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\title{The Impact of Institutional Quality on Monetary Policy in Pakistan}
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\begin{document}

             \author[1]{Kifayat  Ullah}

             \author[2]{Abdul  Wajid}

             \author[3]{Fazal Ali  Khan}

             \affil[1]{  Karakoram International University Gilgit, Pakistan}

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\date{\small \em Received: 12 December 2015 Accepted: 31 December 2015 Published: 15 January 2016}

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\begin{abstract}
        


This study seeks to examine whether the monetary management in Pakistan is conducted under the framework advocated by the Taylor rule. We investigate this question after modifying the Taylor rule to control for the influence of institutional quality on monetary policy. Even after controlling for institutional quality, we find no evidence that the central bank makes use of the Taylor framework in devising monetary policy. Our results also suggest that by and large the focus of the monetary policy has been on containing inflation.

\end{abstract}


\keywords{institutional quality, monetary management, central bank, taylor rule, ARDL method.}

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\let\tabcellsep& 	 	 		 
\section[{I. Introduction}]{I. Introduction}\par
growing body of literature now maintains that sound institutions 1 are essential for sustainable economic growth. Institutional quality can effect overall economic growth by providing the underlying environment for economic activity to flourish. Recent developments in institutional economics suggest that besides affecting the overall economic activity, institutions can also affect the conduct, transmission, as well as the cyclical properties of monetary policy 2 . The primary goal of the monetary policy is to stabilize prices and to promote sustainable growth. There is debate in literature whether the monetary policy should be conducted based on some predetermined rule or should the policy maker enjoy discretion in the conduct of monetary policy. Though the debate is not fully settled but literature seems to favor rules over discretion 3 . Barring some exception, the monetary policy has not been able to rein in inflation or stabilize output in Pakistan \hyperref[b13]{(Qayyum, 2008)}. This makes it important to thoroughly examine how the monetary policy is conducted, whether the authority follows a rule or the discretion is allowed to influence the monetary policy. This type of investigation has been conducted for Pakistan using the standard Taylor rule framework 4   1 North (1990) defines institutions as "the humanly devised constraints that shape human interaction". These constraints are further divided into formal rules (constitutions, laws, rules etc.) and informal constraints (norms, conventions, self-imposed code of conducts etc.). 2 See for example \hyperref[b1]{Huang and Wei (2006)},  {\ref Calderon et al. (2010)}, Choudhary et al.  {\ref (2010)}, Duncan (2011) etc. 3 See for example taylor  {\ref (1993,} {\ref 1997,} {\ref 2007)}, Clarida et al.  {\ref (1999)},  {\ref Barro and Gordon (1983)},  {\ref Sevenson (1996)} etc. 4 Studies include \hyperref[b9]{Malik and Ahmed (2007)}, \hyperref[b8]{Malik (2007)}, \hyperref[b3]{Iqbal (2009)},  {\ref Malik} and Ahmed (2011) etc. , Author ? ? ?: Karakoram International University Gilgit, Pakistan. e-mails: kifayat@kiu.edu.pk, wajidiiie@gmail.com, fazal@rspn.org.pk however the standard Taylor rule does not account for the influence of institutions on monetary policy. This study examines the conduct of monetary policy by augmenting the Taylor rule to account for the influence of institutional quality on monetary policy. This has not been done so far in case of Pakistan. 
\section[{a) Objectives of the Study}]{a) Objectives of the Study}\par
The objectives of this study are: 1. To estimate the monetary policy reaction function for Pakistan by incorporating institutional quality variable in the Taylor rule framework. 2. To examine whether the monetary policy is procyclical or counter cyclical by incorporating institutional quality in the traditional framework. 
\section[{II. Review of Literature}]{II. Review of Literature}\par
The neo-institutional economist 5 a) Institutional Quality and Monetary Policy stress that institutional factors play an important role in the process of economic growth. Using state activism, property rights and corporate governance as a measure of institutional quality, \hyperref[b2]{Huang et al. (2004)} examine the relationship between institutional quality and growth by disaggregating foreign direct investment into investment that supports local entrepreneurship and investment that replaces local entrepreneurship. They find that institutional quality influence foreign direct investment. They further find that foreign direct investment that supports local entrepreneurship translates into sustainable growth.\par
