Eviews Enterprise Edition 70 01 Serial Number

there are 87 dual-classing and 66 single-classing companies appeared in the table. among the 88 dual-classing companies, 57 companies are the subsidiaries of other companies; and among the 66 single-classing companies, 20 companies are the subsidiaries of other companies. among the 88 dual-classing companies, 63 companies are mainly included in coal, iron, petroleum, mining, and electric power.

there are total population of 1457 companies and 1333 dual-classing observation with 894 companies and we use multiple linear regression for the analysis with ols (ordinary least square) method with panel data. initially, the below variables are tested by software eviews 7 to get the coefficient estimation between dependent variables and independent variable with control variables.

according to the relevant literature, li wei [ 1 ] believes that for the arima model and the bp neural network model of predictable tourist numbers, the simulation prediction of the arima model is better overall, while the bp neural network model is relatively less. perhaps, kong chaoli [ 2 ] believes that with the deterministic factor decomposition model decomposition, the arima model preview effect is better. liu ruoyu and liu libo [ 3 ] believe that compared with regression analysis, which needs basic function model for prediction and estimation, human factors have less influence on time series processing in arima model. therefore, according to the traditional regression analysis method, the arima model has better precision. it can be seen that in the process of prediction, many researches do not involve the unit root test and white noise residual test, which leads to low reliability [ 4 ]. based on the above research, this paper uses arima model system and eviews statistical software to process and analyze the number of tourists to lanzhou during 20092019, and make short-term forecast.

17) please note that the output of the application in the show report window should not be used to make decisions about your data. and that your data to be used for analytic should be stored in a database. this leads to the question: do we need to control for the variables that could be classified under “size” so that we can understand the true relationship between all the other variables and the returns (in the sense of the difference between a high and a low market return)?when we have more than one variable that we could explain the trends in an investment return, is it necessary to create a control for each variable?to answer to the above questions, a dummy variable is set up when there are more than one variables, as the first variable i set up dummy variable to control the size variable (num_shares_to_manipulate), so in the dummy variable of size, the dummy variable is set to 1 when the num_shares_to_manipulate value is 1, and 0 otherwise. is it necessary to use dummy variable for the size and other control variables ( num_shares_to_manipulate, size, debt_ratio, roe, year ), i set up dummy variables to control the other variables which are not in the size (i.e. roe and debt_ratio), you can find how many dummy variables you need to create, with one dummy variable or more by calculating the correlations among the variables that you want to control.and you have not figured out that other variables have already been controlled.the second question is also answered, when we have more than one variable, we don’t need to create a dummy variable for each of them, instead we should adjust the regressions model using a large sample of dummy variables, the following piece of advice is an example:we also calculate the matrix of the correlation between variables (i. the correlation between the variables and dummy variables of each variable). the matrix of the correlation is basically used to make sure that they don’t cross. just think of them as a matrix of numbers that has been multiplied with the variables of the sample that you need to control, so if the number one on the first row is multiplied with our sample, that would be multiplied with our sample, and if the number one on the first column is multiplied with our sample, that would be multiplied with our sample, however, the remainder of them (if there are other variables in the matrix) are not multiplied with our sample. instead, it can be used to eliminate variables that would result in negative correlations with the variables of our sample. if a variable is in the matrix, it indicates that it has a strong negative correlation with the variables of our sample, so it can be controlled by the matrix.in this case, however, because the correlation of each variable of our sample is at least 0.99, and the correlation of the variables of our sample and dummy variables (which should be 0) is also at least 0.99, there will be no problems with the correlation.in conclusion, if there is no problem about controlling for size, we don’t need to create a dummy variable, but if there is, we should create a dummy variable. to create dummy variables, please refer to the table 2 below which shows the correlation between variables. 5ec8ef588b