The ISLM model was considered indispensable and had great importance and recital for macroeconomic phenomenon at theoretical as well empirical analysis since 1960s. Now a days the applications of ISLM model has been greatly restricted to few situations depicts losing the fame. However, it has been a crucial part of the basic principles and manuscripts of macroeconomics long since 1960s. Observing the reliability and existence of ISLM model in case of Pakistan, for that purpose the study regressed two model; in first the basic model without including the model parameters and in second, with model parameters. The empirical results found the reliability and existence of ISLM model.
ISLM model, planned expenditure, Descriptive Statistics, ARDL approach.
ISLM analysis is a philosophical phenomenon, and to realize its determination one should be aware of the principles of basic economic theory (Casares and McCallum, 2006). The ISLM theory was considered indispensable and had great performance at both empirical and theoretical analysis of macroeconomic phenomenon since 1960s, however the thought didn’t persists now, which indicates the chronological changes in the model during last century according to requirements of situation. Now a days the applications of ISLM model has been greatly restricted to few situations depicts losing the fame. However it has been a crucial part of the basic principles and manuscripts of macroeconomics long since 1960s. Even though the Aggregate Expenditure or Aggregate Production formation of the ISLM model is vanishing from the macroeconomic manuscript and is being swapped with Aggregate Supply or Aggregate Demand model.
One of the interesting features of this advancement is that, in principle, the Aggregate Supply or Aggregate Demand model is the derivation result of ISLM framework. Previously, the Aggregate Expenditure or Aggregate Production multiplier model, that is fundamental part of ISLM model formation, appeared to be the compulsory part of the macroeconomic texts and principles, and the ISLM transitional model was the expansion of the basic Aggregate Expenditure or Aggregate Production model. Currently, the model multiplier has lost its importance from the basic principles of macroeconomics, and has been replaced with the Aggregate Supply or Aggregate Demand based analysis with strong theoretical grounds and significant empirical applications. Furthermore fewer references of the ISLM model observed in top journals shows the limited scope of the discussion in the contemporary theory.
Efforts have been done by (Clarida et al., 1999; and Yun, 1996) to interpret modern research and theory in context of ISLM framework. However their models are based upon the dynamic general equilibrium model, and the transformation their models in ISLM is not fundamental part of their study. The transformation is just operated by policy concerned to provide mean of connecting their outcomes to ISLM framework. Colander and Gamber (2002) presented dynamic general equilibrium models by assuming minor inflexibilities; a transitory negative association can possibly be seen between interest rates and output growth that is be named “IS” curve. The model has been closed by an arrangement of nominal interest rates instead of a traditional LM curve; however that difference is nominal that is associated with standard ISLM model via introducing effectual LM curve into the model that integrates the monetary reaction.
The present situation is almost opposite to that of 1960s where ISLM model has been the focal point of both theoretical and policy debates and discussions. ISLM was at the core of the macroeconomic offering a combination of Keynesian and classical models, which were a key factor in politics and higher education on the economy. If someone had learned ISLM in 1960s, then there was no huge leap between advance macroeconomic work and intermediate studies. Till mid 1970s the ISLM was basics of the graduate economics course. Nowadays the debate about multiple market items and the equilibrium of money market lost the focus to great extent, and ISLM is merely used just as a framework for policy argument, indicating that ISLM continued, but its function has altered significantly.
In the 1960s, the ISLM model was highlighted not only as the starting point for a theoretical macroeconomics, but it was also the basics for the overall empirical macroeconomic and econometric models which was then the focal point of advance macroeconomic policy analysis. In 1960s the application of ISLM was extended to large econometric models which had hundreds of equations however the structure of those equations remained the same. Lawrence (2000) represented the educational use of the ISLM in a very good and revealed how ISLM model could be presented with empirical contents in view of its role in past and assuming the same performance for present situation. It is recommended by him that “systems that are carefully fitted to observed data and capable of generating realistic values are far better for teaching purposes.” The statement is accurate but it doesn’t represent the case of ISLM in the advance analysis as profession is more apprehensive of the economic models on a large scale and the consequence derived are ambiguous while dealing with real situation.
