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Data comes from various sources. You may use other standard tools and programs including spreadsheet programs such as Excel when working with data. RunGEM provides a Windows environment for carrying out simulations with a fixed model. Users with little experience of modelling can carry out simulations by choosing just the closure and shocks. RunDynam and variants including RunGDyn are sold as as a separate product. These are programs used for specialised tasks, usually by power users.

SUMEQ can also be used to diagnose homogeneity problems with a model — see chapter SEENV is for working with Environment files which store the set of exogenous and endogenous variables in one closure for a model — see chapter These are:. New users often ask us "Why are there so many different files? In our experience, some users are keen to have detailed information about this topic while others are not very interested. Since a detailed knowledge about this topic is not essential for doing effective modelling, you should feel free to skip this section or to skim it very quickly.

You can always refer back to it later on, if and when you need to. A table summarising the different files is given in section 3. We begin with the most important files, namely TABLO Input files, data files, Command files and Solution files, all of which you have met earlier in this chapter.

These contain the theory equations etc for a model. Alternatively they may be for data manipulation. These files have suffix. The program TABmate see section Data files. These may be Header Array files or text files. The suffix is not prescribed by the software, though suffix. HAR is recommended. DAT is sometimes used. Data files can be inputs to a simulation the original input-output data, the parameters and updated versions are output from a simulation.

Updated data files are usually given the suffix. Chapter 6 below contains an introduction to the different ways in which you can create data files and modify data on existing data files. Command files. These contain the details of a simulation, including closure, shocks, starting data and solution method see section 3. The suffix is not prescribed by the software, though.

CMF is usual The statements allowed in Command files are documented in chapter Solution files. These are the main outputs from a simulation. They contain the change or percentage change results for all the linearized variables. They may also contain levels results. Solution files are documented in chapter Equations files. These contain numerical versions of the linearized equations of a model. They are usually only produced when you wish to use SAGEM to obtain several Johansen approximate solutions of a model, as explained in chapter You may also create an Equations file if your model is not homogeneous in prices or quantities see section Equations files have suffix.

Equations files are documented in chapter Shock files. Sometimes it is necessary to shock the different components of a variable by different amounts. If the variable has a large number of components, or if the shocks are calculated by a program, it is convenient to put the numerical values of the shocks onto a so-called "shocks file". This file may be a text file or a Header Array file. The suffix for shocks files is not prescribed by the software, though often. SHK is used. The use of shock files is documented in sections 24 to Solution Coefficients SLC files.

These are output from a simulation. SLC and have the same name apart from suffix as the Solution file from the simulation SLC files are documented in section Extrapolation Accuracy files. You can ask 26 for an Extrapolation Accuracy file to be produced when you extrapolate from 3 separate multi-step calculations as you have seen in section 3.


These text files show estimates as to how accurate the different simulation results are. They have suffix. XAC and have the same name apart from suffix as the Solution file from the simulation. Extrapolation Accuracy files are documented in section There are a number of files which are used for communication between programs. This is the Fortran. Auxiliary files. AXS and. AXT files are always produced at the same time. In Step 1 b for simulations see section 3. It is this EXE file which is run to carry out a simulation. If you need to check this output for example, to see where an error occurs , you can ask for a LOG file to be created You can then look at this log file in your favourite text editor.

The suffix for LOG files is not prescribed by the software, though suffix. LOG is usual. The program would ask a sequence of questions — the responses were usually single letters to indicate options or filenames for input and output. Obviously it could be very tedious to repeatedly run programs in this way. Stored-input files or STI files were a way to reduce the drudgery — they consist of a text file which stores the necessary responses to the questions that the program asked. To produce report3. STI files are rather hard to read and understand — you only see one half of a conversation.

For new models you can and should specify condensation actions — such as Omit, Substitute and BackSolve — within the TAB file, but this option is relatively new, so many older models still use a STI file to store the condensation. Stored-input files are further described in section They usually have suffix.

