-4.710556 Durbin-Watson stat 2.099147 Since we are talking about asset returns, a standard GARCH model may not be the best choice as we would expect there to be asymmetry in the volatility (Brooks 2008, p. 404). The EGARCH model would allow negative shocks to have a larger effect on the conditional variance than positive ones. As we can see in the below Eviews output, it is the case that negative shocks have a larger effect because the coefficient C(4) is negative. Because we are estimating the log of the conditional variance, unlike the standard GARCH model, it can be more difficult to interpret the exact meaning of all the parameters. Dependent Variable: RLSP500 Method: ML - ARCH (Marquardt) - Normal distribution Date: 07/29/12 Time: 20:08 Sample (adjusted): 1/10/2005 1/31/2011 Included observations: 317 after adjustments Convergence achieved after 35 iterations Presample variance: backcast (parameter = 0.7) LOG(GARCH) = C(2) + C(3)*ABS(RESID(-1)/@SQRT(GARCH(-1))) + C(4) *RESID(-1)/@SQRT(GARCH(-1)) + C(5)*LOG(GARCH(-1)) Variable Coefficient Std.
Testing Assumptions There are two histograms, showing information on GPA, and showing information on final grade. Histograms are commonly used with interval or ratio level data (Corty, 2007). The data in the GPA is distributed and slightly skewed to the right, which means it has a positive skew and has a peaked distribution. The final histogram also has a leptokurtic frequency distribution, but is skewed to the left meaning this has a negative skew. Descriptive Statistics N Minimum Maximum Mean Std.
From this we deduce that this elasticity is relevant to the design of the lottery (Farrel 1). The way that the demand elasticity is derived is by comparing the rollover weeks with the non-rollover weeks. By doing this, the normal demand is recorded during the non-rollover weeks to see what level the demand is usually at. Then from there they can see how the demand increases as the lott... ... middle of paper ... ...ing how some studies and economic research has been taking place and where. I found some of the studies to be trivial.
3. Inferential statistics concerned with generalizing from a sample to make estimates and inferences about a wider population (Neuman, 2003... ... middle of paper ... ....e. more than 30 (Hair et al., 2006). Sekaran (2003) suggests the approximation to normality of the observed variables could be investigated by inspecting the data through histograms, stem-and leaf displays, probit plots and by computing univariate and multivariate measures of skewness and kurtosis. Histograms, stem-and-leaf and probit plots indicate the symmetric distribution of variables or sets of variables. Tabachnick and Fidell (1996) suggest the value of skewness and kurtosis is equal to zero if the distribution of a variable is normal.
Mean The mean of a data set is the arithmetical average of all the numbers. Its formula is: where n is number of cases. One problem with using the mean, is that if there is one outcome that is very far from the rest of the data (outlier), then the mean will be strongly affected by this outcome. There is possibility to low down the effect of outliers. This method is called the trimmed mean.
Therefore, the degrees of freedom of an estimate of variance is equal to N - 1, where N is the number of observations (Jackson, 2012). Given a single set of six numbers (N) the df = 6 – 1 = 5. What do inferential statistics allow you to infer? Inferential statistics establish the methods for the analyses used for conclusions drawing conclusions beyond the immediate data alone concerning an experiment or study for a population built on general conditions or data collected from a sample (Jackson, 2012; Trochim & Donnelly, 2008). With inferential statistics, you are trying to reach conclusions that extend beyond the immediate data alone.
The greater the standard deviation is, the more spread out the observations are. In this study, the highest value of standard deviation is consumer ethics .82572. Therefore, skewness measures the degree and direction of asymmetry. A symmetric spreading such as a normal distribution has a skewness of 0, and a distribution that is skewed to the left. In this study the skewness is negative value.
Distribution of the residual in multiple regressions should follow normal distribution (Lind and Marchal and Whaten, 2008). There are two ways to conduct the normality test and... ... middle of paper ... ...f-test is the overall evaluator for the whole model and t-test is the evaluator for each of the independent variable. Thus, in this T-Test According to Cooper and Schindler (2011), t-test is a test to know the statistical significance of an independent variable towards dependent variable. Writers will compare the results of the t-test with the ANOVA table. Writer will compare the results of the T-test with the significance level of the research.
The basis for CAPM is that asset risk is measured by the variance of its return over future periods. (McCullough, 2005) Assets with β < I will display average movements in return less extreme than the overall market, while those with a > I will show return fluctuations greater than the overall market. All other measures of risk is not important. CAMP works best for long-term investments. Ki = the required return on asset i Rf = risk-free rate of return on a U.S. Treasury bill βi = beta coefficient or index of non-diversifiable risk for asset i km = the return on the market portfolio of assets The Discounted Cash Flow Method, (DCF) summarizes a company cash flow to reflect the time value of money.
( Equation3.2) where ra is the selected action, either right or left. δar and δal are the Kronecker delta, δar = 1 if a = r and δar = 0 if a = l and δal = 1 if a = l and δal = 0 if a = l . ȓ is the mean reward under the specified policy. These equations perform the stochastic gradient ascent on the average reward, whatever the value of ȓ (the mean reward).Different values of ȓ lead to different variation of the stochastic gradient terms, and thus different speeds of learning.