Traditionally, monetary policy and institutional quality have been treated as independent disciplines. Initially, studies were conducted in a principal agent framework to assess the potential negative impact of corruption among government officials, who secured bribes from private citizens interested in government produced goods, on the development of a nation. These studies pointed at identifying the motivating factors among the agents to be honest-the honest the official, the higher the payoff for the nation. \hyperref[b1]{Huang and Wei (2006)} model the role of institutions in the effectiveness of monetary policy. Using corruption as a proxy of institutional quality they show that institutional quality has implications for the conduct and transmission of monetary policy. They argue that weak Institutions can directly affect monetary policy by weakening the monetary authority in terms of autonomy and by eroding the credibility of the central bank. 
\section[{b) Institutional Quality and Cyclicality of Monetary Policy}]{b) Institutional Quality and Cyclicality of Monetary Policy}\par
Pro-cyclicality of macroeconomic policy is often described as a problem faced by developing countries \hyperref[b0]{(Frankel, 2010)}. Pro-cyclical monetary policy is harmful because it reinforces the business cycle  {\ref (Kaminskyet al., 2005)}. Pro-cyclical monetary policy harms the economic activity because during the periods of rapid economic growth (booms) expansionary monetary policy is pursued leading to overheating of the economy and thereby exerting inflationary pressure while during periods of slower economic growth (busts) contractionary monetary policy is pursued further pushing the economy towards recession. 
\section[{c) Taylor Rules: Studies for Pakistan}]{c) Taylor Rules: Studies for Pakistan}\par
Studies that have estimated Taylor rule for Pakistan include \hyperref[b9]{(Malik and Ahmed, 2007;}\hyperref[b8]{Malik, 2007;}\hyperref[b4]{Islam, 2009;}\hyperref[b3]{Iqbal, 2009;}\hyperref[b14]{Sulaiman et al., 2011;}\hyperref[b10]{Malik and Ahmed, 2011)}. All these studies find that the central bank has not been following Taylor rule for the conduct of monetary policy. \hyperref[b8]{Malik (2007)} and Islam (2009) using counter factual simulations argue that economic performance could have been improved, had the central bank followed Taylor rule. \hyperref[b9]{Malik and Ahmed (2007)} argue that the reason for non-observance of the rule could be that besides the stability of output and inflation the central bank follows other objectives as well e.g. exchange rate stability. They further argue Taylor rule does not hold for Pakistan even after adjusting upwards the parameters of inflation and output gap.\par
All of these studies have failed to find classical Taylor type relationship in the conduct of monetary policy of Pakistan. After failing to observe the classical relationship, they have modified the Taylor type rule by augmenting the Taylor rule with other objectives that the monetary authority may take into consideration. These objectives may include interest rate smoothening, exchange rate management, reduction of trade deficits, government borrowings etc. Most of the studies find that the central bank follows policy of interest rate smoothening while some studies find exchange rate stabilization also significant. 
\section[{III. Theoretical Framework and Empirical Strategy a) Theoretical Framework}]{III. Theoretical Framework and Empirical Strategy a) Theoretical Framework}\par
We seek to examine the effect of institutional quality on monetary policy. Based on recent literature, our premise is that institutional quality affects the outcomes of monetary policy. One way to characterize the monetary policy of a country is through the Taylor type rule. The Taylor rule 6 recommends increasing the policy rate of interest when inflation is above target and lowering interest rate when recession appears to be more of a threat. The Taylor rule works on the assumption that the interest rate channel of monetary policy is very effective \hyperref[b15]{(Taylor, 1993)}. Recently, there has been a surge in the literature to incorporate institutional quality into monetary policy framework 7 . Few studies which have examined Taylor rule for Pakistan conclude that the central bank does not follow the Taylor rule 8 b) Empirical Strategy . Given the view that institutions influence the conduct of monetary policy, it is important to revisit the Taylor rule for Pakistan while controlling for institutional quality.\par