STI although other suffixes may be used. You will need to learn about STI files as you become more experienced. You can find introductory examples of creating and working with them in sections A number of files can be created in order to speed up or simplify subsequent tasks.

Examples are Stored-input files see section 3. Many programs create and use working files while they are running. These work files can be large. Usually these work files are deleted when the program finishes so you do not see them or need to know about them. Occasionally these files are left around on your hard disk if the program finishes with an error. See below or section Especially when running multiperiod simulations, GEMPACK tends to create many output files — most of which will not be needed in a few days time. A list of these 'junk files' appear below.

From a command prompt in your working folder, type:. Alternatively, TABmate's Tools.. Delete Junk files menu item offers another way to cull files. You have just learnt the most important things about GEMPACK, namely how to set up and carry out simulations, and how to look at the results. Whatever your main interest, we strongly encourage you to at least skim chapter 4 first, and then go on to your main interest. In order to build a model within GEMPACK, it is necessary to prepare a TABLO Input file containing the equations of the model and to construct one or more data files whose purpose is essentially to give one solution of the levels equations of the model.

We illustrate each step in the process by doing it for the Stylized Johansen model. In sections 4. TABLO linearizes all levels equations in TABLO Input files and converts all levels variables to the associated linear ones change or percentage change in the associated levels variables. This is described in section 4. We conclude this chapter in section 4. If you are familiar with using GAMS for general equilibrium modelling, you may prefer to work through the document Kohlhaas and Pearson instead of, or before, reading this chapter.

In Table 4. The letters P, X and D denote prices, quantities and dollar values respectively, while the symbols A and a denote parameters. Because the first three equation blocks are identically linear in the logarithms they are natural candidates for presentation and explanation of the model. TABLO Input files contain the equations of a model written down in a syntax which is very similar to ordinary algebra.

Once you have written down the equations of your model in ordinary algebra, it is a simple matter to put them into a TABLO Input file, as we illustrate in section 3. You are free to use levels or linearized versions of the equations or a mixture of these two types.

For example, if a certain dollar value D is the product of the price P and quantity Q, the levels equation is. The linearized version says that, to first order of approximation, the percentage-change in the dollar value is the sum of the percentage changes in the price and the quantity. Whichever version of the equation you include, GEMPACK can still produce accurate solutions of the underlying levels equations which are usually nonlinear.

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The best way of making the above clear is to take a concrete example, as we do below, using Stylized Johansen as our example model. We start from the equations as written down in Chapter 3 of DPPW to which we refer readers interested in the derivation of, and motivation behind, these equations. An excerpt from that chapter, SJ. The equations of the model are shown in Table 4.

In that table, both the levels and linearized versions of each equation are shown, taken essentially unchanged from DPPW 1. Notice that upper case letters for example, X denote levels quantities while lower case letters for example, x denote percentage change in the corresponding levels quantity. For our first implementation of Stylized Johansen see section 4. That is, we decided to use the levels versions of some of the equations most are accounting identities and one is the numeraire equation and the linearized versions of the top three equations which are behavioural equations.

Later, in sections 4. Of course, each of these 3 implementations is valid and all three produce the same results. Hence we use a different notation. The levels variables of the model are as follows. In DPPW subscripts 1 and 2 refer to sectors commodity or industry , subscripts 3 and 4 refer to factors 3 is labor and 4 is capital while subscript 0 refers to households. Below in Table 4.

Thus it is necessary to provide data from which initial that is, pre-simulation values of all levels variables and the values of all parameters of the model can be inferred. As we shall see in section 4. Once dollar values are known, it is often possible to set basic prices equal to 1 this amounts to a choice of units for the related quantities , from which the quantities can be derived by dividing the dollar value by the price.

Any other fixed value would be as good. Because there is a Cobb-Douglas production function involved, it is well-known that these are cost shares, namely. One instance of the data required is as shown in the body of Table 3. In the TABLO Input file, the pre-simulation values of these data will be read and the values of all others will be calculated from them.

