Gretl Manual: Gnu Regression, Econometrics and Time-series Library
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# The main window menus

Reading left to right along the main window's menu bar, we find the File, Utilities, Session, Data, Sample, Variable, Model and Help menus.

- File menu

  - Open data: Open a native `gretl` data file or import from other formats. See Chapter 4.

  - Append data: Add data to the current working data set, from a `gretl` data file, a comma-separated values file or a spreadsheet file.

  - Save data: Save the currently open native `gretl` data file.

  - Save data as: Write out the current data set in native format, with the option of using gzip data compression. See Chapter 4.

  - Export data: Write out the current data set in Comma Separated Values (CSV) format, or the formats of GNU R or GNU Octave. See Chapter 4 and also Appendix D.

  - Clear data set: Clear the current data set out of memory. Generally you don't have to do this (since opening a new data file automatically clears the old one) but sometimes it's useful (see the Section called Creating a data file from scratch in Chapter 4).

  - Browse databases: See the Section called Binary databases in Chapter 4 and the Section called Creating a data file from scratch in Chapter 4.

  - Create data set: Initialize the built-in spreadsheet for entering data manually. See the Section called Creating a data file from scratch in Chapter 4.

  - View command log: Open a window containing a record of the commands executed so far.

  - Open command file: Open a file of `gretl` commands, either one you have created yourself or one of the practice files supplied with the package. If you want to create a command file from scratch use the next item, New command file.

  - Preferences: Set the paths to various files `gretl` needs to access. Choose the font in which gretl displays text output. Select or unselect "expert mode". (If this mode is selected various warning messages are suppressed.) Activate or suppress `gretl`'s messaging about the availability of program updates. Configure or turn on/off the main-window toolbar. See Chapter 12 for details.

  - Exit: Quit the program. If expert mode is not selected you'll be prompted to save any unsaved work.

- Utilities menu

  - Statistical tables: Look up critical values for commonly used distributions (normal or Gaussian, t, chi-square, F and Durbin–Watson).

  - p-value finder: Open a window which enables you to look up p-values from the Gaussian, t, chi-square, F or gamma distributions. See also the `pvalue` command in Chapter 13.

  - Test statistic calculator: Calculate test statistics and p-values for a range of common hypothesis tests (population mean, variance and proportion; difference of means, variances and proportions). The relevant sample statistics must be already available for entry into the dialog box. For some simple tests that take as input data series rather than pre-computed sample statistics, see "Difference of means" and "Difference of variances" under the Data menu.

  - Gretl console: Open a "console" window into which you can type commands as you would using the command-line program, `gretlcli` (as opposed to using point-and-click). See Chapter 13.

  - Start Gnu R: Start `R` (if it is installed on your system), and load a copy of the data set currently open in `gretl`. See Appendix D.

  - NIST test suite: Check the numerical accuracy of `gretl` against the reference results for linear regression made available by the (US) National Institute of Standards and Technology.

- Session menu

  - Icon view: Open a window showing the current `gretl` session as a set of icons. For details see the Section called The Session concept in Chapter 3.

  - Open: Open a previously saved session file.

  - Save: Save the current session to file.

  - Save as: Save the current session to file under a chosen name.

- Data menu

  - Display values: Pops up a window with a simple (not editable) printout of the values of the variables (either all of them or a selected subset).

  - Edit values: Pops up a spreadsheet window where you can make changes, add new variables, and extend the number of observations.

  - Sort variables: Rearrange the listing of variables in the main window, either by ID number or alphabetically by name.

  - Graph specified vars: Gives a choice between a time series plot, a regular X–Y scatter plot, an X–Y plot using impulses (vertical bars), an X–Y plot "with factor separation" (i.e. with the points colored differently depending to the value of a given dummy variable), boxplots, and a 3-D graph. Serves up a dialog box where you specify the variables to graph. See Chapter 8 for details.

  - Multiple scatterplots: Show a collection of (at most six) pairwise plots, with either a given variable on the y axis plotted against several different variables on the x axis, or several y variables plotted against a given x. May be useful for exploratory data analysis.

  - Read info, Edit info: "Read info" just displays the summary information for the current data file; "Edit info" allows you to make changes to it (if you have permission to do so).

  - Print description: Opens a window containing a full account of the current dataset, including the summary information and any specific information on each of the variables.