Various researches have efficiently used active macroeconomic models having enhancing comportment articulated in the form of associations that were alike in numerous aspects to conventional models of the ISLM (i.e. Woodford, 2003; Gali and Gertler, 1999; Jeanne, 1998; Walsh, 1998; Woodford, 1995). The major differentiation among the models of the cited studies and traditional ISLM formation is the differentiation of interest rates (real and nominal) and the accompaniment of IS function that contains extra term concerning to the anticipated expenditure in future. In addition of the term provides futuristic feature of expenditure assessments, an aspect that in general consequence in considerably changed dynamic performance in the model, comparative to the traditional kind. Yet, optimizing models have considerable restrictions, among which a most noteworthy is that capital investment is generally considered to be exogenous (McCallum and Nelson; 1999) or absent (Woodford; 1995). Obviously, this limitation is pretty considerable. Besides ignoring the opportunity of investigating matters concerning the growth and capital formation, however it also eliminates the chance of describing endogenously the complementary changeability of investment/speculative and consumption expenditure, a distinction characterized in most of the literature related to realbusiness cycle (Gali; 2001).
From the above discussion it can be concluded that though ISLM model and its existence had remained an important debate for the academic teachers and learners as well as for policy makers and economist. Still ISLM model has essential place in economic activities by covering both fiscal and monetary policy side. Both the government and financial institution of the country used ISLM model in development and frameworks of the different policies to be implemented. Keeping the importance of ISLM model, the existence of ISLM model and test their reliability through planned expenditure for Pakistan from 19802018 to be examined. This empirical attempt may be helpful for an economic agents and policy maker in implementing and making both the monetary and fiscal policies. It will give direction for taking the decision regarding money supply, interest rate, government expenditure, implementation of taxes and propensity change to consumption and possible role in growth.
Developing of Empirical Model and Theoretical Justification
This research paper aims to empirically analyze the reliability of ISLM (Planned expenditure) in case of Pakistan, for that purpose on the basis of above brief theoretical introduction, consider the ISLM model as;
Consumption functions = a + b(Y  T)……… (1)
Investment function: I = c – dr……………….. (2)
The sign of Parameters is (a > 0, 0 < b < 1, c > 0, d > 0,)
As the Planned expenditure is
Y = C + I + G……………………….. (3)
Now by putting the value of equations (1) and (2) in to equation (3), it becomes as
Y = (a + b (Y  T)) + (c  dr) + G………………………. (3.1)
Y (1  b) = a  bT + c  dr + G…………………………… (3.2)
Y = (a bT + c dr + G)/ (1b)……………………………. (3.3)
From equation (3.3) the slope of IS curve will be;
Slope of IS curve = dY/ dr= d/ (1b)…………………………… (3.3a)
d[IS(r)]/d(d) = 1/(1b) < 0…………………(3.3b)
As the real demand for money, that is the function of interest rate (r) and income (y) that is
(M/P) = L(r, Y) = (eYfr)……………………………. (4)
fr = (eYM/P)……………….…………(4.1)
r = (eYM/P)/f……………….………. (4.2)
The slope of LM curve sensitivity to the parameters of interest rate to money demands function, that is;
r = (eYM/P)/f……………………….. (4.2a)
Slope of LM r = dy/dr= e/f ……………………..… (4.2b)
dy/df= e/f^{2}………………………… (4.2c)
Note that as “f” gets larger, money demand becomes increasingly sensitive (flatter) to interest rate. If LM is more sensitive to real interest rates than “∆r” responds less strongly to changes in income to achieve money market equilibrium; with some ∆Y, smaller r needed to return to equilibrium. Mathematically
dy/dM= d/ {1+(e/f)p^{2}f}…………………………………………………(5)
dy/dM = d/ P{(1b)f+de}= 1/ P{e +(1b/d)f} > 0………………………..(5.1)
dy/dG= 1/ {1+(de/f)b} = f/de+(1b)/f} > 0……………………………(5.2)
dy/dT= b/ {1+(de/f)b} = be/de+(1b)/f} < 0………………………….(5.3)
For reliability of planned expenditure, compare equation (3.3) and (4.2), that is;
(eYM/P)/f = (Y( 1b) a+bTcG)/d ……………..………………………..(6)
By
rearranging equation (6)
eY/f +Y(1b)/d = ( abT +c +G)/d +M/Pf …………………………….……….(6.1)
Y( e/f +(1b)/d) =( abT +c +G)/d +M/Pf ……………………………………..(6.2)
Y =1/P* (M/f) / ( e/f +(1b)/d) + ( abT +c +G)/d( e/f
+(1b)/d) ……………..(6.3)
The above equation (6.3) shows that planned expenditure or aggregate demand (Y) is the function of P, exogenous variables M, G, and T. whereas “f”, “e”, “b”, “c” and “d” are the model’s parameters.
Analysis, Findings and Discussions
Statistical Description and Normality of the Data
The descriptive statistics Presentation of data is merely important to shows the average and mean values as well as the statics in a meaningful way. Further, it also helps to understand the main statistics of the variables data and their interpretation in a simple and easy understandable way. Therefore, before going to any empirical and regression analysis we applied Histogram and JarqueBera test for descriptive and normality of the variables data
Table 1. Descriptive and Normality Analysis