It consists of the equations written in a syntax which is very similar to ordinary algebra. It also contains a description of the data to be read, where it is to be read from, and how this data is to be used to calculate values of parameters and pre-simulation values of the other levels variables occurring in the equations. Before the equations must come. The order of these in the TABLO Input file is somewhat flexible but follows the general rule that items cannot be used until they have been declared.

Thus the SET statements saying which sets are involved usually come first. These ideas are best learnt and understood by example. You can use any text editor you are familiar with. You should only have to do this once: WinGEM should remember which editor you chose. We will discuss the details of this equation in the next section. In this subsection we consider just two equations of Stylized Johansen, namely E9 and E4 in section 4. Consider the very simple equation E9 relating prices, quantities and dollar values of household consumption.

These linear variable names are used in reporting simulation results see the results in section 3. We must also indicate how pre-simulation values of the levels variables can be read or calculated from the data base. We do this via the statements. The third of these contains the same expression as the equation we are considering.

Now consider the equation E4 "price formation for commodities". If you are using the TABmate editor, try using the Gloss button. This file is usually called SJ. You will find all the statements shown above in that file. Since declarations must come before use, you will find the TABLO statements in pretty much the reverse order from that in which we have introduced them above. Each statement ends with a semicolon ';'. Text between exclamation marks '! Text between hashes ' ' is labelling information; the positioning of this labelling information is restricted see chapter 10 for full details.

When most variables being declared are levels variables, it seems wasteful to have to keep repeating the qualifier LEVELS. If you put the statement. Of course, if most equations in your TABLO Input file are linearized ones, you could put the opposite default statement. Next comes the declaration of the single data FILE required. This file is given the logical name 'iodata'. The actual name of the file on your computer containing this data is not limited by this logical name. You can give the actual file any convenient name. HAR ;". Then come READ statements telling the program to read in initial that is, pre-simulation values of certain levels variables.

Each READ statement says from where the data is to be read that is, which file and which header on the file. Notice also the use of the syntax. As explained in section 4. The syntax of the last equation the numeraire equation may surprise you. We could have expressed this as. Complete documentation of TABLO Input files is given in chapters 8 to 18 , which you will need to consult when you start to build a new model.

Many levels variables for example, prices, quantities, dollar values are always positive and so it is natural for the associated linear VARIABLE to be a percentage change. However, when the relevant levels variable can be positive or zero or negative examples are the Balance of Trade and an ad valorem tax rate , it is wiser to specify that the associated linear VARIABLE is an ordinary change.

This is because, in such a case, if the levels value happens to be zero at the start of any step of a multi-step simulation, the associated percentage change could not be calculated since it would require division by zero. When you build a model, you have in mind the sorts of questions you will be using the model to answer. You may be thinking of holding some quantities constant and varying others. In Stylized Johansen, there are only two exogenous variables, the supplies of the primary factors labor and capital, so this issue does not arise.

The description there applies to all TABLO Input files - that is, to those containing just levels equations, just linearized ones and to those such as the one in section 4. We will introduce more information about the syntax and semantics in sections 4. We illustrate this by giving in full in section 4. This file is usually called SJLN. Advice about linearizing equations by hand can be found in section The linear variables are declared explicitly. This makes results from the 2 files easier to compare. But we could have chosen different names. Starting with these prices equal to one explains why it is sensible to take the pre-simulation values of the supplies XCOM i to be equal to the pre-simulation dollar values DVCOM i , as indicated in.

TAB shown in section 4. On any step, the old value is the value before the step and the new value is the one put on the data base updated after the step. The statement could be. Here, in the levels,. In such files,. In section TAB in section 4. The purpose of an UPDATE statement is to tell how much some part of data read changes in response to changes in the model's variables in the current step of a multi-step simulation. An introductory example was given in section 4. If, in the levels, V is equal to the product of two or more percentage-change variables, say p and q, use an UPDATE statement of the form.