  - Summary statistics: Shows a fairly full set of descriptive statistics for all variables in the data set, or for selected variables.

  - Correlation matrix: Shows the pairwise correlation coefficients for all variables in the data set, or selected variables

  - Principal components: Active only if two or more variables are selected; produces a Principal Components Analysis of the selected variables.

  - Mahalonobis distances: Active only if two or more variables are selected; computes the Mahalonobis distance of each observation from the centroid of the selected set of variables.

  - Difference of means: Calculates the t statistic for the null hypothesis that the population means are equal for two selected variables and shows its p-value.

  - Difference of variances: Calculates the F statistic for the null hypothesis that the population variances are equal for two selected variables and shows its p-value.

  - Add variables: Gives a sub-menu of standard transformations of variables (logs, lags, squares, etc.) that you may wish to add to the data set. Also gives the option of adding random variables, and (for time-series data) adding seasonal dummy variables (e.g. quarterly dummy variables for quarterly data). Includes an item for seeding the program's pseudo-random number generator.

  - Add observations: Gives a dialog box in which you can choose a number of observations to add at the end of the current dataset; for use with forecasting.

  - Remove extra observations: Active only if extra observations have been added automatically in the process of forecasting; deletes these extra observations.

  - Refresh window: Sometimes `gretl` commands generate new variables. The "refresh" item ensures that the listing of variables visible in the main data window is in sync with the program's internal state.

- Sample menu

  - Set range: Select a different starting and/or ending point for the current sample, within the range of data available.

  - Restore full range: self-explanatory.

  - Dataset structure: invokes a series of dialog boxes which allow you to change the structural interpretation of the current dataset. For example, if data were read in as a cross section you can get the program to interpret them as time series or as a panel. See also Chapter 7.

  - Compact data: For time-series data of higher than annual frequency, gives you the option of compacting the data to a lower frequency, using one of four compaction methods (average, sum, start of period or end of period).

  - Define, based on dummy: Given a dummy (indicator) variable with values 0 or 1, this drops from the current sample all observations for which the dummy variable has value 0.

  - Restrict, based on criterion: Similar to the item above, except that you don't need a pre-defined variable: you supply a Boolean expression (e.g. `sqft > 1400`) and the sample is restricted to observations satisfying that condition. See the help for `genr` in Chapter 13 for details on the Boolean operators that can be used.

  - Drop all obs with missing values: Drop from the current sample all observations for which at least one variable has a missing value (see the Section called Missing data values in Chapter 4).

  - Count missing values: Give a report on observations where data values are missing. May be useful in examining a panel data set, where it's quite common to encounter missing values.

  - Set missing value code: Set a numerical value that will be interpreted as "missing" or "not available".

  - Add case markers: Prompts for the name of a text file containing "case markers" (short strings identifying the individual observations) and adds this information to the data set. See Chapter 4.

  - Remove case markers: Active only if the dataset has case markers indentifying the observations; removes these case markers.

  - Restructure panel: Allows the conversion of a panel data set in stacked cross-section form into stacked time series or vice versa. (Unlike the Dataset structure menu item above, this one actually changes the organization of the data.)

  - Transpose data: Turn each observation into a variable and vice versa (or in other words, each row of the data matrix becomes a column in the modified data matrix); can be useful with imported data that have been read in "sideways".

- Variable menu Most items under here operate on a single variable at a time. The "active" variable is set by highlighting it (clicking on its row) in the main data window. Most options will be self-explanatory. Note that you can rename a variable and can edit its descriptive label under "Edit attributes". You can also "Define a new variable" via a formula (e.g. involving some function of one or more existing variables). For the syntax of such formulae, look at the online help for "Generate variable syntax" or see the `genr` command in Chapter 13. One simple example:

      	    foo = x1 * x2

  will create a new variable `foo` as the product of the existing variables `x1` and `x2`. In these formulae, variables must be referenced by name, not number.

- Model menu For details on the various estimators offered under this menu please consult the Section called Estimators and tests: summary in Chapter 13 and Chapter 13 below, and/or the online help under "Help, Estimation". Also see Chapter 9 regarding the estimation of nonlinear models.

- Help menu Please use this as needed! It gives details on the syntax required in various dialog entries.

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