EViews is a useful and specific econometric as well as research software which will offer educational, governmental, non-governmental companies, learners and others some highly effective mathematical resources. It will also let you use estimating and developing tools. Moreover, EViews includes a user-friendly interface which will allow you to use this application easily. Its simple enough as anyone having even lower knowledge about development languages can use it effectively. Furthermore, You will observe object oriented design with a full set of components. EViews can easily read, create large amounts of various data types. Calculate as well as reproduce econometric designs on best results. While concluding, we can say EViews is an easy to use and easy to learn application with best mathematical resources.
EViews supports a wide range of basic statistical analyses, encompassing everything from simple descriptive statistics to parametric and nonparametric hypothesis tests. Basic descriptive statistics are quickly and easily computed over an entire sample, by a categorization based on one or more variables, or by cross-section or period in panel or pooled data. Hypothesis tests on mean, median and variance may be carried out, including testing against specific values, testing for equality between series, or testing for equality within a single series when classified by other variables (allowing you to perform one-way ANOVA). Tools for covariance and factor analysis allow you to examine the relationships between variables.
You can visualize the distribution of your data using histograms, theoretical distribution, kernel density, or cumulative distribution, survivor, and quantile plots. QQ-plots (quantile-quantile plots) may be used to compare the distribution of a pair of series, or the distribution of a single series against a variety of theoretical distributions. You can even perform Kolmogorov-Smirnov, Liliefors, Cramer von Mises, and Anderson-Darling tests to see whether your series is distributed normally, or whether it comes from another distribution such as an exponential, extreme value, logistic, chi-square, Weibull, or gamma distribution. EViews also produces scatter plots with curve fitting using ordinary, transformation, kernel, and nearest neighbor regression. Bubble plots allow you to use a third series to determine the size of the dots in a scatter plot.
Explore the time series properties of your data with tools ranging from simple autocorrelation plots to frequency filters to Q-statistics to unit root tests. EViews provides autocorrelation and partial autocorrelation functions, Q-statistics, and cross-correlation functions, as well as unit root tests (ADF, Phillips-Perron, KPSS, DFGLS, ERS, or Ng-Perron for single time series and Levin-Lin-Chu, Breitung, Im-Pesaran-Shin, Fisher, or Hadri for panel data), cointegration tests (Johansen with MacKinnon-Haug-Michelis critical values and p-values for ordinary data, and Pedroni, Kao, or Fisher for panel data), causality, and independence tests.
EViews also provides easy-to-use front-end support for the U.S. Census Bureau’s X-13 Seasonal Adjustment programs and MoveReg for weekly data from the U.S. Bureau of Labor Statistics. STL Decomposition provides seasonal adjustment for any frequency data, and simple seasonal adjustment using additive and multiplicative difference methods is also supported in EViews. You can even use EViews to compute trends and cycles from time series data using the Hodrick-Prescott, Baxter-King, Christiano-Fitzgerald fixed length and Christiano-Fitzgerald asymmetric full sample band-pass (frequency) filters.
EViews features a wide variety of tools designed to facilitate working with both panel or pooled/time series-cross section data. Define panel structures with virtually no limit on the number of cross-sections or groups, or on the number of periods or observations in a group. Dated or undated, balanced or unbalanced, and regular or irregular frequency panel data sets are all handled naturally within the EViews framework. Data structure tools facilitate transforming your data from stacked (panel) to unstacked (pooled) formats, and back again. Smart links, auto series, and data extraction tools, allow you to slice, match merge, frequency convert, and summarize your data with ease.
Support for basic longitudinal data analysis ranges from convenient by-group and by-period statistics, tests, and graphs, to sophisticated panel unit root (Levin-Lin-Chu, Breitung, Im-Pesaran-Shin, or Fisher) and cointegration diagnostics (Pedroni (2004), Pedroni (1999), and Kao, or the Fisher-type test). Specialized tools for displaying panel data graphs allow you to view stacked, individual, or summary displays. Display line graphs of each graph in a single graph frame or in individual frames. Or show summary statistics for the panel data taken across cross-sections, with mean (or median) and standard deviation (or quantile) bands.
EViews allows you to choose from a full set of basic single equation estimators including: ordinary and nonlinear least squares (multiple regression), weighted least squares, two-stage least squares (instrumental variables), quantile regression (including least absolute deviations estimation), and stepwise linear regression. Weighted estimation is available for all of these techniques. Specifications may include polynomial lag structures on any number of independent variables.
EViews also offers powerful tools for analyzing systems of equations. You may use EViews to estimation of both linear and nonlinear systems of equations by OLS, two-stage least squares, seemingly unrelated regression, three-stage least squares, GMM, and FIML. The system may contain cross equation restrictions and in most cases, autoregressive errors of any order.
For custom analysis, EViews’ easy-to-use likelihood object permits estimation of user-specified maximum likelihood models. You simply provide standard EViews expressions to describe the log likelihood contributions for each observation in your sample, set coefficient starting values, and EViews will do the rest.
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