The Importance Of Time Series Data In Research

Meaning is there some degree of significant when I visually analyze the regression correlation. This allows me to establish a quantitative relationship between two or more variables. The independent variables (LHS) are used to predict, while the dependent variable (RHS) is used to response to the independent variable. To get the best fitted line of regression the best method of least squares. This is done by minimizing the sum of squares of vertical deviation, because since the deviation is squared followed by summed there is no indication of positive and negative values. Once I fitted my regression model, then I will analyze my residuals. The residuals are my deviations from the observation values. This will determine if my assumption on the linear relationship is feasible.

Good job with the equations. Make sure you keep using proper subscripts and superscripts in future reports.

5) Graph the equation you wrote in step four superimposed over the original data. Comment on how well or how poorly the equation fits the data.

9. Refer to the model and estimates in the previous question. Ceteris paribus, according to these

1. In the first box you will identify one of the 6 Assumptions of the Andragogical Model

G) Explain how the model (diagram,parameters, equations) relates to the purpose of the article. (How was the model used to achieve the purpose ?)

regression analysis for this model it was determined that it was able to explain approximately

Soni talks about the theoretical model and its focus on three independent variables. These variables are assumed to

The two estimation equations that have been used for the purpose of this analysis has been summarised as follows:

The research data was analyzed using a statistical software called STATA. Before running the data for the regression analysis, panel unit-root test was performed to see if data is stationary over time so that it can be used in estimating the variables in question. The value of the variables in the model throughout the research period is not constant; that means, there are some periods where there are spikes. These periods of ups and downs in value of economic variables are called shock in economic jargon. The notion behind testing for stationarity is to identify whether the effect of such shocks is permanent or transitory. If the effect of such shocks is temporary, the subsequent values of the variables will return to their long term equilibrium suggesting that the data is stable even with the presence of shocks.

The last step before fitting autoregressive and moving averages terms to the data, is to check for unit roots. We will use the augmented Dickey-Fuller test to decide whether the data has a unit root or not. The H0 of the test is that the data has a unit root against the Ha, that the data has no unit root. Table 1.1 shows the result of the augmented Dickey-Fuller test. The p-value of the

As shown in Tables A1 and A2, some countries allow for the existence of a long-run relationship in more than one functional form. In those cases, we need to determine the functional forms together with the long-run relationship parameters estimated by Equation (4). We will check (i) whether the parameters are significant; (ii) whether turning pints of is inside of the sample; (iii) whether cubic forms has only tiny decreasing sections and approach linear models; (iv) whether the error of Equation (3) is serially uncorrelated and the model is stable. Table A3 in Appendix lists the ECT estimation for long-term relationships with three functional forms and the optimal functional form that we selected based on the bonds test and ECT estimation.

The stationary property of time series is tested by using Phillips-Perron (PP) unit root tests as PP-test has greater power than the Augmented Dickey and Fuller (ADF) test (Banerjee et al 1993). Another advantage of the PP tests over the ADF test is that the PP tests are robust to general forms of heteroskedasticity in the error term ut (Phillips and Perron 1988). Besides, unlike the ADF technique, the user does not have to specify a lag length for the test regression in the PP technique (Debnath, A., and N. Roy 2012). The result of unit root tests on the natural logarithms of per capita GDP and per capita electricity consumption is shown in

After testing the stationarity of variables, we fit a VAR model. And we choosea VAR model by correlogram, information criteria (i.e. LR, FPE, SC, AIC, and HQ), and AR roots graph. The method are based on Chapter 5(pp.285-287).

tools and approach as the main analytical methods. It includes OLS regression after testing stationarity, co-integration and causal relationships among the variables for time series data-set.