ARDL Used in Econometrics

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Applied Economics LettersPublication details, including instructions for authors and subscription information:http://www.informaworld.com/smpp/title~content=t713684190

Financial development and economic growth an ARDL approach for the caseof the small island state of MauritiusBoopen Seetanah aa University of Technology, Mauritius, Pointes aux Sables, Mauritius

First Published:August2008

To cite this Article Seetanah, Boopen(2008)'Financial development and economic growth: an ARDL approach for the case of the smallisland state of Mauritius',Applied Economics Letters,15:10,809 — 813To link to this Article DOI 10.1080/13504850600770889URL http://dx.doi.org/10.1080/13504850600770889

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Applied Economics Letters , 2008, 15 , 809–813

Financial development andeconomic growth: an ARDL

approach for the case of thesmall island state of Mauritius

Boopen Seetanah

University of Technology, Mauritius, Pointes aux Sables, MauritiusE-mail: [email protected]

The article investigates the dynamic empirical link between financialdevelopment and economic performance for the case of the developingsmall island state of Mauritius using a unique time-series data set over theperiod 1952 to 2004. The analysis was performed using two differentproxies for financial development in an ARDL framework. The resultssuggest that financial development have been contributing to the outputlevel of the economy in both short and long run. It thus highlights theeconomic importance of financial development and provides new evidencefor the case of island economies using recent cointegration approach.

I. Introduction

The importance of the financial sector in theeconomic development has received much attentionin the recent literature. 1 A strong consensus hasemerged in the last decade that well-functioningfinancial intermediaries have a significant impact oneconomic growth, 2 see King and Levine (1993),Jayaratne and Strahan (1996), Ross and Levine(1997), Chowdhury (1997), Demirgu ç̈ -Kunt andMaksimovic (1998), Rajan and Zingales (1998),Levine and Zervos (1998), Neusser and Kugler(1998), Levine et al . (2000), Wachtel (2003).However until now most studies have been focusedon developed countries cases and it is only lately thatscholars have been implicitly dealing with the issue of causality and dynamics in the financial development

and economic growth link (Levine et al ., 2000).

Studies using time-series analysis for developingcountry cases have been scarce and to our knowledgeno studies have been performed for the case of developing small island states. Empirical findingsfrom developed countries’ cases are not directlyapplicable and relevant to island states given theirspecial characteristics and vulnerability. The aim of the article is thus to investigate the empirical linkbetween financial development and economic perfor-mance for the case of the developing small islandstate of Mauritius using a unique time-series data setover the period 1952 to 2004 and allowing fordynamics. It is hoped that the study will add newinsights to and also to fill a gap in the literature.

The structure of this article is as follows: Section IIdescribes the preferred modelling function used,

1 Refer to King and Levine (1993) for a comprehensive theoretical overview.2 There are however some empirical works which could not establish a significant link, for instance, Dawson (2003), Ram(1999) among others.

Applied Economics Letters ISSN 1350–4851 print/ISSN 1466–4291 online 2008 Taylor & Francis 809http://www.informaworld.com

DOI: 10.1080/13504850600770889


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elaborates on the data collection and investigates theempirical link between financial development andeconomic growth for the case of Mauritius.Section III concludes and deals with policyimplications.

II. Methodology and Analysis

Causality and dynamic issues and ARDL model

To allow for causality and dynamics and given thatnot all of our time-series are stationary to the sameorder (some are I(0) while others are I(1), this isdiscussed below), the recently developedcointegration procedure by Pesaran et al . (2001), theautoregressive distributed lag model (ARDL) proce-dure will be applied. In fact the procedure allowsfor different long-run relationships and short-rundynamics and this is important for the estimation of

the equilibrium conditions. In addition, the Pesaranet al . (2001) technique can be implemented regardlessof whether the variables are integrated of order (1) or(0) and can be applied to small finite samples.