We use the Autoregressive Distributive Lag (ARDL) method for estimation of our model. The reasons for using this model are manifold. First, we suspect that the variables used in the analysis are integrated, potentially of different order of integration. Second, if the variables are integrated of different order then the traditional co-integration methods like Engel Granger (1987) and \hyperref[b5]{Johansen (1991}\hyperref[b6]{Johansen ( , 1995) )} cointegration techniques are not applicable. Third, as pointed out by Ruth (2004) and others 9   i. ARDL Method to Co-integration , Taylor rule can be better characterized by accounting for the interest rate smoothening. This can be done in a co-integration framework using the error correction term. Taking into consideration all the above mentioned benefits of ARDL, we consider it the appropriate technique for estimating our model. The ARDL estimation technique is described below.\par
In our case, the general form of the ARDL equation can be written as follows:\par
Where i=1,2,3?, ? 0 is the intercept, is the trend, c 0 ,?,? 1 ,? 2 ,?,? are parameters.?? is the interest rate, ?????? is the output gap, ????ð??"ð??" is the inflation, ???? is the institutional quality index and is the difference operator i.e. Î?"y t =y t ?y t?1 . In this equation, the variables at levels define the long run relationship and variables in difference form define the short run dynamics. The maximum lag will be set to three years and final model will be selected on basis of Schwarz Bayesian Criterion (SBC).\par
ii. Co-integration Test The Bound testing procedure in ARDL is used to test for existence of co-integration in the model. The bound testing can be carried out for equation 5.7 by testing the following hypothesis: H 0 :? 1 =? 2 =? 3 =? 4 =0???????? (5.8) If H 0 is rejected then we conclude that there is a long run relationship among the variables. According to \hyperref[b11]{Pesaran (1999)} the asymptotic distribution of the F-Statistics are non-standard regardless of the degree of integration of the variables when the null hypothesis is of no co-integration. This depends upon whether the variables included in the ARDL model are I(0) or I(1), the number of regressors, whether the ARDL model contains an intercept and/or a trend, and the sample size. Two sets of critical F-values, representing the lower bound and the upper bound, have been suggested by \hyperref[b12]{Pesaran et al. (2001)}. If the statistic is higher than the upper bound, the null hypothesis of no co-integration can be rejected and the next step is to estimate the ARDL based ECM where the short-run and long-run elasticity's may be determined.\par
iii. Long Run Relationship After confirmation of existence of long run relationship, long run coefficients from equation 5.7 can be extracted by normalizing the coefficients of the variables with respect to coefficient of dependent variable. In our case this will be done as follows.? 1 i t?1 +? 2 Gap t?1 +? 3 inf t?1 +? 4 IQ t?1 =0?????? (5.9) ?1it?1 =??2Gapt?1 ??3inft?1 ??4IQt?1 i t?1 =? 1 Gap t?1 +? 2 inf t?1 +? 3 IQ t?1 ??????? (5.10) 
\section[{Where}]{Where}\par
, and are the long run coefficients. 
\section[{iv. Dynamic Short Run Relationship and Error Correction}]{iv. Dynamic Short Run Relationship and Error Correction}\par
The dynamic short run relationship of equation 5.7 can be generated by replacing the long run equation 5.9 by the error correction term. The final dynamic short run equation with error correction can be can be specified as follows:\par
The coefficient of error correction term (ECM t?1 ) ? represents the speed of adjustment. It tells us the magnitude of the error correction accounted for in the current period. 
\section[{c) Data, Variable Definition and Construction}]{c) Data, Variable Definition and Construction} 
\section[{i. Data Span}]{i. Data Span}\par
We have used annual time series data from 1984 to 2011, drawn from International Financial Statistics (IFS) for Pakistan. As the focus of this study is on the impact of institutions on monetary policy, therefore we use annual frequency data.\par
ii. Output Gap Output Gap is defined as the difference between the actual output produced within the economy and the potential output. One of the techniques widely used is the Hodrick -Prescott (HP) Filter. Assumingy t is the series for which we want to extract the long run trend component, the equation for the HP filter can be written as follows Where s is the smoothed series or long run series of yt, derived by minimizing the variance of y t around s t . is the smoothness parameter and it controls the smoothness of the series. For annual series is generally taken as 100.\par
iii. Inflation\par
To capture the aggregate level of prices, we have used GDP deflator. Inflation is computed as:\par
Where subscript shows the current period and t?1shows the previous period. Data for GDP Deflator is also drawn from the International Financial Statistics (IFS). 
\section[{iv. Rate of Interest}]{iv. Rate of Interest}\par