In case 3 above, the expression for the change in V is obtained by linearizing the levels equation connecting V to other levels variables whose associated linear variables have been declared in the TABLO Input file. More details about Updates, including examples, can be found in section In this section we look at numerical versions of the linearized equations in SJLN. TAB section 4. Some users are keen to have detailed information about these topics; others are not. Since a detailed knowledge about these topics is not essential for doing effective modelling, you should feel free to skip these sections.

You can always refer back to them later on, if necessary. The equation is. There are really 2 equations here, one for each sector "s1", "s2". When evaluated at the base data values see Table 3. At the start of the simulation ie, on the first Euler step — see section 3. When GEMPACK solves the equations above, all the variables are put onto one side so that the equation says that some expression is equal to zero. The equations above are rewritten as. If you look at Table 3. Here we consider the 4-step Euler calculation with Stylized Johansen in which the supply of labor is increased by 10 percent and the supply of capital is fixed.

We look at the effect of the Update statements after the first step of this 4-step calculation. During the first step, the supply of labor is only increased by 2. The software solves the linear equations those in section 4. Some results from solving these equations are as follows:. As we indicated in section 3. Here we look at this for the second step of the 4-step Euler calculation discussed in section 4. The values of all Coefficients read from the data base are updated at the end of the first step of this calculation. During the second step, these Coefficients take these updated values.

The values taken during step 2 of all other Coefficients which are not Coefficient Parameter s are inferred from the relevant Formulas. Similarly for all other Coefficients. Hence the numerical linear equations solved during step 2 may be different from those solved during step 1. More details about the values used and calculated during the different steps of this 4-step calculation can be found in section You should expect the levels files to contain explicit calibration FORMULAs of the kind familiar to levels modellers for calculating the values of the parameters of these functions.

A surprise with the Cobb-Douglas specification in Stylized Johansen is that, although such parameters appear in the levels equations, we do not need to calculate their values since these parameters do not appear in the linearized equations produced by TABLO. These involve two parameters not present in the linearized versions of these equations, namely. Once this has been done, the levels equations are ignored.

Variable W j has been introduced to simplify the "intermediate demands" and "price formation" equations. When you want to build your own model, you will usually construct the TABLO Input file by modifying one from an existing model. For example, you may wish to add some equations to an existing model. Suggestions about this can be found in section 8. You will also need to write Command files for simulations.

In section 4. TABmate can be a great help in finding errors. TAB which contains some typical errors.

You will see the reason. You can see that a semi-colon is missing from the end of the previous line the end of the declaration of variable PF. To remedy this error, insert a semi-colon at the end of that line. TABmate does not immediately realise that you have fixed this error. This time it gets past the previous error but finds another error, underlining the word FACT in red and giving Unknown set as the reason for this error.

This time TABmate tells you No error found in "go-ahead" green rather than "stop" red. Now that you have removed all errors, you can return to WinGEM to continue. In this window, click on Rerun. At the first occurrence you should see something like:. Note the? The reason "Expected ;. To fix the error, you would need to add a semi-colon at the end of this statement in sjerror. Here the? The reason is "Unknown set". Again this needs to be corrected in sjerror.

PF is unknown because of the first error above where the semi-colon being omitted means that TABLO did not understand the statement declaring variable PF. We call this a consequential error since it is only an error because of an earlier error. We refer to errors identified at this stage as syntax errors in the Command file. If you have a syntax error in the Command file for example, do not spell one of the keywords correctly , the program stops with an error as soon as the whole Command file is processed in this way.

Example 1 below is an example of a syntax error. If there are no syntax errors, the program begins the simulation. Other errors in the Command file can be indicated later during the simulation. For example, you may not have a valid closure, or you may read shocks from a text file which does not have the expected form. In these cases, the error message may not refer explicitly to the Command file. Look at the Log file to identify the error.

