Introduction to Microfit 4 for Windows Microfit 4 for Windows is an econometrics computer package. Its purpose is the estimation and testing of regression equations. The Windows version of this package is the successor to three earlier DOS versions, Microfit 286, Microfit 386 and Microfit 4.The package takes a lot of the drudgery out of econometrics and should be used regularly as part of the course. In particular, the various econometrics textbooks provide exercises at the end of each chapter, which can easily be completed using Microfit. Only by using the package will it become familiar.In the next 3 or 4 sessions Microfit will be introduced by example. A number of regression equations will be estimated. The fist and most detailed example will be described in these notes, and involves estimating a simple bivariate regression. In the initial stages of the course, the examples can be used as templates for the computing components of your own work. Obtaining estimates of a regression equation using Microfit is easy. So easy that it is possible to forget that the primary concern is economic theory. An econometric model is set up to test economic theory against real data. Microfit is simply a tool used to estimate the parameters of a chosen econometrics model and to establish the reliability of the estimates.There are a large number of facilities in Microfit, which are beyond the scope of the course. However, an understanding of the main facilities will provide a basis for independent investigation of the more complicated features of the package. Interpreting the results, testing assumptions and correcting for violations of those assumptions are the topics that will be covered in the econometrics course.Data saved to the hard disk of the machines may be deleted without warning. Save output from Microfit to 3.5” High-Density floppy disks.The example is taken from Basic Econometrics, 3rd edition, D. Gujarati, McGraw Hill Int, 1995, question 3.17 page 90 and investigates the relationship between the level of unemployment and the quit rate.The first step is to state the hypothesis being tested. The quit rate (employees voluntarily leaving their jobs) is expected to be inversely related to the level of unemployment in the country as a

whole. If the level of unemployment is high, employees will be less certain of finding alternative employment, encouraging them to stay in their existing jobs. Conversely, if the level of unemployment is low, alternative employment will be more readily available, and the temptation to leave existing jobs greater.Secondly, an econometric model of the hypothesis is needed. Assuming the quit rate is linearly related to the employment rate, a single equation econometric model describing the hypothesis would be as follows:

= + +

wherethe dependent variable Y=quit rate andthe independent variable X=unemployment rate.and the disturbance term u makes the relationship stochastic rather than deterministic.The model is of the population; i.e. it describes the relationship that theory suggests holds between the quit rate and the unemployment rate in the population as a whole. It may also referred to as the population regression function (PRF). The disturbance term, u, introduces a random element into the equation, and can be seen as measuring the effect of variables not include specifically in the equation, accounting for measurement error or for the unpredictable factor in human actions.

The next step is to use the available date to obtain estimates of

and . A sample regression function is used to estimate the

population regression function.Y= + +

Where is an estimate of ; is an estimate of ; and e is the

residual term.For the bivariate case the estimates of the coefficients of the regression and their standard errors are given by the following equations:

The estimate of the slope coefficient, , is expressed in a number of

equivalent forms.

or or in deviation form

In deviation form, and

The standard error of the slope coefficient, :

The numerator, lower case sigma, is the constant standard deviation of the disturbance term and the denominator is in deviation form.The estimate of the intercept term, , is as follows:

and its standard error:

The constant standard deviation of the disturbance term, lower case sigma, is outside of the square root and the denominator is in deviation form.Gujarati provides the following data to obtain our estimatesYEAR Quit Rate per 100

employeesUnemployment Rate (%)

1960 1.3 6.21961 1.2 7.81962 1.4 5.81963 1.4 5.71964 1.5 5.01965 1.9 4.01966 2.6 3.2

1967 2.3 3.6

1968 2.5 3.31969 2.7 3.31970 2.1 5.61971 1.8 6.81972 2.2 5.6Source: Manpower Report of the President, 1973,Microfit can be used to complete the calculation.1) Starting Microfit.Within the Economics program group double click the Microfit icon:

The following initial screen will appear.