Without Model Parameters 
With Model Parameters 
Mean 
7.386310 
4.976458 
Median 
9246.271 
13453.44 
Maximum 
310981.2 
692537.3 
Minimum 
204721.8 
684730.8 
Std. Dev. 
109864.7 
287259.8 
Skewness 
0.298874 
0.113664 
Kurtosis 
3.762476 
3.408359 
JarqueBera 
1.134229 
0.327653 
Probability 
0.567160 
0.843758 
Observations 
36 
36 
Mean value illustrate the center tendency of the whole data in a single value. The results incorporated in table (1) indicates that mean value shows the standard measures of the whole variables data used in this study as well as center distribution of the data in descriptive analysis of both (Model without and with Parameters) models.
Central tendency is mainly measures by median in the data. But median is affected if there are any outliers and unusual values included in the variables data. If there is any sort of outliers or unusual values problem in the data, than median is not a best measurement to represent data in the meaningful and descriptive form. The table (1) clarifies that median is represent the data used in this study in their best form thus rejecting any chances of outliers and unusual values in the data. Further, the median also expresses that the data used in the study is preeminent.
In statistical description the data is further elaborated by measuring the range of dispersion using range of variance and standard deviation. The value of standard deviation falls close to the midrange by comparing the maximum and minimum values assumed by both models as shown in table (1). Skewness shows the probability distribution of the variables as well as error term or random term and it can assume either negative or positive values. Further, Skewness shows and measures symmetry in the data or lack of symmetry. The zero (0) 0f skewness shows that data is perfectly symmetry and if the value of skewness falls between one (1) and zero (0) that indicates that the data set used is symmetry. The simple formula for the skewness that is regressed and considered by most of economic and statistical software is in the form
$\mathrm{Skewness}=\frac{n}{(n1)(n2)}\sum \frac{(Z\stackrel{`}{z})}{{s}_{2}}=\frac{n}{{s}_{2}(n1)(n2)}\mathrm{(5)}$
However, it is too difficult to exactly interpret and Skewness has assumed positive values and very close to zero for both models (without and with Parameters) showing the data used in this study is properly symmetry of both the models. Moreover, Kurtosis measures the degree of distributed peakedness in the data and the value of Kurtosis assumed the distributed peakedness.
The study also regressed JarqueBera test for the normality that either the data is normally distributed as well as normality and correlation between Skewness and kurtosis in the model. The general form of JarqueBera test in the regression analysis is
$\mathrm{JarqueBera}=\frac{\mathrm{nk+1}}{6}({s}_{2}\mathrm{1/4}(\mathrm{kt}3)\mathrm{(8)}$
The results of JarqueBera test and their probability values captures normal distribution indicating that variable data is normally distributed and didn’t show any serious problem.
Integration of Sationarity
Shrestha and Chowdhury (2005) concluded that a conspicuous that a negligible alteration in the data specification as well as in assumptions may effectively manipulate and change the outcomes and sometimes nonstationarity that alters the significant results of the variables to spurious, insignificant and biased. Consequently, if the variables data shows their stationarity but still there remains a risk of misspecification. Nonstationarity has keen suspicious for researchers and investigator while doing research on time series data, as this investigation is also doing on time series analysis so been tested for unit root and the findings showing the rejection of null hypothesis at different level of integration specified below.
Table 2. Stationarity Level Results
Variables 
Acronyms 
Without Model Parameters 
With Model Parameters 