The policy interest rate indicates the stance of monetary policy. The most widely used measure of institutional quality comes from the international country risk guide (ICRG) dataset compiled by experts at Political Risk Services (PSR) Group. ICRG Political dataset consist of 12 categories i.e. Government Stability, Socioeconomic Conditions, Investment Profile, Internal Conflict, External Conflict, Corruption, Military in Politics, Religious Tensions, Law and Order, Ethnic Tensions, Democratic Accountability and Bureaucracy Quality. We have selected six indicators from ICRG data set for our analysis because these host the potential to influence monetary policy in one or the other way. 
\section[{d) Principle Component Analysis}]{d) Principle Component Analysis}\par
Principle Component Analysis (PCA) is a variable deducting method 11   Where, X j is the variable under consideration, P i is the i th principal component; a ji is the factor loading of the j th variable on the i th principal component. The principal components are given by: . We use the normalized data from ICRG dataset to compute composite index of institutional quality using PCA. As in PCA, each variable can be written as linear combination of PCs, in our case this can be done using following formula as: 10 Definition of the components of institutional quality are from ICRG methodology document available at http://www.prsgroup.com/PDFS/ icrgmethodology.pdf. 11  Where ? i is the eigen-value associated with the principle component P i . The composite index of institutional quality can be generated using the principal components generated by equation 5.5 using the following formula as:\par
Where, E i are the eigen-values and is the index of institutional quality. Using annual data from ICRG dataset, institutional quality index is constructed by PCA. We have retained components with eigen-value ? 1.0, identified using scree plots, and which account for more than 60 \% of variance in the composite index. 
\section[{e) Test of Stationarity}]{e) Test of Stationarity}\par
Though, the ARDL method doesn't require to test for the order of integration of the data series, but Ouattara (2006) argues that F-statistics for bounds test provided by \hyperref[b12]{Pesaran et al. (2001)} remains valid if and only if the variables used in the model are either I(0) or I(1) -any data series in the model should not be integrated of higher order, therefore we employ the Augmented Dickey Fuller and Phillips-Perron methods to test the order of integration of the data series. 
\section[{Year ( )}]{Year ( )}\par
IV. Results and Discussions a) Descriptive Statistics Table \hyperref[tab_2]{4}.1 summarizes the Descriptive statistics of the variables used in the analysis.     As mentioned earlier, to use the ARDL model the underlying data series should at most be integrated of order one (difference stationary). Table \hyperref[tab_2]{4}.2 shows that the variables are either integrated of order zero or integrated of order one. Therefore, we can now safely proceed with estimation of our model, using the ARDL method. 
\section[{Year ( ) c) Test of Stationarity}]{Year ( ) c) Test of Stationarity} 
\section[{d) Estimation Results}]{d) Estimation Results}\par
We have used Schwartz Bayesian criterion for selection of our model. Table \hyperref[tab_2]{4}.3 presents results of the selected equation which will be tested for existence of the long run relationship. The test results indicate that a co-integrating relationship exists among the variables as the computed F-statistics lies outside the inconclusive range and is greater than the upper critical bound. 
\section[{ii. Breusch-Godfrey Serial correlation LM test}]{ii. Breusch-Godfrey Serial correlation LM test}\par
For the results to be valid there should be no autocorrelation in the residuals of the regression equation. Table \hyperref[tab_2]{4}.5 summarizes the results of Breusch-Godfrey Serial correlation LM test. The long run equation results suggest that the relationship between the institutional quality and the rate of interest is insignificant, though positive. This result indicates that in the long run, rate of interest does not respond to changes in institutional quality. Table \hyperref[tab_2]{4}.7 shows that there is a negative and highly significant relationship between rate of interest and output gap. This result indicates that when output gap increases, rate of interest is decreased by the monetary authority. This also shows that the monetary policy is pro-cyclical, often described as a problem faced by developing economies. The relationship between inflation and interest rate is positive and highly significant and more than one for one in terms of magnitude. In the long run this relationship is in accordance with the Taylor rule. 
\section[{f) Dynamic Short Run Relationship}]{f) Dynamic Short Run Relationship}\par