2) Data Entry

The first task in Microfit is to enter the data. Without any data the package cannot estimate an equation. Note that large portions of the opening screen are gray rather than black, indicating that those facilities are not available. Most become available once the data has been entered.There are 5 methods of entering data: from the keyboard, by loading an already existing Microfit data file, by loading a CSV (comma separated variables) file, as could be created by Excel, by loading an AREMOS file or by using copy and paste from the clipboard. For this example data will be entered from the keyboard. Later examples will cover the other methods of entering data.Microfit requires the dimensions of the data to be entered before any numbers. The dimensions consist of the frequency of the data (Monthly, Quarterly, Half Yearly, Annual or Undated), the period covered (e.g. 1960 to 1990) and the number of variables.To enter a new dataset make the following selection from the menu bar:

File/Open

A dialogue box appears for the dimensions of the dataset. The dimensions are entered by clicking the relevant radio buttons and filling in the boxes. The employment data is annual, from 1960 to 1972 and there are two variables, the quit rate and the unemployment rate. The completed dialogue box is as follows.

2.1 The variable Window – Variable Names and Descriptions.Having entered the dimensions of the data, the variable window appears. Variable names and descriptions are entered into the variable window. Once the data has been entered, the Variable Window is available throughout the Microfit session and is accessed by clicking the Variables button on the main screen.With only two variables it might seem superfluous to have a window devoted to keeping track of the variables, but in regression analysis it is common to create a large number of variables and this window will be used frequently.Microfit enters default names X1, X2…for the variables in the dataset. Change these for meaningful names. Names are not case sensitive, can be a maximum of 9 characters long, and cannot begin with a number or symbol. As with all computer packages, names of commands and reserved words will be rejected. A short description of the variable is a useful aid to memory. The description may be up to 80 characters. Be sure to enter the units the variable is measured in. For the example the dialogue box is as follows:

Press the Go button to continue.2.2 The Data Editor Window.Microfit uses the dimensions entered earlier to create a grid for data entry: a columnFor each variable and a row for each frequency of the data. Enter the data from the keyboard, using the arrow keys to move down the columns. Pressing enter will not move to the next cell. For our example, the completed data window is as follows:

Press the Close button to continue and control will pass to the Command Editor Window.3) Saving Data.Entering data is tedious. Having entered the data, save it immediately to avoid inadvertently losing the file and having to re-enter the data. To save the data select File/Save from the menu bar and a standard windows dialogue box for saving files appears.The file can be saved in Microfit, ASCII, CSV or AREMOS format.Microfit format create a file with suffix. FIT. The file contains the data and information on the variables, such as name and description. It does not contain the results of any regressions run or graphs created. It cannot be read by any package other than Microfit.American Standard Code for Information Interchange (ASCII) is the lowest common denominator between computer packages. Almost all computer packages will read ASCII files, but some manipulation may be necessary to ensure they are read correctly.Comma Separated Variables (CSV) files are used to read data between spreadsheets. As the name suggests, commas are used to

separate fields. This is the best format to use if moving the data from Microfit into Excel.Aremos (TSD) files are database files for the Aremos package.To change the type of file, click the arrow to the right of the Save File as Type: box and the file types listed above will appear.For the example, save the file to a floppy disk as a Microfit file with the name QUITUNEM, as in the dialogue box below. If omitted, the suffix. FIT will be attached automatically.

Microfit provides the opportunity to save a subset of the dataset. To save a subset adjust the first and last observation dates using the arrow keys to the left of the relevant boxes. In most cases the entire dataset is saved. Save the entire dataset for this example by leaving the default start and end dates and clicking the OK button.

Having saved the data, move to the command editor by clicking the Process button at the top of the screen or by pressing the Close button to the right of the screen. Close completes the process and moves to the next step. Go completes the process but the package does not move to another screen but stays in the data window.4) The Command Editor/Window.The command window is used to transform variables, add variables, graph data and complete preliminary data analysis. With practice the range of features will become familiar. In the first instance, a brief description of the screen will be useful.

As with all windows applications, the top line contains the control icon, the title bar with the name of the current dataset, and the minimize and maximize buttons. Below this is the Menu Bar. Many of the routines available through the menu bar are available directly from buttons on the window.Below the Menu Bar is the button bar. The first 5 buttons, each leading to a different window. The Variables button leads to the Variable Window in which you can change the names of variables and/or their description. The Data button moves to the data processing window where it is possible to edit the data series already entered. The Processing button leads to the Command Window, used for data transformation and graphing. The Single Button leads to the window used to estimate a single regression equation. The Multi button leads to the window used to estimate a system of regression equations.