You will need to read the error message and interpret it. Example 2 below is an example of this kind. The run should end with an error. You should see something like the following. The error in the example above is because the statement. To fix it remove the exclamation mark at the start of the line. In general, once you have identified the source of the error, edit the Command file to fix this error and rerun the simulation.

Following the error, there is a trace-back string of subroutines. If you need help with an error, it will be helpful if you save the Log file and send it to us when you report the problem. This chapter contains an introduction to Header Array files and to the programs which can be used to create or modify them section 5.

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Header Array files contain one or more arrays each containing data values. An individual array of data on a HAR file is accessed by supplying the unique 4-character identifier or Header for that array of values. In addition to its header, each array of data has an associated long name up to 70 characters long which can contain a description of the data in the array. Each array can have set and element labelling which indicates, for example, the names of the commodities associated with each number — see section 5. Header Array files are binary files that cannot be viewed or edited using normal text editors.

The data is encoded in binary form to keep the size of the file small. You need to use a special program, such as ViewHAR, to examine or modify such files. Header Array files are binary files so they cannot be printed or edited directly. These include. ViewHAR has been introduced in chapter 3 above — further details can be found below. The data values held on an individual array can be either all real numbers, all integer numbers or all character strings.

Depending on the type of data that is to be stored, the number of dimensions allowed varies. Headers for arrays on any one file must be unique since the header acts as a label or primary key to identify the associated array. Once written, an array contains not just the data for the array itself but also self-describing data, including the type of data values, dimensions of the array and a descriptive "long name" of up to 70 characters.

Header Array files have the advantage that you can access any array just by referring to the header which uniquely identifies the array in the file. There is no need to consider here the format 1 of the arrays or any other details since they are all taken care of automatically by the software. Headers consist of up to four characters which are usually letters A to Z, a to z or digits 0 to 9.

Different arrays must have different headers. The case upper or lower of a header is not significant. Headers starting with letters 'XX' are reserved for internal program use. Each array on a Header Array file has an associated type. Arrays of integers have type 2I , arrays of character strings have type 1C. The RE type includes set and element labelling row and column labels — see section 5. Arrays of real numbers on Header Array files usually contain set and element labelling information. This set and element labelling consists of.

We refer to this labelling information as set and element information on an array. Set and element labelling information can only be attached to arrays of real numbers — not to arrays of integers or character strings. Each header has an associated Long Name which can be up to 70 characters long.

Updated data files usually have the same long names as initial data see section This History consists of several lines of text each line is limited to 60 characters which are stored on the file. You could make notes there about your file edits. The top part of the History form shows the file Creation Information. The idea is that Creation Information and History help to remind you how, when and why you created the file. If you send the file to someone else, it could tell that person useful information. When you carry out a simulation, the updated versions of any Header Array data files have History written on them, as does the Solution file.

The usual way to create header array files containing raw data is via ViewHAR. A blank zero-filled array is created in ViewHAR; then numbers from a spreadsheet are pasted into the array. ViewHAR can also modify single numbers right-click on the value. These procedures are described briefly in section 6.

Some of the possibilities are illustrated in figure 5. Usually the raw data requires considerable processing or manipulation before it can be used by a CGE model. Figure 5. Then the sequence of programs, STEP1. Then comes the often difficult and time-consuming task of assembling the actual data numbers ; we say nothing about this here. Header Array files are binary files which contain one or more arrays containing data values.

An individual array of data on a Header Array file is accessed by referring to the unique 4-character identifier known as a Header for that array of values. See chapter 5 for more details. The usual way of creating a Header Array data file is to start with data from another program or source in XLS or some text format. We illustrate this for Stylized Johansen in section 6.

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Otherwise, it may be possible to read it into a spreadsheet to create XLS source. Section 6. When you construct a data base for a model, it is important to check that it is balanced. We give some examples in section 6. We give a hands-on example and some details in section We provide a table in section 6. There are many techniques used in preparing and modifying data. In this chapter we only scratch the surface. We recommend sources of further information in section 6. You have looked at the Header Array data file SJ.