The economic model

We follow the standard literature, see King andLevine (1993), Ross and Levine (1997), Levine andZervos (1998), Levine et al . (2000), Wachtel (2001),in specifying the economic model subsequently.

y ¼ f ðIVTGDP, XMGDP, SER,LIQGDP Þ model 1

y ¼ f ðIVTGDP, XMGDP, SER,PRIGDP Þ model 2

where IVTGDP is the country’s investment dividedby its gross domestic product (GDP; investmentratio), XMGDP is total of export and importsdivided by the GDP of the country and is measureof openness and SER is the secondary enrolmentratio and proxies for the quality of human capital.

To measure financial development we use mone-tary aggregates, more specifically LIQGDP (refer tomodel 1) which is the ratio of liquid liabilities to thecountry’s GDP. Liquid liabilities include currency

plus demand deposits and interest-bearing liabilitiesof banks and nonbank financial intermediaries. Thisis a typical measure of ‘Financial Depth’ and hasbeen widely used (McKinnon (1973) and King andLevine (1993)).

The second widely used measure, PRIGDP (referto model 2), has been the value of credits by financialintermediaries to the private sector divided by GDP.This measure of financial development is more than asimple measure of financial sector size.

PRIGDP isolates credit issued to the private sector,as opposed to credit issued to governments, govern-ment agencies and public enterprises. Furthermore, itexcludes credits issued by the central bank. PRIGDPhas been used extensively as an indicator because itimproves on other measures of financial development(Levine et al ., 2000). Higher levels of PRIGDP are

interpreted as higher levels of financing services andtherefore greater financial intermediary development.This proxy shall also be utilized to investigate andconsolidate the financial development and economicgrowth.

The main sources of our independent variables arefrom the World Bank’s and the IMF’s ‘InternationalFinancial Statistics’ (IFS) except for the case of SER,where the country’s Central Statistical Office’sbiannual digest of Statistics has been consulted. Thedependent variable output was proxied by the realGDP per capita at constant price ( Y ) and wasgenerated from the IFS. The time period of thestudy is over the years 1952 to 2004.

The econometric model and preliminary tests

Recall models 1 and 2 aforementioned and takinglogs on both sides of the equation and denoting thelowercase variables as the natural log of therespective uppercase variable, results in the following:

y ¼ 0 þ 1ivtgdp þ 2xmgdp þ 3ser þ 4 liqgdp þ "ð1Þ

y ¼ 0 þ 1 ivtgdp þ 2xmgdp þ 3ser þ 4prigdp þ "

ð2ÞBefore considering the appropriate framework of theeconometric model, it is important to investigate theunivariate properties of all data series and todetermine the degree to which they are integrated.Both the augmented Dickey–Fuller (ADF) (1979)and Phillips–Perron (PP) (1988) unit-roots tests havebeen employed for that purpose and the results aresummarized in Tables 1 and 2 given further.

Test for stationarity (refer to Tables 1 and 2) showsthat all our variables are integrated of order 1 (I(1)and thus stationary in difference) except ivtgdp, the

proxy for openness which is an I(0) variable.

Testing for cointegration using the ARDL

In this part we test the existence of a long-termrelationship (cointegration) in using testing andestimation procedure advanced in Pesaran and Shin(1999).

For the specification 1 and 2 above, the errorcorrection versions of the ARDL model in the

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variables y, invgdp, xmgdp, ser and liqgdp (model 1)and prigdp (model 2) are given respectively by

y ¼ 0 þ Xn

i ¼1bi yt i þ X

n

i ¼1ci ivtgdp t i

þ Xn

i ¼1d i xmgdp t i þ X

n

i ¼1ei ser t i

þ Xn

i ¼1 f i liqgdp t i þ 1 yt 1 þ 2ivtgdp t 1

þ 3xmgdp t 1 þ 4ser t 1 þ 5liqgdp t 1 þ " tð3Þ

y ¼ 0 þ Xn

i ¼1mi yt i þ X

n

i ¼1 pi ivtgdp t i

þ Xn

i ¼1qi xmgdp t i þ X

n

i ¼1r i ser t i

þ Xn

i ¼1

si liqgdp t i þ 1 yt 1 þ 2ivtgdp t 1

þ 3xmgdp t 1 þ 4ser t 1 þ 5liqgdp t 1 þ " tð4Þ

Since we have annual observations, we chose n ¼ 1for the maximum order of lags in the ARDL modelin both cases and carry out the estimation over theperiod of study. In fact the same lag lengthwas chosen when using the final prediction errordue to SBC.