The next two buttons are used to open a new dataset and to save the current dataset. N.B. In the process of running a regression it is likely that variables will be transformed and new variables created. These new variables are not saved to the dataset unless explicitly

done so, i.e. until the second of the two buttons is pressed.The grayed buttons are not available at this time, but are to copy, cut and paste information to the clipboard. The final button closes Microfit.There is a further bar below the button, containing three buttons used for saving the contents of the command editor, loading a previously saved set of functions/commands into the command editor and for clearing the command editor. The contents of the command window can be saved to a file and recalled later using these buttons. The file is given the extension. EQU. Next to the buttons is a description of the dimensions of the current dataset.

Below this is the actual command editor, into which functions and commands can be entered. To the right of the command editor are two pull down lists, one to all available buttons, the second of all available commands. Each function and command has a help entry describing its use. Above the pull down lists are three buttons, the GO button processes the entries in the command editor, the Help button provides help and the BATCH button is for processing batch files, lists of commands.At the bottom of the screen are 5 buttons used to create new variables. One to create an intercept term, one to create a time trend, and the final three to create various types of seasonal dummy variables.

4.1 Entering Commands and Functions into the Command Editor.A single or more commands may be entered into the command editor. Each command must be separated by a semi-colon (;). The GO button processes all commands in the command editor. Clear the command editor if the commands have been run successfully or they will be run again.To create a new variable simply enter the variable name, followed by an equal sign with an expression to the right. The expression can be constant, a variable, a function or any combination of these. The mathematical order of operation is followed, and can be changed using parenthesis(). Once a variable has been created, its name and

description is listed in the Variable Window and the actual data appears in a column in the Data Windows. It is not however saved to the file on disk until explicitly done so either by using the File/Save menu bar option or by clicking on the save button on the first toolbar.A few examples should make this clear. Try the following mathematical operators, either individually or by separating the commands by semi-colons and running all the examples at once:Operator Function Example^ Raising to a Power quitsq=quit^2* Multiplication quit2=2*quit/ Division quithalf=quit/2+ Addition quitquit=quit+quit- Subtraction quit0=quitquit-quit2

Check the Variable and Data Windows and then clear the command editor.Transformations are frequently used in Econometrics to overcome weaknesses in the assumptions. Amongst the most common transformations is taking the natural log of a variable. The inverse of the natural log of a number is raising e to the power of the log. Microfit provides the functions LOG and EXP. Try the following.logquit=log(quit);equit=exp(quit);quit3=exp(log(quit));Check the Variable and Data Windows and then clear the command editor.

Microfit provides the function SQRT to take the square root of a number.quitroot=sqrt (quit);quit3=sqrt (quit^2);Check the Variable and Data Windows and then clear the command editor.

To find other roots, raise the variable to the power of a fraction. Try the following:quitr3=quit^(1/3)

quit4=(quit^3)^(1/3)Check the Variable and Data Windows and the clear the command editor.

4.3 Creating a Unitary VariableMicrofit uses matrix manipulation to calculate a vector of the Ordinary Least Squares estimated coefficients.OLS Estimates Vector=

y is the vector of values of the dependent variable, in this case the quit rate. X is the data matrix, with a vector of data for each coefficient to be estimated. Data has been entered for the unemployment rate but no data has been entered for the intercept term. In order for Microfit to estimate the intercept term a unitary variable is required, i.e. a variable with the value 1 for each observation, a data vector of 1’s. The population regression function could have been written as follows:

where for all values of i.