HAR for Stylized Johansen in section 3. In this section we take you through the steps to construct that file SJ. We recommend that you carry out these steps on your own computer. As you have seen in section 4. The matrices of data required are as shown in the data base in Table 3. XLS in the examples folder , as shown in the table below. To get to this mode, select from the File menu Use simplified, read-only menu. The second mode is Editing mode where you are allowed to modify data in the Header Array file.

To get to this mode, select from the File menu Use advanced, editing menu. You must be in Editing mode to carry out the operations described below. The first step is to create ViewHAR headers for these 2 sets. Type in. Press OK when you are done. You should see that the file now contains 1 header, "SEC". Fill in or choose the following options:. You should see that the file now contains a new header, CINP. Examine the values of this header — they will be all zero, but at least you have an array of the right size with the right labels.

Leave this array of zeros in view in ViewHAR. You should see the right numbers appear. Now use File Save to save your work 4 so far. Examine the values of the new FINP header — they will again be all zero. And again File Save. And yet again File Save. In the example above, we first created the sets, then used these to create blank labelled arrays. A more old-fashioned but sometimes still useful way is to first create blank un-labelled arrays, then create the sets, then attach sets to the arrays as row or column labels.

Use the Choose Dimension box in the bottom left-hand corner to select the first dimension. Then, with Choose Set drop-down list on the right-hand part of the form, select the right set. Then use the Choose Dimension box to select the second dimension, and the Choose Set drop-down list to select the right set for that. And so on, until all dimensions are labelled. You can modify a number in ViewHAR by right-clicking that number in the Data window and typing in a new value. Usually you need to increase the number of visible decimal places before you copy.

ViewHAR offers many other capabilities. You can find more information in section When you prepare the data file s for a model, you must be careful to check that all the different balancing conditions are satisfied. For example, for Stylized Johansen, costs in each industry must equal the total demand for the corresponding commodity. In ORANI-G, which allows each industry to produce several commodities, there are two balancing conditions: costs in each industry must equal the value of all outputs from that industry; and output of each commodity from several industries must equal the total demand for that commodity.

The TAB file for a well-constructed model will contain code to check that such balance conditions hold, and even assertions see section Nonetheless, it is common perhaps as part of a sequence of programs to produce a database to write a self-contained TABLO Input file to read the data and carry out the calculations to check the balancing conditions. Such a TAB file will write the results of its calculations to a file you can look at to check that values are in balance.

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TAB which is used to check the balance of a data set for Stylized Johansen. An Assertion statement see section You will also see that a check is made to count the number of negative numbers in the data base there should be none. If you prefer to use WinGEM, first make sure that both default directories point to the directory in which the Stylized Johansen files are located. Also check that there are no negatives in the data base. If you are working from the command prompt, change into the directory to which you copied the Stylized Johansen files.

Whenever you carry out a simulation, the updated data should satisfy the same balance checks as the original data — otherwise there is something wrong with the TAB file for your model. CMF see chapter 3. CMF and alter the statement. UPD is still balanced. A more sophisticated, production-quality model should include such checking code in its main TAB file. That way, checking occurs every time the model runs — so alerting you early to potential problems.

You can see an example of the use of this TAB file in section This shows a neat way of arranging summary data into headers on a Header Array file. You may be able to adapt some of these techniques in your own work. ViewHAR can be used interactively to do many conversions; but if a process is to be automated, command-line programs if available are preferred — they can be run from batch BAT scripts.

Table 6. The table rows source and columns destinations are labelled with 3-letter abbreviations, as follows:. In many cases alternative programs could be used; for example, ViewHAR can do most conversions. The table above shows the most appealing command-line program, if there is one — otherwise ViewHAR is shown.