Table \hyperref[tab_2]{4}.8 summarizes the results of short run dynamic equation.  High R-Square value suggests that the model has a high explanatory power. Table \hyperref[tab_2]{4}.8 shows that institutional quality effects interest rate for sufficiently longer period. Contemporaneous impact of institutional quality on interest rate is negative but is positive in the next two periods. This does not yield a clear picture regarding the nature of relationship between institutional quality and interest rate. The results further indicate that there is a positive relationship between the rate of interest and inflation in the contemporaneous period but there is a negative relationship between these two variables at a year lag. This means that initially the monetary authority reacts to the change in inflation by increasing the rate of interest but down the road this relationship turns around. In the current period, changes in output gap do not affect interest rate as the coefficient of output gap is insignificant. However, the interest rate reacts positively to changes in the output gap at a year's lag. The coefficient of error correction term indicates that three fourth of the error is accounted for in the current period. This shows that the central bank follows a policy of interest rate smoothening, changing the policy rate gradually to achieve the price stability goal. 
\section[{V. Conclusion}]{V. Conclusion}\par
The results of the study indicate that effect of institutional quality on monetary policy is not clear. Institutional quality affects the monetary policy in the short run but the direction of the impact changes from lag to lag, while in the long run impact of institutional quality is insignificant. The results also suggest that the central bank has been putting more weight on inflation stabilization rather than output stabilization. It could be due to the fact that inflation during the period has, by and large remained at a relatively higher level. Moreover, during most of the period covered by the study's data span the country has been borrowing under the IMF structural adjustment programs. Almost all of these programs required containing the fiscal deficit. This again required a focus upon inflation. We found that the central bank has not been following Taylor rule in setting the policy rate of interest even after controlling for the institutional quality. This result is at variance with the findings for the developing countries in a cross country setting (Duncan, 2011). The reason could be that the institutional quality is poorer in Pakistan than typically observed in the countries included in the said study. Our results also suggest that the central bank follows an interest rates smoothening policy as the error correction term is highly significant and about three forth of the error is accounted for in the current period. Our results confirm that monetary policy of Pakistan is highly procyclical as suggested in the literature. We argued that the pro-cyclicality could be due to the low institutional quality.\begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-2.png}
\caption{\label{fig_0}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-3.png}
\caption{\label{fig_1}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{6}\includegraphics[]{image-4.png}
\caption{\label{fig_2}Figure 6 .}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{6}\includegraphics[]{image-5.png}
\caption{\label{fig_3}Figure 6 .}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-6.png}
\caption{\label{fig_4}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-7.png}
\caption{\label{figure7}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-8.png}
\caption{\label{figure8}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-9.png}
\caption{\label{figure9}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.85\textwidth}}
10\end{longtable} \par
  {\small\itshape [Note: v. Institutional Quality]} 
\caption{\label{tab_0}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{41} \par 
\begin{longtable}{P{0.22543478260869568\textwidth}P{0.085\textwidth}P{0.08869565217391304\textwidth}P{0.07760869565217392\textwidth}P{0.07760869565217392\textwidth}P{0.09608695652173913\textwidth}P{0.09978260869565217\textwidth}P{0.09978260869565217\textwidth}}
Variable\tabcellsep Mean\tabcellsep Median\tabcellsep Max\tabcellsep Min\tabcellsep Std. Dev.\tabcellsep Jarque-Bera\tabcellsep Probability\\
Call Money\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
Rate\tabcellsep 8.57\tabcellsep 8.53\tabcellsep 12.47\tabcellsep 2.13\tabcellsep 2.74\tabcellsep 1.04\tabcellsep 0.59\\
Output Gap\tabcellsep -0.001\tabcellsep -0.95\tabcellsep 9.28\tabcellsep -5.78\tabcellsep 4.02\tabcellsep 1.54\tabcellsep 0.46\\
Inflation\tabcellsep 3.73\tabcellsep 3.50\tabcellsep 8.03\tabcellsep 1.06\tabcellsep 1.69\tabcellsep 1.23\tabcellsep 0.53\\
Institutional\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
Quality\tabcellsep 45.14\tabcellsep 43.23\tabcellsep 77.33\tabcellsep 13.37\tabcellsep 17.33\tabcellsep 0.91\tabcellsep 0.64\end{longtable} \par
 