The variable has to be created explicitly, its value being 1 for

each of the observations.There are two ways of creating a unitary variable from the Command Window. The first is simply to create a variable and set it equal to a constant.int=1Check the Variable and Data Windows and then clear the command editor.The second is to use the button on the bottom left of the screen marked Constant. A prompt appears for a name for the variable. Simply enter a name and click OK. Check the Variable and Data Windows to make sure it has worked.4.4 Graphing.Graphing is an essential tool in econometrics. A graph can highlight the relationship between variables, uncover any structural breaks or drastic changes in trend, and pinpoint the existence of outliers and mistakes in data entry. It is good practice to use graphs to get a feel

for the data and to learn from the data.To obtain a graph enter one of the graphing commands in the Command Window. The graph will then appear in its own window, from where it can be edited, printed, and saved in various formats.The graphing commands in Microfit are PLOT, XPLOT, SCATTER, and HIST.Try the following:The plot command plots one or a number of variables against time.PLOT QUITPLOT QUIT UNEMP

Line graph of the Quit rate and Unemployment rate against time. Note the inverse pattern.The xplot command plots up to three variables against another. This command needs at least two variables as arguments. The variable against which the others are to be plotted, the X value, must come last in the list.XPLOT QUIT UNEMP

Line graph of the quit rate against the unemployment rate.To obtain a scattergram of the data points, quit rate against unemployment, use the scatter command.

SCATTER QUIT UNEMP

The hist command creates a histogram for a single variable and superimposes a normal distribution for reference.HIST QUIT;HIST UNEMP;Each of the graphs appears in its own window. A series of 5 buttons below the title bar of the window provide options to save and edit the graph.

The first button is to send the graph to a printer connected to the computer. The second is to save the graph to a file in one of two graphics formats, as a bitmap (BMP) or as a Windows Metafile (WMF). The third is to save it as a Microfit Graph, which could the be read back into Microfit, but not into any other package. The fourth is to save it to the clipboard, from where it could be pasted into a word processor such as Word. Finally, the 5th opens a dialogue box where a vast array of graph types and formatting facilities has been assembled. The help from this box in only for this graphing tool, not for Microfit in general.5.Estimating the Regression Coefficients.Having entered the data and created a unitary variable, all the necessary variables are defined(QUIT, UNEMP and INT) to estimate the regression coefficients. From the command window, select the Single button directly below the menu bar and an estimation window appears.To specify the regression equation that is to be estimated, enter the dependent variable name first, followed by the names of the independent variables. For the example, the hypothesis is that the quit rate is affected by the general rate of unemployment, and that the relationship is linear. The dependent variables are QUIT and the independent variables are INT for the intercept term and UNEMP for

the unemployment rate. The completed window looks as follows:

Note, the three buttons below the title bar.

The first is used for saving the contents of the editor box into a file with the suffix .LST. The second reads a previously saved .LST file into the editor box. The third clears the contents of the editor box. The method of estimation, Ordinary Least Square, is displayed to the right of the buttons. Below this two drop down lists provide the possibility of using a subset of the data for the estimation. The available variables for the estimation are provided in a third drop down list. Press the START button to continue.6. The Results.A results window appears containing two tables. The features of the results window are described in the following section, Printing, Saving and Exporting Information from Microfit. This section will describe the actual results.The results can be broken into two parts. The first, itself broken up into three sections, displays the outcome of the Ordinary Least Squares Estimation and a number of associated statistics. The

second displays the results of 4 diagnostic tests of the assumptions of the Classical Linear Regression Model (CLRM).

The statistic should become more familiar as the course continues. References to the textbook Basic Econometrics, by D.Gujarati, third edition, McGraw Hill Inc. N.Y. are included.In the first section the results screen the estimation process and the underlying data are described.1) The method of estimation appears as a title, in this case Ordinary

Least Squares.2) The dependent variable, in this case the quit rate or variable

QUIT appears next.3) The number of observations used for the estimation, generally

referred to as n, and the estimation period is displayed. For our example, 13 observations were used and the estimation period was 1960 to 1972.

In the second section information on the estimated are displayed.4) The names of the variables, the estimated values of the

coefficients and the standard errors of the estimated coefficients appear in columns.

5) The final two columns are used to test the significance of the estimated coefficients. T-ratios and significance levels for the null hypothesis that the true value of the estimated coefficient is 0, against the alternative hypothesis that the true of value of the coefficients is not equal to 0 are displayed.

In the third section a number of statistics are displayed.6) The coefficient of determination, R-squared, the adjusted

coefficient of determination, R-bar-squared, and standard error of the regression, are provided as general measures of the goodness of fit. (see Gujarati section 3.5 for a description of the coefficient of determination in a bivariate regression and page 71 for the standard error of the regression equation)

7) The F-statistic is used to test the overall significance of the explanatory variables in a multiple regression. It can be used to test the null hypothesis that the coefficient of determination is 0. (See Gujarati section 8.5).

