This long run and short run co-integration of

Thischapter of the study aims to explain the results of the time series data. Inthis study E-views 9 software is used for the analysis of data to obtain theobjectives of the study.

The chapter begins with explaining the results ofstationarity tests on data which is necessary to check the long run and shortrun co-integration of selected variables with demand for life insurance.  5.2. Stationarity TestBeforeapplying the ARDL approach to cointegration, data of all variables are requiredto test for stationarity. To know whether the data is stationary ornonstationary, the Augmented Dickey Fuller test or unit root test is used. Aseries is said to be stationary, when its mean and variance are constant overtime.

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5.2.1. Results of ADF TestThus,according to the results of ADF test which is applied on selected variables: priceof life insurance, crude death rate and education are stationary at level at 5%significance level. The variables: gross saving, inflation and sum assured arenon-stationary at level. After checking the stationarity at level, data arechecked at first difference. The results of the first difference showed thatthese variables became stationary after taking the log of education and sumassured, therefore we could apply ARDL approach to cointegration.

The spuriousregression problem occurs when the error terms (the residuals) of theregression model have a unit root. Therefore, results show that error term haveno unit root which means that there is no spurious regression problem. Theresults are mentioned in table 5.1 below.5.3.

ARDL Bounds tests forcointegration:forthe empirical analysis of the long-run relationships and short run dynamic relationsamong the selected variables, the autoregressive distributed lag (ARDL)cointegration technique is used. The ARDL cointegration approach was first timeused by Pesaran and Shin (1999) and Pesaran et al. (2001). It is applied ondata as all the variables are not integrated at the same order, some variablesare integrated of order one, some of order zero and as the sample size of datais less than 30 years therefore, the ARDL test is relatively more efficient.  The estimates of the long-run model areunbiased which are obtained with ARDL technique (Harris and Sollis, 2003). 5.

3.1. ARDL Bound Test forCo-IntegrationLag-lengthselection is very important for correct results of long-run relationship in themodel (Bahmani-Oskooee and Bohal, 2000). Table 5.

2 presents the computedF-statistic to select optimal lag-length in the model. with lag of order 4 thelower and upper bound values at 90 percent significance level are 2.75 and 3.79respectively.

Table 5.2 shows that the computed value of F-statistic (3.09) isin between lower and upper bound value of F-statistic at 10 % which indicatesinconclusive. Therefore, we conclude that there is may or not long-runrelationship among the variables.Thejoint F-statistic is mostly used in bound test.

first, six equations (1, 0, 2,0, 1, 1) are estimated with the help of ordinary least squares (OLS) byconducting an F-test for the joint significance of the coefficients of thelagged levels of the variables.AICis used to select the orders of the ARDL (1, 0, 2, 0, 1, 1) model in the sixvariables. Thus, equations are found with orders of ARDL (1, 0, 2, 0, 1, 1) model.The results obtained by normalizing on lnlidd in the long run are given inTable 5.

2.Table5.3 indicates the long run results of ARDL model. The coefficient shows thatthe relationship is either positive or negative between dependent andindependent, whereas the probability value shows that whether the relationshipbetween independent and dependent variables is significance or insignificance.

According to rule of thumb, if p < 5% it indicates the significance there ispositive relationship between dependent and independent variables.Findingsconfirm that there is statistically positive and significance relationshipbetween gross saving (gst) and demand for life insurance (lnliddt) at 5% levelof significance. It indicates that if we increase 1unit in gst variables inthis response there will increase of 0.031484 in lnliddt.This results supportsthe findings of Steven Habermanand Chee Chee Lim's studyconducted in 2011Theresult shows that the price of life insurance is significantly negativerelationship with the demand for life insurance as the p values is less than 5%at 1. it indicates that if we increase 1unit in plit variables in this responsethere will decrease of 0.00227 in lnliddt.

This result supports the literaturepresent on the impact of price of life on the demand of life insurance and ismatched with finding of StevenHaberman and CheeChee Lim conducted in 2011. According to results there is positive andsignificance relationship education (lnedt) and demand for life insurance becauseof p values is less than indicates that if we increase 1unit in education(edt) variable in this response there will increase of 0.612579 in lifeinsurance demand lnliddt. Thisfinding is lined with Kjosevski study conducted in 2010, Amrot Yilma’sstudy conducted in 2014, studyof Steven Haberman and Chee Chee Lim conducted in 2011 and is opposite to the study of Celik and Kayali, higher educationinfluences positively life insurance demand.

Accordingto the results the inflation influences the demand for life insurance significantlynegative. The result on inflation rate was consistent with the theoreticalpropositions and is not lined with Neumann analysisconducted in 1946- 1964 and is lined with Kjosevski study conducted in 2010 andAmrot Yilma (2014), Nesterova (2008), study of Steven Haberman and Chee Chee Lim’s study conducted in 2011 which showed thatinflation had significant negative influence and a damping impact on thepurchase of life insurance. Theresult also shows that there is positive and significance relationship betweencrude death rate life insurance demand at 5 % level of significance as thevalue of p is less than 5% at lag 2 which indicates that when there is 1 unit increasein crude death rate (cdrt), the demand for life insurance will increase by 4.899575which is lined with study of Steven Haberman and Chee Chee Lim conducted in 2011 and all literature of determinantsof life insurance demand in different countries.TheR2 is 0.

99; implying that approximately 99% of variations in lifeinsurance demand are explained by all the independent variables while theremaining 1% is captured by the error term. There is a significant linearassociation between the life insurance market demand and the economicvariables. The overall significance test ofmodel:The P value for the F-test of overall significance test is0.000 which is less than significance level 0.05, therefore, reject thenull-hypothesis and concluded that the model of study gives a good fit than theintercept-only model.


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