#EVIEWS OUTPUT INTERPRETATION PORTABLE#
#EVIEWS OUTPUT INTERPRETATION SERIES#

XLSM), Gauss Dataset files, SASВ® Transport files, SPSS native and portable files, Stata files, TableauВ®, raw formatted ASCII text or binary files, HTML, or ODBC databases and queries (ODBC support is provided only in the Enterprise Edition). Convert data between EViews and various spreadsheet, statistical, and database formats, including (but not limited to): Microsoft AccessВ® and ExcelВ® files (including.EViews native, disk-based databases provide powerful query features and integration with EViews workfiles.

Support for complex data structures including regular dated data, irregular dated data, cross-section data with observation identifiers, dated, and undated panel data.

Samples and sample objects facilitate processing on subsets of data.

Powerful language for expression handling and transforming existing data using operators and functions.

Extensive library of operators and statistical, mathematical, date and string functions.

#EVIEWS OUTPUT INTERPRETATION SERIES#

Numeric, alphanumeric (string), and date series value labels.While we can't possibly list everything, the following list offers a glimpse at the important EViews features: In this case, you can conclude that the residuals are independent.EViews offers a extensive array of powerful features for data handling, statistics and econometric analysis, forecasting and simulation, data presentation, and programming. These correlations are usually caused by random error instead and are not a sign that the assumption is not met. However, you may see 1 or 2 significant correlations at higher order lags that are not seasonal lags. Autocorrelation function of the residuals If no significant correlations are present, you can conclude that the residuals are independent. If the p-value is greater than the significance level, you can conclude that the residuals are independent and that the model meets the assumption. Usually, a significance level (denoted as О± or alpha) of 0.05 works well. Ljung-Box chi-square statistics To determine whether the residuals are independent, compare the p-value to the significance level for each chi square statistic.

If the assumption is not met, the model may not fit the data and you should use caution when you interpret the results. Use the Ljung-Box chi-square statistics and the autocorrelation function of the residuals to determine whether the model meets the assumptions that the residuals are independent. You may want to refit the model without the term. P-value > О±: The term is not statistically significant If the p-value is greater than the significance level, you cannot conclude that the coefficient is statistically significant. P-value в‰¤ О±: The term is statistically significant If the p-value is less than or equal to the significance level, you can conclude that the coefficient is statistically significant. A significance level of 0.05 indicates a 5% risk of concluding that the term is not significantly different from 0 when it is significantly different from 0. The null hypothesis is that the term is not significantly different from 0, which indicates that no association exists between the term and the response. To determine whether the association between the response and each term in the model is statistically significant, compare the p-value for the term to your significance level to assess the null hypothesis.

Eviews output interpretation