8) The mean and standard deviation of the dependent or Y variable provide measures of spread and location of the dependent variable.

9) The residual sum of squares of the regression is , the

sum of the squared differences between the actual values of Y and the estimated values of Y. It is this equation that is minimized to obtain estimates of the regression coefficients. The value of the RSS is also used in the calculation of a number of other statistics, including the coefficient of determination.

10) Maximum likelihood estimation is an alternative method of estimation to Ordinary Least Squares or moment estimation. It involves maximizing the joint probability of obtaining the values of Y in the sample, given the data is generated by a specific probability distribution. The equation log-likelihood statistic provides the maximum value of the log of the likelihood function. (See Gujarati Appendix 4A)

11)The Akaike Info Criterion and the Schwarz Bayesian Criterion are used as indicators in model selection.

12)The Durbin Watson d-statistic is a test for first order autocorrelation. If there were lagged values of the dependent variable in the regression the Durbin Watson d- statistic would not be an appropriate test for first order autocorrelation. However, if the dependent variable were lagged one time period and included in the regression equation in the form Y(-1) the Durbin Watson h-statistic would be generated. (See Chapter 12 in Gujarati).

The second part of the results contains 4 test statistics, each generated in two forms, the LM version following a chi-squared distribution and the F version following an F distribution. The test statistics are designed to check some of the assumptions of the

classical linear regression model.

The first test is the Breusch Godfrey test of higher order autocorrelation (see Gujarati, page 425-427). The null hypothesis is that there is no autocorrelation. The value of the test statistic is displayed in the LM version and the F Version, with the associated significance probability (SP) in brackets. If the null hypothesis were to be tested at the .05 level of significance and the value in the brackets after the test statistic were less than .05, then the null hypothesis would be rejected. For the LM version of the test in the example, SP=.013, which is less than .05, so the null hypothesis of no autocorrelation is rejected.The second test is of specification error, functional form or model specification. It is the Ramsey RESET test (see Gujarati pages 464 to 467). The null hypothesis is that the model is correctly specified. Rejecting the null hypothesis suggests the model is misspecfied. For the LM version of the test in the example, the sp=. 259, so there is no evidence to reject the null hypothesis.The third test is the Jarque-Bera test of normality. (See Gujarati pages 143 to 144). The null hypothesis is that the residuals of the regression are normally distributed. Rejecting the null hypothesis suggests that the normality assumption of the CLRM has broached. For the LM version of the test in the example, the SP=. 547, so there is no evidence to reject the null hypothesis.The fourth test is of heteroskedasticity. It is obtained by regressing the square of the residuals against the square of the fitted values and testing the significance of the overall equation. The squared fitted values are introduced as a proxy for all the explanatory variables. If the value of the disturbance term is determined by the

explanatory variables, the problem of heteroskedasticity occurs. The null hypothesis is that there is no heteroskedasticity. Rejecting the null hypothesis suggests there is a problem. In the LM version of the test in the example, the SP=.181 so there is no evidence to reject the null hypothesis.5.1 Interpretation of the ResultsAssume that the assumptions of the Classical Linear Regression Model have been met. The first step would be to construct the regression equation given the estimates. QUIT= 3.3663*INT -.28621*UNEMPThe sign of the slope coefficient agrees with the hypothesis that an increase in the level of unemployment will result in a decrease in the quit rate. The slope coefficient measures the estimated average effect of a unit change of the independent (X) variable on the dependent (Y) variable. In this case the estimates suggest that a unit increase in the level of unemployment will result on average in a fall of approximately .29 of a percentage point in the quit rate.The t-tests and the significance probabilities are used to test the estimated parameters against the null hypothesis that their true, population value is 0. Adopting a .05 level of significance, the null hypothesis is rejected for both the intercept and slope term., suggesting that the estimates are different from 0 and that the level of unemployment does have an effect on the quit rate. .05>.000 for the intercept term and .05>.001 for the slope term. Note that the significance probabilities are shown to 3 decimal places. A significance probability of .000 indicated that it is less than .001. The goodness of fit statistics would be used to look at the effect of the equation as a whole in a multivariate regression. In the bivariate regression they confirm the information given by the t-statistics of the estimated coefficients.The assumptions of the CLRM should now be tested as they hold as an exception rather than a rule. The Durbin Watson d-statistic suggests there is a problem of first order positive autocorrelation. The and for a regression with one explanatory

variable and 13 observations at the.05 level of significance. The calculated dw-d statistic of 0.50329 falls between 0 and the lower level suggesting positive autocorrelation. Autocorrelation will effect the variances of the estimated coefficients and may suggest model misspecification, the wrong functional form or missing variables.