\caption{\label{tab_1}Table 4 . 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{4} \par 
\begin{longtable}{}
\end{longtable} \par
 
\caption{\label{tab_2}Table 4 .}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{4} \par 
\begin{longtable}{}
\end{longtable} \par
 
\caption{\label{tab_3}Table 4 .}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{42} \par 
\begin{longtable}{P{0.19377203290246767\textwidth}P{0.08589894242068155\textwidth}P{0.08589894242068155\textwidth}P{0.0809048178613396\textwidth}P{0.1308460634547591\textwidth}P{0.10887191539365453\textwidth}P{0.09388954171562867\textwidth}P{0.0699177438307873\textwidth}}
\tabcellsep \tabcellsep \tabcellsep ADF Test\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep Order of\\
\tabcellsep \tabcellsep At Level\tabcellsep \tabcellsep \tabcellsep At First Difference\tabcellsep \tabcellsep Integration\\
\tabcellsep No\tabcellsep \tabcellsep \tabcellsep No\tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep Intercept,\tabcellsep Intercept\tabcellsep Intercept,\tabcellsep Intercept,\tabcellsep Intercept\tabcellsep Intercept,\tabcellsep \\
Variable\tabcellsep No Trend\tabcellsep Only\tabcellsep Trend\tabcellsep No Trend\tabcellsep Only\tabcellsep Trend\tabcellsep \\
Call Money\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
Rate\tabcellsep -0.09\tabcellsep -1.50\tabcellsep -1.71\tabcellsep -4.68***\tabcellsep -4.62***\tabcellsep -4.06***\tabcellsep I(1)\\
Output Gap\tabcellsep -3.01***\tabcellsep -2.95*\tabcellsep -2.82\tabcellsep -5.35***\tabcellsep -5.25***\tabcellsep -5.27***\tabcellsep I(0)\\
Inflation\tabcellsep -0.15\tabcellsep -2.87*\tabcellsep -3.12\tabcellsep -7.83***\tabcellsep -7.72***\tabcellsep -5.56***\tabcellsep I(0)\\
Institutional\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
Quality\tabcellsep -0.40\tabcellsep -1.68\tabcellsep -5.40***\tabcellsep -4.62***\tabcellsep -4.55***\tabcellsep -4.50***\tabcellsep I(1)\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{PP Test}\tabcellsep \tabcellsep \\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep Order of\\
\tabcellsep \tabcellsep At Level\tabcellsep \tabcellsep \multicolumn{3}{l}{A t First Difference}\tabcellsep Integration\\
\tabcellsep No\tabcellsep \tabcellsep \tabcellsep No\tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep Intercept,\tabcellsep Intercept\tabcellsep Intercept,\tabcellsep Intercept,\tabcellsep Intercept\tabcellsep Intercept,\tabcellsep \\
Variable\tabcellsep No Trend\tabcellsep Only\tabcellsep Trend\tabcellsep No Trend\tabcellsep Only\tabcellsep Trend\tabcellsep \\
Call Money\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
Rate\tabcellsep -0.12\tabcellsep -1.77\tabcellsep -1.94\tabcellsep -4.68***\tabcellsep -4.62***\tabcellsep -4.60***\tabcellsep I(1)\\
Output Gap\tabcellsep -3.01***\tabcellsep -2.95*\tabcellsep -2.75\tabcellsep -7.18***\tabcellsep -7.00***\tabcellsep -7.98***\tabcellsep I(0)\\
Inflation\tabcellsep -0.51\tabcellsep -2.78*\tabcellsep -3.15\tabcellsep -8.54***\tabcellsep -9.36***\tabcellsep -9.24***\tabcellsep I(0)\\
Institutional\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
Quality\tabcellsep -0.40\tabcellsep -1.86\tabcellsep -2.75\tabcellsep -4.62***\tabcellsep -4.55***\tabcellsep -4.49***\tabcellsep I(1)\\
\multicolumn{5}{l}{Note: ***, ** and * shows significance at 1\%, 5\% and 10\% respectively}\tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_4}Table 4 . 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{43} \par 
\begin{longtable}{P{0.4764197530864197\textwidth}P{0.0419753086419753\textwidth}P{0.03358024691358025\textwidth}P{0.26024691358024693\textwidth}P{0.03777777777777778\textwidth}}
\multicolumn{2}{l}{Dependent Variable: Call Money Rate}\tabcellsep \tabcellsep \\
Variables\tabcellsep \tabcellsep \tabcellsep Coefficient\\
Call Money Rate (-1)\tabcellsep \tabcellsep \tabcellsep 0.29 (0.14)\\
Output Gap\tabcellsep \tabcellsep \tabcellsep -0.20 (0.11)**\\
Output Gap (-1)\tabcellsep \tabcellsep \tabcellsep 0.50 (0.15)*\\
Output Gap (-2)\tabcellsep \tabcellsep \tabcellsep -0.58 (0.11) ***\\
Inflation\tabcellsep \tabcellsep \tabcellsep 1.10 (0.25) ***\\
Inflation (-1)\tabcellsep \tabcellsep \tabcellsep -0.49 (0.28) *\\
Inflation (-2)\tabcellsep \tabcellsep \tabcellsep 0.51 (0.13) ***\\
Institutional Quality\tabcellsep \tabcellsep \tabcellsep -0.09 (0.05) *\\
Institutional Quality (-1)\tabcellsep \tabcellsep \tabcellsep 0.24 (0.06) ***\\
Institutional Quality (-2)\tabcellsep \tabcellsep \tabcellsep 0.06 (0.07)\\
Institutional Quality (-3)\tabcellsep \tabcellsep \tabcellsep -0.20 (0.05) ***\\
Intercept\tabcellsep \tabcellsep \tabcellsep 0.90 (1.54)\\
R-Squared\tabcellsep \tabcellsep 0.96\tabcellsep S.E of Regression\tabcellsep 0.74\\
\multicolumn{2}{l}{Schwarz Bayesian Criterion}\tabcellsep -39.07\tabcellsep F-Stats [Prob.]\tabcellsep 31.80 [0.00]\\
\multicolumn{2}{l}{Number of Observations: 28}\tabcellsep \tabcellsep \\
\multicolumn{5}{l}{Note: values in parenthesis indicate Standard Error; ***, ** and * shows significance at 1\%, 5\% and 10\% respectively}\\
\tabcellsep \tabcellsep \multicolumn{2}{l}{Table 4.4 : Bound Test}\\
Computed F-Static\tabcellsep \multicolumn{2}{l}{Lower Critical F-Static}\tabcellsep Upper Critical F-Static\tabcellsep Result\\
10.10783\tabcellsep \multicolumn{2}{l}{5\%=3.05}\tabcellsep 5\%=3.968\tabcellsep Co-integration\\
\tabcellsep \multicolumn{2}{l}{10\%=2.68}\tabcellsep 10\%=3.53\end{longtable} \par
 