For model 1, the hypothesis that is being tested isthe null of ‘nonexistence of the long run relationship’defined by

H o : 1 ¼ 2 ¼ 3 ¼ 4 ¼ 5 ¼ 0

And the alternative hypothesis is

H 1 : 1 6¼ 0; 2 6¼ 0; 3 6¼ 0; 4 6¼ 0; 5 6¼ 0

The recommended statistic is the F -statistics for the joint significance of 1 , 2 , 3 , 4 and 5 . Computationof this F -statistic requires running the followingregression

yt ¼ 0 þ b yt 1 þ c ivtgdp t 1 þ d xmgdp t 1þ e ser t 1 þ f liqgdp t 1 þ " t ð5Þ

and a variable addition test is subsequently made byincluding the following.

1 yt 1, 2ivtgdp t 1, 3xmgdp t 1 , 4ser t 1, 5liqgdp t 1

It should be however be noted that the distribution of

the F -statistic is nonstandard, irrespective whetherregressors are I (0) or I (1). Pesaran et al . (1996) havetabulated the appropriate critical values for differentnumber of regressors and whether the regressorscontain an intercept or a time trend.

The F -Statistics F (y/ivtgdp, xmgdp, ser, liqgdp)turned out to be 6.34 (This is also confirmed bythe maximum eigen values and trace values of theJohansen test for cointegration) and exceeds theupper bound of the critical value band. We thus reject

Table 2. Summary results of unit-root tests in first difference: D/F and Phillips–Perron test

Variables(in log)

Lagselection

Aug.Dickey–Fuller Phillips–Perron

Criticalvalue

Variabletype

Aug. Dickey–Fuller(with time trend, t )

Criticalvalue

Variabletype

y 0 8.57 8.78 2.936 I (0) 8.98 3.508 I (0)xmgdp 0 8.77 5.29 2.936 I (0) 8.65 3.508 I (0)ser 0 4.23 3.74 2.936 I (0) 4.45 3.508 I (0)liqgdp 0 4.75 2.97 2.936 I (0) 5.03 3.508 I (0)prigdp 0 4.23 3.74 2.936 I (0) 4.45 3.508 I (0)

Table 1. Summary results of unit-root tests in level form: Dickey–Fuller and Phillips–Perron test

VariablesLagselection

Aug.Dickey–Fuller

Phillips– Perron

Criticalvalue

Variabletype

Aug. Dickey–Fuller(time trend, t )

Criticalvalue

Variabletype

y 1 1.46 2.59 2.924 I (1) 2.2 3.51 I (1)ivtgdp 1 3.58 2.284 2.924 I (0) 4.34 3.51 I (0)xmgdp 1 2.13 2.83 2.924 I (1) 3.11 3.51 I (1)ser 1 0.91 2.91 2.924 I (1) 1.18 3.51 I (1)liqgdp 1 1.22 0.56 2.924 I (1) 0.79 3.51 I (1)prigdp 1 1.22 0.56 2.924 I (1) 0.79 3.51 I (1)

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the null hypothesis of no long-run relationshipbetween the variables irrespective of their order.The test results thus suggest that there is a long-runrelationship between the variable.

Substituting liqgdp by prigdp as the alternativeproxy for financial development and replicating the

same procedure as above yielded an F -Statistics F ( y/ivtgdp, xmgdp, ser, prigdp) of 4.54 which implied theexistence of cointegration in this alternative specifica-tion as well.