The programs gdx2har and har2gdx see section Slightly different versions are distributed with GAMS. The programs har2csv, csv2har, har2txt, txt2har, har2xls, head2csv, head2xls and xls2head are command-line Windows-only programs. To obtain instructions for using, say, txt2har, you would simply type from the command line:.

There are many techniques used in preparing and updating data files for models. This chapter is just a very brief introduction to the topic. Suggestions for finding out more are given below. Usually the file-name suffix extension indicates the file type. Some types of files must be given system-determined suffixes. Below we list the most important type of files with system-determined suffixes see also 3. Whenever a program asks you for the name of any of these files with system-determined suffixes, you should never include the suffix in your input since the program will add the suffix automatically.

For example, in a Command file put. Similar to the. SLC Solution Coefficients file, are. AVC, and. CVL files -- see sections Examples are in the table below. In some cases there are considerable advantages from using these "usual" suffixes. SEE for its output files. We suggest that you go along with these suggestions unless you have good reasons to do otherwise. There are two basic file types on all computers — text files which are sometimes called ASCII files and binary files. Text files are more portable — they can be viewed or printed using any text editor.

Binary files are more compact and faster to process, but use one of a number of proprietary formats — so only special programs can read or create them. Although not recommended, you can also hold such data in text files: when used for this they must follow a standard format described in chapter We have listed many of the important files, and discussed their roles, in section 3.

Other files are created for users to look at or use. Some contain information which is important in reporting simulation results while others may allow experienced modellers to carry out tasks more efficiently. Directory names are restricted in the same way that file names are. Even if you are trying to create a file with a legal name, it may not be possible to create the file in a directory with an illegal name.

For example, Chinese or Scandinavian characters in a directory name may cause problems. To maintain compatibility with those:. Another virtue of the above rule is that is simpler to remember than the more complex rules above. To specify a file name containing spaces in a Command CMF file, enclose the name inside double quotes:.

If you are running a program interactively, you must not add quotes "" when specifying file names containing spaces. Similarly you should not add quotes when specifying such file names in Stored-input STI files. TAB characters are replaced by a single space. Most control characters are replaced by a single space but will cause a warning message.

The program will stop with an error message if it finds a Control-Z character before the end of the file if there is text after it, or if there are two or more in the file.

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There is no testing for other characters ASCII characters but these would cause problems if used in file names — see section 7. Section 3. Chapter 9 provides some of the fine print about running TABLO, including how it linearizes levels equations and about its Condensation stage. Chapter 10 is a full description of the syntax required in TABLO Input files while chapter 11 contains a comprehensive description of the semantics and a few points about the syntax which may not be clear from chapter 10 for the current version of TABLO.

Chapter 12 describes the TABLO statements for post-simulation processing; chapter 13 builds on this to shows how ranked sets can be used to present tables of winning and losing sectors in a simulation. Chapter 15 provides some assistance with the important task of verifying that your model is actually carrying out the calculations you intend.

In chapter 17 we give examples of ways in which the TABLO language can be used to express relationships which at first sight are difficult to capture within the syntax and semantics of TABLO Input files. Chapter 18 gives rules for linearizing levels equations, and indicates how the linearized equations are shown on the Information file. We expect that chapters 9 to 18 will be used mainly as a reference document rather than being read through in order.

Chapters 19 to 35 describe carrying out simulations on economic models after they have been implemented using TABLO. Each of these examples assumes that you wish to run an existing model, or modify slightly an existing model. You will find that TABmate see section 8. In chapter 3 the Stylized Johansen model SJ. TAB was used as the main example model. In following chapters the main example models are. TAB, you should use the Stored-input file which carries out condensation. At the Command prompt, the commands to use are. At the Command prompt, the command to use is. STI and then click on the Run button.