At I(0) 
At 1(1) 
Critical Values 
At I(0) 
At 1(1) 
CriticalValues 

Planned Expenditure 
Y 
1.889012 
4.945417* 
2.945842 
 
 
 
Government Expenditure 
G 
3.784680* 
6.724531* 
2.945842 
3.477860* 
5.678959* 
2.951125 
Taxes 
T 
4.983063* 
7.736903* 
2.945842 
2.213937 
4.475638* 
2.951125 
Money Supply 
M 
1.744261 
4.108121* 
2.945842 
1.676608 
3.821911* 
2.951125 
Price Level Index 
P 
2.127776 
4.032559* 
2.945842 
3.890167* 
6.701775* 
2.951125 
Marginal Propensity to Consume 
MPC 
 
 
 
1.630152 
3.594049 
2.951125 
Autonomous Investment 
I_{0} 
 
 
 
3.578214 
5.293643 
2.951125 
Investment Sensitivity to r 
Ir 
 
 
 
1.897352 
4.708319 
2.951125 
(*) shows rejection of null hypothesis at 5%
1.1. Modeling and Interpretation of Regression Results
Inspect the reliability and existence of ISLM model, the ARDL approach as technique of regression is applying based on the findings of the stationarity outcomes of table (2). The study regressed two model, one is the basic model without including the variables parameters; i.e.
Y= f (M, G, T, P)……………………………………. (12)
Equation (12) shows the theoretical model in which “Y” is the planned expenditure “M” is the money supply, “G” is the government expenditure, “T” is the taxes and “P” is the price level. The econometric model (without model Parameters) is
$${Y}_{t}={\alpha}_{0}+{\alpha}_{1}{G}_{\mathrm{t}}+{\alpha}_{2}{T}_{\mathrm{t}}+{\alpha}_{3}{M}_{\mathrm{t}}+{\alpha}_{4}{P}_{\mathrm{t}}+{\mu}_{\mathrm{t}}\mathrm{(13)}$$
The sign of coefficient estimator that shows the impact and relation of regressor with dependent variables be;
$${\alpha}_{1}>0,{\alpha}_{2}>0,{\alpha}_{3}>0,{\alpha}_{4}<0,$$
The ARDL model (without model Parameters) that be regressed for variables data to empirically examined the reliability of planned expenditure from ISLM model
$${Y}_{t}={\alpha}_{0}+{\alpha}_{1}{G}_{\mathrm{t}}+{\alpha}_{2}{T}_{\mathrm{t}}+{\alpha}_{3}{M}_{\mathrm{t}}+{\alpha}_{4}{P}_{\mathrm{t}}+{\sum}_{\mathrm{ti}}^{\mathrm{i=n}}{\beta}_{0}\u2206{y}_{\mathrm{ti}}+{\sum}_{\mathrm{ti}}^{\mathrm{i=n}}{a}_{1}\u2206{G}_{\mathrm{ti}}+{\sum}_{\mathrm{ti}}^{\mathrm{i=n}}{a}_{2}\u2206{T}_{\mathrm{ti}}+{\sum}_{\mathrm{ti}}^{\mathrm{i=n}}{a}_{3}\u2206{M}_{\mathrm{ti}}+{\sum}_{\mathrm{ti}}^{\mathrm{i=n}}{a}_{4}\u2206{P}_{\mathrm{ti}}+{\eta}_{\mathrm{t}}\mathrm{(14)}$$
The second model that is regressed includes the model parameters as assumed by final model (6.3) after deriving. The theoretical and econometric form of that model is
Y= f (M*e/f, G, T, P, c, I_{0}, I_{0}*dr)
The econometric model is
