What Is The Methodology Used In Costimating The Impact Of Capital Flight?

a. R2 value generated by empirical estimation regression model individual very high but many independent variables that are not significantly affect the dependent variable. b. Analyzing the correlation matrix of the independent variables. If the correlation between independent variable is fairly high (generally above 0.90), then this is an indication multicollinearity. Multicollinearity can be appear due to the combined effect of two or more independent variables.

In order to remedy serial correlation, GLS is introduced. Generalized least squares (GLS) is “a method of ridding an equation of pure first-order serial correlation and in the process restoring the minimum variance property to its estimation (Studenmund, 2006).” In this model, GLS did not necessarily run because of the inconclusive result of the DW test, so that the presence of serial correlation is unsure.

For the test of Multicollinearity, I have used the ADF test and then generating the first difference variables and running regression to correct the Multicollinearity. First using the Dicky-Fuller test, the non-stationary variables were separated from the stationary variables. After which, the non-stationary variables were made stationary by using the first difference for these variables and getting them close to the mean. Now, the last step to correct the Multicollinearity was to run regression on the first difference variables.

After the dropped in favored outside conditions after Zimbabwe gained independence, the real onset of the economic crisis was traced back to the trash of the Zimbabwean dollar on the 14th of November 1997. There is that correlation is being found between the president Robert Mugabe’s election in 1980, the establishment of one party state by the unification of the ZANU and ZAPU in 1987 (Jenkins 2011, p.591), and also the economic decline of the country. By this reason, it show that, this is what caused Zimbabwe to be a failed

The cheapest and effective way of reduce customer waiting time is by managing restaurant table capacity. This includes management of when the seats are assigned and how the server handles the station as well.

Table: 1, represents the results of regression analysis carried out with the dependent variables of cnx_auto, cnx_auto, cnx_bank, cnx_energy, cnx_finance, cnx_fmcg, cnx_it, cnx_metal, cnx_midcap, cnx_nifty, cnx_psu_bank, cnx_smallcap and with the independent variables such as CPI, Forex_Rates_USD, GDP, Gold, Silver, WPI_inflation. The coefficient of determination, denoted R² and pronounced as R squared, indicates how well data points fit a statistical model and the adjusted R² values in the analysis are fairly good which is more than 60%, indicates the considered model is fit for analysis. Also, the F-Statistics which provides the statistical significance of the model and its probabilities which are below 5% level and hence proves the model’s significance.

The Unit Root Test, also named Augmented Dickey-Fuller Test (ADF Test), is put forward according to whether the macroeconomic datum or financial datum has some special characteristics, which is a particular method to test stationarity of the financial time series (Choi, 2015). To put it simply, testing the unit root is to examine whether there will be a unit root in the analysis of time series or not. The financial time series would be considered as non-stationarity if the unit root exists, which is likely to result in spurious regressions. Due

To prove if this is the case in our study, we calculated some numbers called correlation coefficients and variance inflation factors. Basically, the rule is that if one of the correlation coefficient between a pair of explanatory variables is higher than 0.9 there is evidence of multicollinearity. Another criteria is that if the variance inflation factor of each explanatory variable is higher than 10, there is a case of multicollinearity, as well. In our study, multicollinearity was not an issue.

In this section we introduce the method of likelihood to estimate the parameters involved and use them to create confidence intervals for the unknown parameters.

The one-step approximation the estimates, generalized cook distance and likelihood distance are considered. Cook and likelihood distance are two statistical diagnostics method. Models diagnostics procedures imply both graphical and formal statistical tests. These procedures recognize us to inspect whether the supposition of the regression model are justifiable and decide whether we can belief subsequent inference outcome. We use these methods to explore the performance of residual and influence in nonlinear regression model. These residuals can produce confusing results when used in diagnostic method corresponding to those for linear regression. Simulation provide to

We first use Augmented Dickey Fuller unit root test to check stationarity of variables at their level and at their first difference. In Table 2, we list the results of tested integrated orders

After collecting and cleaning the data, the first model was built using all the regressors under consideration. A thorough analysis of this full model, including residual analysis and multicollinearity check was done. The best subset regression was also tried. The normal probability

Before using the regression analysis, we have conducted test for the classical assumptions of linear regression model, including multicollinearity, heteroscedasticity and wrong functional.

To modify the regression model before running the results, I do some diagnostic tests for the obtained series. Table A2 in the appendix I shows some descriptive statistics and diagnostic tests of data. First, the series average of these three areas is all strictly positive and greater than 1.5. The standard error of JPY series is 0.907 which is close to 1 while others are a long way from unity (0.284 and 0.331). In addition, the Jarque-Bera test statistics of all series are too large, which shows that all EUR, GBP and JPY series are not normally distributed. Second, p-values of Ljung-Box Q-statistic tests for level and squared residual in all series are 0. LB(Q) presents that all series have an autocorrelation for residuals and LB(Q2) shows that all series are conditionally heteroskedastic. In this way, ARCH or GARCH model can be utilized to improve the original model (3). Third, the unit root test. On 23

In order to test for a long-term relationship in the Sri Lankan economic model, we use the Engle-Granger residual-based approach with all the variables and test for cointegration.In this case, we test the stationarity ofβ_1 x_1t+β_2 x_2t+⋯β_n x_nt=e_t. This approach is based on the Chapter 6(pp.343) in “Applied econometric time series” (Enders, 2015).