\caption{\label{tab_5}Table 4 . 3 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{4} \par 
\begin{longtable}{P{0.1627659574468085\textwidth}P{0.31648936170212766\textwidth}P{0.10851063829787233\textwidth}P{0.2622340425531915\textwidth}}
\tabcellsep \tabcellsep \tabcellsep Year\\
\tabcellsep \tabcellsep \tabcellsep Volume XVI Issue III Version I\\
\tabcellsep \tabcellsep \tabcellsep ( )\\
Lag\tabcellsep \multicolumn{2}{l}{5 : Breusch-Godfrey Serial correlation LM test F-statistic Chi-Square}\tabcellsep Global Journal of Management and Business Research\\
1\tabcellsep 0.759\tabcellsep 1.488\\
\tabcellsep (0.401)\tabcellsep (0.222)\\
2\tabcellsep 0.351\tabcellsep 1.501\\
\tabcellsep (0.711)\tabcellsep (0.472)\\
3\tabcellsep 0.215\tabcellsep 1.516\\
\tabcellsep (0.884)\tabcellsep (0.679)\\
\multicolumn{2}{l}{Note: values in parenthesis indicate probability}\tabcellsep \end{longtable} \par
 
\caption{\label{tab_6}Table 4 .}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{46} \par 
\begin{longtable}{P{0.3339285714285714\textwidth}P{0.5160714285714285\textwidth}}
\tabcellsep Value\\
F-statistic\tabcellsep 0.0046\\
\tabcellsep (0.94)\end{longtable} \par
 