$${Y}_{t}={\beta}_{0}+{\beta}_{1}{M}_{\mathrm{t}}*\mathrm{(}\mathrm{e/f}\mathrm{)}+{\beta}_{2}{G}_{t}+{\beta}_{3}{T}_{\mathrm{t}}+{\beta}_{4}{P}_{\mathrm{t}}+{\beta}_{5}{c}_{\mathrm{t}}+{\beta}_{6}{I}_{\mathrm{0t}}+{\beta}_{6}I\mathrm{*}\mathrm{(}\mathrm{dr}\mathrm{)}+{\mu}_{\mathrm{t}}\mathrm{(15)}$$
In the above econometric model Planned expenditure (Y) is the dependent variable, whereas, independent variables is money supply “M” that is the function of money demand to income (e) and interest rate (f), government expenditure (G), Taxes (T), Marginal propensity to consume (c), autonomous Investment (I_{0}) and Investment that is function of interest rate (dr), regressed model is
$${Y}_{t}={\beta}_{0}+{\beta}_{1}{M}_{\mathrm{t}}*\mathrm{(}\mathrm{e/f}\mathrm{)}+{\beta}_{2}{G}_{t}+{\beta}_{3}{T}_{\mathrm{t}}+{\beta}_{4}{P}_{\mathrm{t}}+{\beta}_{5}{c}_{\mathrm{t}}+{\beta}_{6}{I}_{\mathrm{0t}}+{\beta}_{6}I\mathrm{*}\mathrm{(}\mathrm{dr}\mathrm{)}+{\sum}_{\mathrm{ti}}^{\mathrm{i=n}}{\alpha}_{0}\u2206{y}_{\mathrm{ti}}+{\sum}_{\mathrm{ti}}^{\mathrm{i=n}}{\beta}_{1}\u2206{M}_{\mathrm{ti}}*\mathrm{(}\mathrm{e/f}\mathrm{)}+{\sum}_{\mathrm{ti}}^{\mathrm{i=n}}{\beta}_{2}\u2206{G}_{\mathrm{ti}}+{\sum}_{\mathrm{ti}}^{\mathrm{i=n}}{\beta}_{3}\u2206{T}_{\mathrm{ti}}+{\sum}_{\mathrm{ti}}^{\mathrm{i=n}}{\beta}_{4}\u2206{P}_{\mathrm{ti}}+{\sum}_{\mathrm{ti}}^{\mathrm{i=n}}{\beta}_{5}\u2206{c}_{\mathrm{ti}}+{\sum}_{\mathrm{ti}}^{\mathrm{i=n}}{\beta}_{6}\u2206{I}_{\mathrm{0ti}}+{\sum}_{\mathrm{ti}}^{\mathrm{i=n}}{\beta}_{7}\u2206I*{\mathrm{(dr)}}_{\mathrm{ti}}+{\eta}_{\mathrm{t}}\mathrm{(16)}$$
Both the models were regressed, in first the basic variables without including model parameters (14) and in the second model with models parameters (16) to empirically examine the reliability of planned expenditure deriving from ISLM model. ARDL acquire satisfactory number of lags for the regression. The automatic lag length criteria following Akaike Information Criteria (AIC) for both the models (without and with model parameters) and the models were regressed a number of times testing at different level is selected. The optimal lag number that gives best results as well as shows minimum number of lag length was at (1, 0, 1, 1, 0) for the model without including parameters and (1, 1, 0, 0, 1, 0, 1, 1) for the models that include parameters.
The first model with basic variables that doesn’t contains model parameters as derived from ISLM model in equation (14); the performance of the model is noteworthy as the prob. Fstat is highly significant. Moreover, R^{2} value enlightens the satisfactory variation between variables included in the model.
Table 3. Analysis of the Variables (Without Model Parameters)
Variables 

