Re: Eviews 7 Serial Number Generator 202
Macabeo Eastman
EViews includes an extensive library of functions for working with data. In addition to standard mathematical and trigonometric functions, EViews provides functions for descriptive statistics, cumulative and moving statistics, by-group statistics, special functions, specialized date and time series operations, workfile, value map, and financial calculations.
EViews also provides random number generators (Knuth, L'Ecuyer or Mersenne-Twister), density functions and cumulative distribution functions for eighteen different distributions.These may be used in generating new series, or in calculating scalar and matrix expressions.
EViews' powerful tools for expression handling mean that you can use expressions virtually anywhere you would use a series. You don't have to create new variables to work with the logarithm of Y, the moving average of W, or the ratio of X to Y (or any other valid expression). Instead, you can use the expression in computing descriptive statistics, as part of an equation or model specification, or in constructing graphs.
When you forecast using an equation with an expression for the dependent variable, EViews will (if possible) allow you to forecast the underlying dependent variable and will adjust the estimated confidence interval accordingly. For example, if the dependent variable is specified as LOG(G), you can elect to forecast either the log or the level of G, and to compute the appropriate, possibly asymmetric, confidence interval.
Link objects allow you to create series that link to data contained in other workfiles or workfile pages. Links allow you to combine data at different frequencies, or match merge in data from a summary page into an individual page such that the data is dynamically updated whenever the underlying data change. Similarly, within a workfile, formulas can be assigned to data series so that the data series are automatically recalculated whenever the underlying data is modified.
Value labels (e.g., "High", "Med", "Low", corresponding to 2, 1, 0) may be applied to numeric or alpha series so that categorical data can be displayed with meaningful labels. Built-in functions allow you to work with either the underlying or the mapped values when performing calculations.
In addition to numerical data, an EViews workfile can also contain alphanumeric (character string) data, and series containing dates, all of which may be manipulated using an extensive library of functions.
EViews also provides a wide range of tools for working with datasets (workfiles), data including the ability to combine series by complex match merge criteria and workfile procedures for changing the structure of your data: join, append, subset, resize, sort, and reshape (stack and unstack). EViews workfiles can be highly structured. Alphanumeric and date data are fully supported. File Import and Export
Exchanging data with other programs is easy, since EViews reads and writes over 20 popular data formats (including Excel, formatted and unformatted ASCII/Text, SPSS, SAS (transport), Stata, SPSS, Html, Microsoft Access, Gauss Dataset, Rats, GiveWin/PC Give, TSP, Aremos, dBase, Lotus, and binary files). Simply drag-and-drop your foreign file onto EViews and your data will automatically appear in an EViews workfile. Or use the easy-to-use dialogs and wizards to cutomize the importing of your data.
EViews provides sophisticated built-in database features. An EViews database is a collection of EViews objects maintained in a single file on disk. It need not be loaded into memory in order to access an object inside it, and the objects in the database are not restricted to being of a single frequency or range. EViews databases offer powerful query features which can be used to search through the database for a particular series or select a set of series with a common property.
Series contained in EViews databases may be copied (fetched) into a workfile, or they may be accessed and used by EViews procedures without being fetched into workfiles. In both cases, EViews will automatically perform frequency conversion if necessary. Automatic search capabilities allow you to specify a list of databases to be searched when a series you need cannot be found in the current workfile.
As part of the EViews Enterprise Edition (an extra cost option over EViews Standard Edition), support is provided for access to data contained in relational databases (via ODBC drivers) and to databases in a variety of proprietary formats used by commercial data and database vendors. Open Database Connectivity (ODBC) is a standard supported by many relational database systems including Oracle, Microsoft SQL Server and IBM DB2. EViews allows you to read or write entire tables from ODBC databases, or to create a new workfile from the results of a SQL query.
EViews Enterprise Edition also supports access to FAMETM format databases (both local and server based) Global Insight's DRIPro and DRIBase databanks, Haver Analytics DLX databases, Datastream, FactSet, and Moody's Economy.com. The familiar, easy-to-use EViews database interface has been extended to these data formats so that you may work with foreign databases as easily as native EViews databases.
