The first step taken by our Eviews homework helper is to visualize the time series plot of CPI. It shows that the coefficient of Kentucky is not significant in the model which infers that there is likely no state-specific effect that drives the result in b Similar detailed results are provided as a part of our EViews assignment help service. We conclude that the marginal effect of age is not significantly different from 0. Therefore, we cannot reject the null hypothesis that it is not significantly different from 0. Specifically, the average duration after reform is higher than before reform by 1.68 weeks, and the p-value is0.00220.05. The regression result presented in table 1, column 1,showsthat the reform hasa significant effect on the duration of claiming benefits as the coefficient is significant. This implies a weak positive relationship between the duration of benefits and the benefits policy change dummy variable exists. The correlation coefficient between the duration of benefits and the benefitspolicy change dummy is 0.0332. The higher the number of claims, the longer it will take to have the benefit. The number of claims is also another reason. This means it may take longer for those claiming benefits to be able to claim it. The larger the size of the firm, the more the bureaucracy and the more the number of claims. If he is the high wage class, he can afford treatment without claiming benefit, so there will be a longer duration of benefits claim. Another factor is the income level of the injured worker.
Severe injury means a longer time of stay in the hospital, which connotes a longer duration to benefit claim.
The severity of the injury is one factor that will affect the duration of benefit claims. and the respective exchange rate aren't cointegrated.Help with Eviews Assignment on Regression Analysis I'm not sure what to make of this result as it's hard to believe that differentials in short-term interest rates between U.S. However, when I test for cointegration using Johansen's test I find no cointegration equation between the two series (the output results are below using XLSTAT), which undermines the case for a linear regression I presume. However, if the two series are non-stationary and cointegrated then the residuals from a linear regression model will be stationary and hence the linear regression model could be useful. I ran Dickey-Fuller test on both and find both series to be non-stationary, which indicates that as a framework a linear regression model may not be useful (here short-term rates are the independent variable and GBP/USD is the dependent variable). and the GBP/USD exchange rate where observations are taken on a daily frequency since 2015. I'm whether a linear relationship exists between two time series: short-term interest rate differential of U.S. Hello - this is more of a conceptual question.
0 kommentar(er)
