EViews Assignment Homework Help

EViews a statistical package for Windows, used mainly for time-series oriented econometric analysis. It is developed by Quantitative Micro Software (QMS), now a part of IHS. Version 1.0 was released in March 1994, and replaced MicroTSP. The TSP software and programming language had been originally developed by Robert Hall in 1965. The current version of EViews is 9.0, released in March 2015. EViews combines spreadsheet and relational database technology with the traditional tasks found in statistical software, and uses a Windows GUI. This is combined with programming which displays limited object orientation.


We with our group of highly qualified, trained & certified experts strive round the clock to meet the client’s requirements with utmost quality and perfection. With a vision of raising the standards of the Eviews projects & assignments, and removing the bars of grades, we are here 24x7. Our Experts have the capability to write the content on any referencing styles, while delivering all the projects & assignments are accompanied by substantiation documentation that helps the students in viva voce as well as in making the presentations over the topic. Our case analysis is faultless in execution and is as per the standards of most highly rated universities of the world.  


 Following is the list of comprehensive topics in which we offer help with Assignment, Homework and Project:

  • ARMA
  • Creating Specialized Graphs
  • Data analysis and evaluation
  • Data Transformations
  • Date Dummies, Trends and Seasonality
  • Entering Data from a Spreadsheet
  • Forecasting with EViews
  • Frequency Conversion Between Time Series Files
  • Graphical Presentations
  • Heteroskedasticity and Autocorrelation
  • In-sample/out-of-sample forecasting techniques
  • Multiple Regression Model
  • Panel Data Frequency Conversion
  • Response function analysis
  • Serial Correlation and ARMA modeling
  • Simple Time Series Regressions
  • Sociological behavior
  • State space models
  • Statistical forecasting
  • VAR and causality
  • Variance decomposition
  • Volatility forecasting
  • Working with data series

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