This model also needs a condensation STI file to run successfully. TABLO then runs in batch mode and does not expect any other input. If there are condensation actions in the TAB file, running with -pgs or -wfp will do those condensation actions but give no opportunity for other condensation actions and then go to Code stage. EXE which is running. The Options On this pass, it just counts the numbers of the different types of statements and allocates memory for the checking which follows on the second pass.

You may find that there are errors above this position in the TAB file, since TABLO has not really checked anything except line length on the preliminary pass. Code and Other menu item. You run the EXE file to carry out simulations with the model. If you wish to develop a TAB file for your own model without relying on an existing model, how do you go about it?

Chapter 4 describes how to build a new model using Stylized Johansen as a simple example. To summarise, the steps are as follows. Write down your equations in ordinary algebra. Choose whether to write a linearized, mixed or levels model. You may need to linearize your equations by hand see section Compile a list of data needed for the equations levels values, parameters, other coefficients.

Section 4. If your model is large, you may need to condense it — see section 8. The simulation process is described in chapter 3 and in more detail in chapters 19 to Unless you have a firm preference for another text editor, we recommend that you use the TABmate editor because it has many in-built functions to assist you including the following. TABmate's Tools Closure command helps you to find a standard closure for your GE model.

It can also be used to find logical errors in your model, to suggest condensation actions, or to help construct a STI file. The results of the closure analysis are contained in a text report file suffixed CLO. TABmate starts from the premise that there must be the same number of endogenous variables as equations in your model. By extension, we can usefully imagine that each equation explains a particular variable. Variables not explained by any equation are deemed to be exogenous in the standard closure. In order for TABmate to know which variable is explained by a given equation, the modeller must follow a naming convention for equations.

If you do not follow this convention, the Closure command will be no use to you, although the rest of TABmate will work normally. Use the TABmate menu command Tools Help on Tools to find out more about the Tools Closure command. However, consistency is desirable, and can be enforced using TABmate's Tools Beauty Parlour command. You can choose for, say, variables, to be rendered consistently in lower-case, or with the capitalization used when they first appeared in the TAB file.

If you have a Header Array data file containing set and element labelling see section 5. This will write some text to the Clipboard. The following text will be created. ViewHAR has done all the dull part of code writing, and you can quickly edit the code by writing in appropriate filenames, formulas, updates etc to suit your model. If your model is either too large to run on your computer, or too slow, you should consider condensation. Basically you need to consider:.

Closure command see section 8. Also very useful is the Condensation Information file: see section That is the recommended approach. Closure command will produce condensation commands that you can paste into your TAB file. The older method was to specify condensation actions in a STI or Stored-input file.

If you still need to work with the old STI file method, the examples in section The Tools Closure command will produce condensation commands that you can paste into the STI file. Using a basic STI file as a starting point, you can easily add, using your text editor, other variables to omit, substitute or backsolve. There are two versions: OG01GS. AXT using the wfp option. This chapter which you could skip during a first reading provides some additional, advanced information relating to:. There is more about it in section Option ACD is discussed in section 9. Disjointness of two sets, or of a family of sets, may be expressed in terms of intersections of pairs of them.

If a collection contains at least two sets, the condition that the collection is disjoint implies that the intersection of the whole collection is empty. However, a collection of sets may have an empty intersection without being disjoint. Additionally, while a collection of less than two sets is trivially disjoint, as there are no pairs to compare, the intersection of a collection of one set is equal to that set, which may be non-empty.

In fact, there are no two disjoint sets in this collection. Also the empty family of sets is pairwise disjoint. A Helly family is a system of sets within which the only subfamilies with empty intersections are the ones that are pairwise disjoint. For instance, the closed intervals of the real numbers form a Helly family: if a family of closed intervals has an empty intersection and is minimal i. A partition of a set X is any collection of mutually disjoint non-empty sets whose union is X. A disjoint union may mean one of two things.

Most simply, it may mean the union of sets that are disjoint. From Wikipedia, the free encyclopedia. This article is about the mathematical concept. For the data structure, see Disjoint-set data structure.