\caption{\label{tab_7}Table 4 . 6 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{4} \par 
\begin{longtable}{P{0.6479245283018868\textwidth}P{0.2020754716981132\textwidth}}
\multicolumn{2}{l}{7 : Long Run Relationship}\\
Dependent Variable: Call Money\tabcellsep \\
Rate\tabcellsep \\
Variable\tabcellsep Coefficient\\
Output Gap\tabcellsep -0.39 (0.16)***\\
Inflation\tabcellsep 1.60 (0.35) ***\\
Institutional Quality\tabcellsep 0.01 (0.05)\\
Intercept\tabcellsep 1.27 (2.22)\\
\multicolumn{2}{l}{Note: values in parenthesis indicate standard error; ***,}\\
indicate significance at 1\%,\tabcellsep \end{longtable} \par
 
\caption{\label{tab_8}Table 4 .}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{48} \par 
\begin{longtable}{P{0.85\textwidth}}
Variable\end{longtable} \par
 
\caption{\label{tab_9}Table 4 . 8 :}\end{figure}
 			\footnote{Like Douglass North, Darron Acemoglu and Dani Rodrik etc. A © 2016 Global Journals Inc. (US)} 			\footnote{\hyperref[b15]{Taylor (1993)} defined the rule over inflation and output gap as: i t = r * + 0.5y t + 1.5? t , where i t =federal funds rate, ? t =inflation, r * = real rate of interest, and y t =output gap.\hyperref[b6]{7} See for example\hyperref[b1]{Huang and Wei (2006)}, Dimakou (2006), Wu (2008), Calderon et al. (2010), Chaudhary et al. (2010) Duncan (2011).\hyperref[b7]{8} For example\hyperref[b8]{Malik (2007)},\hyperref[b9]{Malik and Ahmed (2007)},\hyperref[b4]{Islam (2009)}, Malik and Ahmed (2011) etc.\hyperref[b8]{9} For example Judd and Rudebusch (1998), Gerlach-Kristen (2003).} 			\footnote{© 2016 Global Journals Inc. (US) 1} 			\footnote{© 2016 Global Journals Inc. (US)} 			\footnote{The Impact of Institutional Quality on Monetary Policy in Pakistan} 		 		\backmatter  			  				\begin{bibitemlist}{1}
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\end{document}