Estimation results. Given that both specificationsare cointegrated, the unrestricted error correctionrepresentation of the ARDL model is given byEquations 3 (for the case of liqgdp) and 4 (for thecase of prigdp) respectively.

The next stage of the procedure would be toestimate the coefficients of the long-run relations andthe associated error correction model (ECM) usingthe ARDL approach. The order of the distributedlag on the dependent variable was selected by theSchwartz Bayesian Criterion (SBC) 3 and turned outto be one.

The SBC criteria select the ARDL (1, 0, 1, 0, 1) forboth models respectively. The long-run estimatedcoefficients are shown in the Table 3.

The point estimates are not too different underboth specifications. It is observed that financialdevelopment may have contributed positively to theoutput of the country in the long run. In fact a 1%increase in the liquid liabilities to GDP ratio (liqgdp)is associated with a 1.3% increase in output level. The

link is also confirmed by using prigdp as the proxyand it was estimated to be slightly lower at 0.1. Asexpected, for the case of Mauritius, the investmentlevel and the extent of openness of the economy havebeen the main ingredients for economic development.The quality of human capital is also reported to havebeen an important factor.

From Table 4 both models suggest that the impactof financial development on the output of Mauritiushas been positive and significant. The coefficients forthe other explanatory variables are well-behaved andhave the expected sign and significance. Moreover,the coefficient of the ECM of the selected ARDL

(1,0, 1, 0, 1) is negative and highly significant at 1%level. This confirms the existence of a stable long-runrelationship and points to a long-run co-integrationrelationship between variables. The ECM representsthe speed of adjustment to restore equilibrium in thedynamic model following a disturbance. The coeffi-cient of the ECM is around 0.4 in both cases and

3 Pesaran and Smith (1998) found that SBC is preferable to AIC, as it is a parsimonious model that selects the smallestpossible lag length, while AIC selects the maximum relevant lag length.

Table 4. Error correction representation for the selected ARDL model

RegressorCoefficient AIC(1,0,1,0,1) Model 1 t-ratio

Coefficient SBC(1,0,1,0,1) Model 2 t-ratio

ivtgdp 0.596*** 4.12 0.584*** 4.06xmgdp 0.187** 1.6 0.187* 1.69ser 0.162** 2.75 0.152** 3.23liqgdp 0.154** 2.00 0.065* 1.66

ECM( 1) 0.403*** 4.21 0.415*** 4.43R-square 0.526 0.58DW 1.81 1.76

Dependent variable is y.* significant at 10%, ** significant at 5%, *** significant at 1%.

Table 3. Estimated long run coefficients based on ARDL approach

RegressorCoefficient SBC(1,0,1,0,1) Model 1 t-ratio

Coefficient SBC(1, 0,1, 0, 1) Model 2 t-ratio

invtgdp 0.919*** 14.35 0.861*** 10.23xmgdp 0.69*** 2.97 0.731*** 4.18ser 0.401*** 2.96 0.361*** 3.43liqgdp 0.131* 1.93prigdp 1.84 0.100** 2.12Constant 7.38*** 5.83 0.622*** 3.57

Dependent variable is y.* significant at 10%, ** significant at 5%, *** significant at 1%.

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this implies that a deviation from the long-runequilibrium following a short-run shock is correctedby about 40% after each year.

IV. Summary of Results

The article investigated the relationship betweenfinancial development and the economic performancefor the case of the developing small island state of Mauritius over the period 1952 to 2004. To accountfor the fact that not all of the series were integrated tothe same extent and also to incorporate dynamicsin the analysis, an ARDL approach was used. Theanalysis was also performed using two differentproxies for financial development and the resultssuggest that financial development have been con-tributing to the output level of the economy in both

short and long run. Error correction modelling wasused to confirm the existence of a stable long-runrelationship and moreover determined a deviationfrom the long-run equilibrium following a short-runshock, which is corrected by about 40% after eachyear. The above results highlight the importance of financial development and moreover provide newevidences for the case of island economies usingrecent innovative cointegration approach.

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ARDL Used in Econometrics