Checking the diagnostic tests in the second half of the results, the Breusch Godfrey test for serial correlation suggests that there is a problem of autocorrelation, which would have to be addressed before the results were accepted.

6. Further Tests.Having run the regression and obtained some preliminary results the next step would be to run further tests on the data. The Post Regression Menu is the gateway to further tests.

Choosing Cancel from this menu moves control to the Backtracking Menu. The Backtracking Menu provides options to re-run the regression adjusting the estimation procedure, the regression equation, and the time period for the sample on which the estimates were based.

Choosing Cancel from this menu moves control to the regression definition screen, within the main window.

8. Printing, Saving and Exporting Information from Microfit.It will be necessary to print, save or export data, graphs, results and equations from Microfit, either to incorporate into a word-processing document or to read into a spreadsheet or statistical package for further analysis. The easiest method of transferring information from Microfit is to use the windows cut and paste facility when possible, and print from the target package.

8.1 Data.There is no facility to print data directly form the Data window. An alternative is to list the data using the LIST command in the Command Editor. The results of the list command appear in their own window, with an option to print. For example,

list QUIT UNEMP

Will display the data in its own result window, with a print icon appearing on the left-hand side.Data can be saved from Microfit in various formats. The File/Save As menu option displays a standard Windows saving dialogue bow. In the bottom left hand corner of the dialogue box is a pull down menu labeled Save Files as type. The default format is a Microfit data file, which contains the data, information on the variable names and data frequency, but can only be read by Microfit. This file

format has the suffix .fit. The data can also be saved as a comma separated variables file, which Excel and other spreadsheets will read easily. This file format has the suffix .csv. The American Standard Code Information Interchange is the lowest common denominator for file transfer between computer packages. Microfit data can be saved as an ASII file, which can then be read into most packages. This file format has the suffix. dat. Microfit data can also be saved as an Aremos file, which is a database file for the Aremos package. This file format has the suffix .tsd. The most versatile forms are .csv and .dat files.Using the windows cut and paste facility is the easiest way to move data between packages. Highlight the data to be moved using the mouse or by holding down the shift key and using the arrow keys on the keyboard.In the top right corner of the screen are 6 buttons.

The first is to open an existing data file. The second is to save the current data file and opens the Save As dialogue box described above. The third is to cut the contents to the clipboard and is greyed out here indicating that option isn’t available. The fourth is to copy the contents to the clipboard. The fifth is to paste the contents of the clipboard, again not possible here. The final button is to close Microfit.Copy the highlighted to the clipboard area by pressing the fourth button and the following dialogue box appears.

Click Ok to continue and the data is copied to the clipboard, with the variable names and the variable descriptions. This can now be pasted into Excel or Word.

8.2 Graphs.Graphs appear in their own window, with a set of buttons in the top left-hand corner for printing, saving and modifying the graph.

The first button is to print the graph directly. The computer has to be linked to a printer, ideally a Hewlett Packard Postscript Printer. The second button is to save the image as a file. There are two formats of file that are available, a bitmap, suffix .bmp, or a windows meta file, suffix .wmf. Bitmap files can be read into word documents by using the Insert/Picture/From File menu bar options and selecting the bitmap file in a standard open file dialogue box. The third button is to save the graph in Microfit file format, which would allow it to be read into Microfit again. This format is not useful for transferring graphs. It has the suffix .mfw. The fourth button copies the graph to the clipboard, from where it can be pasted directly into Word. The final button provides the opportunity to edit the graph, assign titles, and change the format of the axis, lines, markers and titles.Of the options, saving to a bitmap file, button two, and saving to the clipboard, button 4, are the easiest and most reliable methods of reading a graph into Word. They can then printed as a Word document.8.3 Results.Microfit opens a results window when the output is text. For example, the estimated coefficients of a regression and the associated diagnostic tests appear in a result window. Commands

that generate text output, such as the COR command, appear in a results window.The 7 buttons at the top right of the results window can be used to print, save and export the contents of the window.

