000	1.476

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Coefficient of Determination & Durbin – Watson d-Statistic

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