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

: Open a native gretl data file or import from other formats. See Chapter 5.
: Save the currently open native gretl data file.
: Write out the current data set in native format, with the option of using gzip data compression or storing the data in binary format. See Chapter 5.
: Write out the current data set in Comma Separated Values (CSV) format, or the formats of GNU R or GNU Octave. See Chapter 5 and also Appendix C.
: 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 5).
: See the Section called Binary databases in Chapter 5 and the Section called Creating a data file from scratch in Chapter 5.
: Initialize the built-in spreadsheet for entering data manually. See the Section called Creating a data file from scratch in Chapter 5.
: Just as it says.
: Open a window containing a record of the commands executed so far.
: 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, .
: 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.
: Quit the program. If expert mode is not selected you'll be prompted to save any unsaved work.
: Look up critical values for commonly used distributions (normal or Gaussian, t, chi-square, F and Durbin–Watson).
: 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 10.
: 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.
: 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 10.
: Start R (if it is installed on your system), and load a copy of the data set currently open in gretl. See Appendix C.
: Open a window showing the current gretl session as a set of icons. For details see the Section called The "session" concept in Chapter 4.
: Capture the last graph shown in the form of a session icon, so that it can be revisited and manipulated.
: Open a previously saved session file.
: Save the current session to file.
: Save the current session to file under a chosen name.
: pops up a window with a simple (not editable) printout of the values of the variables (either all of them or a selected subset).
: pops up a spreadsheet window where you can make changes, add new variables, and extend the number of observations. (The data matrix must remain rectangular, with the same number of observations for each series.)
: 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) and boxplots. Serves up a dialog box where you specify the variables to graph. The simplest way to fill out the dialog entry is to refer to the variables by their ID numbers (shown in the leftmost column of the main data window). Thus, having chosen the scatter plot option, an entry of "2 3" will plot variable number 2 (here, consumption) against variable number 3 (income). The last referenced variable will be on the x axis. Gnuplot is used to render the graph (except for the boxplots option).
: 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" just displays the header file information for the current data file; "Edit header" allows you to make changes to it (if you have permission to do so).
: shows a fairly full set of descriptive statistics for all variables in the data set.
: shows the pairwise correlation coefficients for the variables in the data set.
: calculates the t statistic for the null hypothesis that the population means are equal for two selected variables and shows its p-value.
: calculates the F statistic for the null hypothesis that the population variances are equal for two selected variables and shows its p-value.
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.
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.
: Select a different starting and/or ending point for the current sample, within the range of data available.
: self-explanatory.
: Impose a particular interpretation of the data in terms of frequency and starting point. This is primarily intended for use with panel data; see Chapter 6.
: 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.
: 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 10 for details on the Boolean operators that can be used.
: 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 5).
: 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.
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 5.
Opens a dialog box which enables you to fix the interpretation of a panel data set as either stacked time series or stacked cross sections (see Chapter 6).
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. 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 10. One simple example:
foo = x1 * x2will 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.
This is introduced in Chapter 2. For details on the various estimators offered under this menu please consult the Section called Estimators and tests: summary in Chapter 10 and Chapter 10 below, and/or the online help under "Help, Estimation".
Please use this as needed! It gives details on the syntax required in various dialog entries.