The first button is to print the contents of the window directly to a printer connected to the computer.The second and third buttons are related to an ASII output file called the report file. Once a report file is opened, any subsequent output can be saved to the file and it will be appended to any existing information. This allows one report file to be opened and numerous results to be written to it. The second button opens a report file and should be used at the beginning of the session. The third button adds the contents of the results window to the open report file. The report file has the suffix .out and as an ASII file can be read by Word directly.The third and fourth button can be used with the results of estimation. In Microfit parlance they open and append information to a model file, with suffix .mod. Unlike a report file, a model file contains the estimated equation, but none of the diagnostics. For our example, the following is the contents of the model file.* QUIT =3. 3663 * INT - .28621*UNEMPThe file is an ASII format file and so can be read directly into Word and printed from there. Again, once a model file is open, subsequent models can be appended to the file by using the fifth button.The six button copies selected text to the clipboard from where it can be pasted into Word. This is the easiest way of getting information from the results window into a word-processor. Highlight the information, copy it to the clipboard, then paste it into the word processor. The layout of the output may change. To retain the layout, highlight the output in the word-processor and change the font to New Counter8.The final button changes the font of the text in the result window. Unfortunately this does not carry over to copied information.8.4 The Regression Specification.The regression equation is entered into an editor box. The contents of that box can be saved as a file, with suffix .lst and read into the

editor box again if you wished to rerun the regression. This can be useful when systems of equations or a complicated regression equation is being used. This is done by clicking the button directly below the right hand side of the title bar (see section 5). The .lst file is an ASII text file, which can be read directly by Word.8.5 The Command Window.The contents of the command window can be read to an ASII text file with the suffix .equ. This is useful if a record of the transformation is required, either as a check. or if the process is to be repeated. This is done by clicking the button directly above the top right hand corner of the Command Window (see section 4). The .equ file is an ASII text file, which can be read directly by Word.8.6 Conclusion.Where possible, use the cut and paste facility in windows to copy the output, be it a graph, text in a results window or data, and then paste into a word-processor or spreadsheet package. The output can then be incorporated within the text of an essay or used for further analysis within a spreadsheet. The output can be printed directly from the word-processor or spreadsheet package.For reference, these are the suffixes of the files created by Microfit.Suffix Type Description

.FIT MICROFIT DATA FILE

ONLY USED BY MICROFIT

.CSV COMMA SEPARATED DATA FILE

SUITABLE FOR EXCEL

.DAT ASII TEXT DATA FILE

SUITABLE FOR MOST PACKAGES, WORD&EXCEL

.TSD AREMOS DATA FILE SUITABLE FOR AREMOS

.BMP BITMAP GRAPHICS FILE

SUITABLE FOR WORD

.WMF WINDOWS META FILE GRAPHIC FILE

SUITABLE FOR GRAPHIC

PACKAGES.MFW MICROFIT GRAPHIC

FILEONLY USED BY MICROFIT

.OUT MICROFIT REPORT FILE IN ASII TEXT

SUITABLE FOR WORD & CONTAINS FULL RESULTS OUTPUT

.MOD MICROFIT MODEL FILE IN ASII TEXT

SUITABLE FOR WORD BUT CONTAINS ONLY THE EATIMATED EQUATION

.LST MICROFIT LIST FILE IN ASII TEXT

SUITABLE FOR WORD &CONTAINS THE SPECIFICATION OF THE REGRESSION EQUATION

.EQU MICROFIT EQUATION FILE IN ASII TEXT

SUITABLE FOR WORD & CONTAINS CONTENTS OF THE COMMAND

9. To Exit MicrofitUse the control icon in the top left-hand corner, or the File/Exit menu bar option, or the close icon from the main window.

Remember, any data transformation or new variables are not automatically saved to file. The current dataset has to be explicitly saved. Andre’ Noor.

Dept . of Economics.

SOAS.