This
chapter of the study aims to explain the results of the time series data. In
this study E-views 9 software is used for the analysis of data to obtain the
objectives of the study. The chapter begins with explaining the results of
stationarity tests on data which is necessary to check the long run and short
run co-integration of selected variables with demand for life insurance.  5.2. Stationarity TestBefore
applying the ARDL approach to cointegration, data of all variables are required
to test for stationarity. To know whether the data is stationary or
nonstationary, the Augmented Dickey Fuller test or unit root test is used. A
series is said to be stationary, when its mean and variance are constant over
time. 5.2.1. Results of ADF Test

Thus,
according to the results of ADF test which is applied on selected variables: price
of life insurance, crude death rate and education are stationary at level at 5%
significance level. The variables: gross saving, inflation and sum assured are
non-stationary at level. After checking the stationarity at level, data are
checked at first difference. The results of the first difference showed that
these variables became stationary after taking the log of education and sum
assured, therefore we could apply ARDL approach to cointegration. The spurious
regression problem occurs when the error terms (the residuals) of the
regression model have a unit root. Therefore, results show that error term have
no unit root which means that there is no spurious regression problem. The
results are mentioned in table 5.1 below.5.3. ARDL Bounds tests for
cointegration:for
the empirical analysis of the long-run relationships and short run dynamic relations
among the selected variables, the autoregressive distributed lag (ARDL)
cointegration technique is used. The ARDL cointegration approach was first time
used by Pesaran and Shin (1999) and Pesaran et al. (2001). It is applied on
data as all the variables are not integrated at the same order, some variables
are integrated of order one, some of order zero and as the sample size of data
is less than 30 years therefore, the ARDL test is relatively more efficient.  The estimates of the long-run model are
unbiased which are obtained with ARDL technique (Harris and Sollis, 2003). 5.3.1. ARDL Bound Test for
Co-Integration

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Lag-length
selection is very important for correct results of long-run relationship in the
model (Bahmani-Oskooee and Bohal, 2000). Table 5.2 presents the computed
F-statistic to select optimal lag-length in the model. with lag of order 4 the
lower and upper bound values at 90 percent significance level are 2.75 and 3.79
respectively. Table 5.2 shows that the computed value of F-statistic (3.09) is
in between lower and upper bound value of F-statistic at 10 % which indicates
inconclusive. Therefore, we conclude that there is may or not long-run
relationship among the variables.The
joint 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) by
conducting an F-test for the joint significance of the coefficients of the
lagged levels of the variables.

AIC
is used to select the orders of the ARDL (1, 0, 2, 0, 1, 1) model in the six
variables. 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 in
Table 5.2.Table
5.3 indicates the long run results of ARDL model. The coefficient shows that
the relationship is either positive or negative between dependent and
independent, whereas the probability value shows that whether the relationship
between independent and dependent variables is significance or insignificance.
According to rule of thumb, if p < 5% it indicates the significance there is positive relationship between dependent and independent variables. Findings confirm that there is statistically positive and significance relationship between gross saving (gst) and demand for life insurance (lnliddt) at 5% level of significance. It indicates that if we increase 1unit in gst variables in this response there will increase of 0.031484 in lnliddt.This results supports the findings of Steven Haberman and Chee Chee Lim's study conducted in 2011 The result shows that the price of life insurance is significantly negative relationship 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 response there will decrease of 0.00227 in lnliddt. This result supports the literature present on the impact of price of life on the demand of life insurance and is matched with finding of Steven Haberman and Chee Chee Lim conducted in 2011. According to results there is positive and significance relationship education (lnedt) and demand for life insurance because of p values is less than 5%.it indicates that if we increase 1unit in education (edt) variable in this response there will increase of 0.612579 in life insurance demand lnliddt. This finding is lined with Kjosevski study conducted in 2010, Amrot Yilma's study conducted in 2014, study of Steven Haberman and Chee Chee Lim conducted in 2011 and is opposite to the study of Celik and Kayali, higher education influences positively life insurance demand. According to the results the inflation influences the demand for life insurance significantly negative. The result on inflation rate was consistent with the theoretical propositions and is not lined with Neumann analysis conducted in 1946- 1964 and is lined with Kjosevski study conducted in 2010 and Amrot Yilma (2014), Nesterova (2008), study of Steven Haberman and Chee Chee Lim's study conducted in 2011 which showed that inflation had significant negative influence and a damping impact on the purchase of life insurance. The result also shows that there is positive and significance relationship between crude death rate life insurance demand at 5 % level of significance as the value of p is less than 5% at lag 2 which indicates that when there is 1 unit increase in crude death rate (cdrt), the demand for life insurance will increase by 4.899575 which is lined with study of Steven Haberman and Chee Chee Lim conducted in 2011 and all literature of determinants of life insurance demand in different countries. The R2 is 0.99; implying that approximately 99% of variations in life insurance demand are explained by all the independent variables while the remaining 1% is captured by the error term. There is a significant linear association between the life insurance market demand and the economic variables.   The overall significance test of model: The P value for the F-test of overall significance test is 0.000 which is less than significance level 0.05, therefore, reject the null-hypothesis and concluded that the model of study gives a good fit than the intercept-only model.

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