Coefficient 
Sd. Error 
tStat 
P. values 

Constant 
C 
0.412383 
0.184085 
2.240169 
0.0372 

Government Expenditure 
G 
0.329815 
0.474459 
0.695139 
0.4954 

Government Expenditure 
G_{(1)} 
0.273602 
0.098712 
2.772041 
0.0121 

Taxes 
T 
0.386571 
0.098110 
3.940161 
0.0009 

Money Supply 
M 
0.275001 
0.251972 
1.091395 
0.2887 

Money Supply 
M_{(1)} 
0.313073 
0.258425 
1.211463 
0.2406 

Price Level 
P 
0.104275 
0.053851 
1.936391 
0.0678 

Price Level 
P_{(1)} 
0.134815 
0.069452 
1.941109 
0.0672 

Lag Value of Y 
Y_{(1)} 
0.572877 
0.142027 
4.033582 
0.0007 

R^{2} 
0.963128 
DW 
1.797640 

Ad. R^{2 } 
0.936248 
Fstat. Prob 
0.000000 

The result of explanatory variables to empirically test the reliability of planned expenditure via ISLM model incorporated in table (3) indicates that both taxes and price level is significant. The coefficient sign of the estimator is also reliable to model and shows that increase in taxes and decrease in price level (inflation) is positive role in planned expenditure in case of Pakistan. This gives the direction for both fiscal and monetary policy as to take decision regarding to taxes is the responsibility of government sector and to control price level (inflation) of the central bank of the country. Further, the results also indicates that lagged value of planned expenditure is significant showing that previous year Planned expenditure also a considerable effect in existing year Planned expenditure.
The results integrated in table (3) also show that both monetary policy and government expenditure is insignificant. It is an evident from last two decades’ that economist, analyst, researchers and policy maker’s criticizing the excessive government expenditure and unnecessary increase in money supply. The empirical results of this study also confirming that government current expenditure and disproportionate money supply cannot play any affirmative role and it should be controlled.
Table 4. Regression of Variables (With Model Parameters)
Variables 
Acronyms 
Coefficient 
Sd. Error 
tStat 
P. values 

Constant 
C 
0.135856 
0.561738 
0.241849 
0.8107 

Government Expenditure 
G 
0.416643 
0.245234 
1.698964 
0.1028 

Government Expenditure 
G_{(1)} 
0.409626 
0.196475 
2.084854 
0.0484 

Taxes 
T 
0.741352 
0.169622 
4.370588 
0.0000 

Taxes 
T_{(1)} 
0.758042 
0.175283 
4.324651 
0.0000 

Money Supply 
M 
0.511138 
0.197078 
2.593581 
0.0149 

Money Supply 
M_{(1)} 
0.733162 
0.239438 
3.062012 
0.0048 

Price Level 
P 
0.187078 
0.091206 
2.051139 
0.0597 

Price Level 
P_{(1)} 
0.172943 
0.095586 
1.809291 
0.0835 

Marginal Propensity to Consume 
MPC 
0.174868 
0.052023 
3.361347 
0.0030 

Marginal Propensity to Consume 
MPC_{(1)} 
0.377561 
0.087659 
4.307132 
0.0000 

Autonomous Investment 
I_{0} 
0.426715 
0.138307 
3.085258 
0.0052 

Autonomous Investment 
I_{0(1)} 
0.402359 
0.138179 
2.911861 
0.0179 

Investment Sensitivity to r 
Ir 
0.491914 
0.219897 
2.237017 
0.0419 

Investment Sensitivity to r 
Ir_{(1)} 
0.679691 
0.280122 
2.426410 
0.0343 

Lag Value of Y 
Y_{(1)} 
0.818948 
0.190999 
4.287693 
0.0000 

Rsquared 
0.939234 
DurbinWatson stat 
1.961687 

Adjusted Rsquared 
0.918867 
Prob(Fstatistic) 
0.000000 

The basic model with its parameters is regressed and the model is reliable and consistent at the prob. Fstat value is vastly considerable. The results of regresor variables, money supply after multiplying with money multiplier (e/f) is positive and significant indicates that money supply successfully bring an increase of fifty one to seventy three percent. The estimator value marginal propensity to consume is also positive and significant. That indicates that increase in income will increase the propensity to consume that leads to positive impact of MPC on planned expenditure. The induced investment is also positive impact on planned expenditure and the results also indicating that planned expenditure indirectly have inverse relation with interest rate. Interest rate directly affect (increase or decrease) investment that will then affect the planned expenditure. The taxes and price level has significant effect in both models while government expenditure remains insignificant in both models (without and with model parameters).
The study conclude from the empirical results of the both (without and with model parameters) models that the model that is regressed after including the model parameters is more satisfactory as compared to model without parameters. So, the later model that include parameters significantly explains and prove the reliability of ISLM via planned expenditure
Conclusion
The history has witnessed and observed that ISLM model had remained the important tool to design fiscal and monetary policy. Besides that it has also an imperative place in macroeconomics. It will not worth saying that without ISLM model macroeconomic be incomplete. Though ISLM model has a complex framework but still it is more focused model for economist, researchers, policy makers and academic readers.
This study is an empirical attempt of investigating the reliability of ISLM model in case of Pakistan for the period of 19802018. The study regressed two models; in first model (without model parameters) is the basic model. Again the same model is regressed by including the model parameters. From the Comparisons of both models it is evident that model with parameters performs well as compared to basic model. The results also enlightening the existence and reliability of ISLM model in case of Pakistan empirically. Moreover, the study also concluded that government expenditure remains insignificant and should be minimized and controlled. The study also concluded that decision regarding to money supply and investment must be indigenize rather than exogenous.