EViews offers many options for frequency conversion, and includes support for the conversion of daily, weekly, or irregular-frequency data. Series may be assigned a preferred conversion method, allowing you to use different methods for different series without having to specify the conversion method every time a series is accessed.
Research is reproducible if its scientific computations can be replicated by an independent researcher (Stodden et al. 2014). On the other hand, interactive reports entail the ability of a computational output to reactively change with the changes in input(s). For example, the output of an input 2+2 is 4. This output is expected to change automatically to 8 when the input changes to 3+5. Modification of figures, tables, bibliography, captions and other objects becomes very easy in dynamic documents. Xie (2014) provides a detailed explanation and implementation of dynamic documents with R.
Sandve (2013) discuss ten simple rules that will ensure reproducibility of computational research. Some of these rules include avoiding manual data manipulation steps, use of version control and providing public access to scripts and results. Christensen and Miguel (2018) examines the transparency, reproducibility, and credibility of Economics research, revealing evidence of result non-replicability within the field. The interest in reproducibility of research has traversed various fields of STEM and social sciences (Ioannidis 2005; Simmons et al. 2011; see for example Franco et al. 2014; Gerber et al. 2014; Harvey et al. 2015). The aforementioned studies emphasise on the need for guidelines and solid criteria to ensure reproducibility of research. Therefore this article can help ensure replicability and reproducibility of research in the fields that employ EViews and R for their computations.
We categorise the reproducibility of research into three: 1. sharing the data and providing an easy guide on how to implement the computations 2. sharing the data, text and software code in separate files 3. sharing the data, the text and code in a single file. This paper aims to implement the third aspect of reproducibility using EViews, R, R Markdown and Quarto.
We intend to contribute to the current theme of dynamic and reproducible research as follows. We have created an R package EviewsR, which does not only integrate EViews and R, but also adds eviews as a new knit-engine for the knitr package. We also show how to create and modify EViews equation, graph, series and table objects dynamically and reproducibly. EViews code can now be embedded in R Markdown and Quarto documents so that both R and EViews users can collaborate on a single document. The package also provides R functions that could be used to 1. graph EViews series objects 2. graph an R dataframe using EViews 3. import data from external sources such as csv, xlsx as a new EViews workfile or into an existing workfile 4. create an EViews workfile from an R dataframe 5. save an EViews workfile or page as a workfile or another file format 6. execute EViews code 7. export an R dataframe as a new EViews workfile or to an existing EViews workfile 8. import EViews table object as kable 9. import EViews series objects as a dataframe or xts object 10. import EViews equation data members, graph, series and table objects 11. simulate a random walk process using EViews. We finally show how to use existing EViews workfiles in a dynamic document in order to avoid repeating time-consuming computations.
The rest of the article is structured as follows. We provide an overview of EViews, R, R Markdown and Quarto in Section ??. The description of the EviewsR package is in Section ??. We briefly explain how to use the package along with R, R Markdown and Quarto in Sections ?? and ??. Section ?? is dedicated to the implementation of dynamic document, Section ?? to the package implementation, while Section ?? covers the summary and conclusion.
RStudio is an Integrated Development Environment (IDE) for the R. It simplifies the use of the R as some of the R code can be executed via the GUI drop-down menus in RStudio4. In addition to that, RStudio works as an efficient plain text editor; it is easy and straightforward to edit text files with extensions such as bib, tex, Rmd, Rmarkdown, md, yaml and several other extensions.
R Markdown provides an easy way to write a markdown document (Allaire et al. 2020). It is available in RStudio with two alternative extensions: Rmd and Rmarkdown. It facilitates the ability to combine Markdown syntax with the syntax of R and other programming languages supported by the knitr package. Users can easily create R Markdown documents in RStudio by clicking File-> New File-> R Markdown. R Markdown documents consist of three components: metadata, text and code (Xie 2015). Metadata, also known as YAML metadata or YAML frontmatter, is written in-between a pair of three dashes. It can contain the author name, output format, title and so on (see Xie 2014, 2015, 2019)
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