# Merge multiple datasets in stata forex

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In both datasets, id does not uniquely identify observations. The example tries , 1:m, and m:1 merge and all yield an error because of the lack of unique identifiers. Then I show that the m:m merge results are the same as those produced with the old syntax of merge. Since you don't have unique identifiers in both datasets, you have to decide how to combine observations with the same key value. The first with the first, the second with the second. What happens when the master has less observations that the using or vice versa?

Also see the eform and transform options for more information on the kinds of statistics that can be displayed. See the star suboption below if you want to attach the stars to another element. Use this to add empty cells. For example, cells "b p" ". Use the incelldelimiter option to specify the text to be printed between the combined elements the default is to print a single blank. A set of suboptions may be specified in parentheses for each element named in array except for. For example, to add significance stars to the coefficients and place the standard errors in parentheses, specify cells b star se par.

The following suboptions are available. The symbols and the values for the thresholds and the number of levels are fully customizable see the Significance stars options. If only one format is specified, it is used for all occurrences of the statistic. For example, type. If multiple formats are specified, the first format is used for the first regressor in the estimates table, the second format for the second regressor, and so on.

The last format is used for the remaining regressors if the number of regressors in the table is greater than the number of specified formats. For instance, type. Note that, regardless of the display format chosen, leading and trailing blanks are removed from the numbers. White space can be added by specifying a modelwidth see the Layout options. The default is the name of the statistic. It is also possible to specify custom "parentheses". For ci the syntax is: ci par[ l m r ] vacant string to print string if a coefficient is not in the model.

The default is to leave such cells empty. For example, the specification t keep mpg would display the t-statistics exclusively for the variable mpg. A 1 indicates that the statistic be printed; 0 indicates that it be suppressed.

For example beta pattern 1 0 1 would result in beta being reported for the first and third models, but not for the second. The default is pvalue p , indicating that the standard p-values are to be used i. Alternatively, specify pvalue mypvalue , in which case the significance stars will be determined from the values in e mypvalue. Values outside [0,1] will be ignored. A droplist comprises one or more specifications, separated by white space.

A specification can be either a parameter name e. Be sure to refer to the matched equation names, and not to the original equation names in the models, when using the equations option to match equations. Specify the relax suboption to allow droplist to contain elements for which no match can be found. This is the default. Type noomitted to drop omitted coefficients.

Type nobaselevels to drop base levels of factor variables. Note that keep does not change the the order of the coefficients. Use order to change the order of coefficients. Reordering of coefficients is performed equation by equation, unless equations are explicitly specified. Coefficients and equations that do not appear in orderlist are placed last in their original order. Extra table rows are inserted for elements in orderlist that are not found in the table.

The syntax for groups is "group" [ "group" The single groups should be enclosed in quotes unless there is only one group and name is specified. Note that name may contain spaces. For example, if some of the models contain a set of year dummies, say y1 y2 y3, specify estout The default is labels Yes No.

Use quotes if the labels include spaces, e. See the varlabels option if you are interested in relabeling coefficients after matching models and equations. The default is to match all first equations into one equation named main, if the equations have different names and match the remaining equations by name.

Specify equations "" to match all equations by name. Alternatively, specify matchlist, which has the syntax term [, term If a number, it specifies the position of the equation in the corresponding model; would indicate that equation 1 in the first model matches equation 3 in the second, which matches equation 1 in the third. A period indicates that there is no corresponding equation in the model; In syntax 2, you specify just one number, say, 1 or 2, and that is shorthand for If it is suppressed, a name such as 1 or 2 etc.

For example, equations 1 indicates that all first equations are to be matched into one equation named 1. All equations not matched by position are matched by name. The exponent of b is displayed in lieu of the untransformed coefficient; standard errors and confidence intervals are transformed as well. Specify a pattern if the exponentiation is to be applied only for certain models. For instance, eform 1 0 1 would transform the statistics for Models 1 and 3, but not for Model 2.

Note that, unlike regress and estimates table, estout in eform-mode does not suppress the display of the intercept. Note: eform is implemented via the transform option. If both options are specified, transform takes precedence over eform. Use as a placeholder for the function's argument in fx and dfx.

For example, type estout Alternatively, list may be specified as coefs fx dfx [ Syntax for coefs is as explained above in the description of the drop option however, include coefs in quotes if it contains multiple elements. Say, a model has two equations, price and select, and you want to exponentiate the price equation but not the select equation.

You could then type estout Specify the pattern suboption if the transformations are to be applied only for certain models. For instance, pattern 1 0 1 would apply the transformation to Models 1 and 3, but not Model 2. This option has an effect only if mfx has been applied to a model before its results were stored see help mfx or if a dprobit see help probit , truncreg,marginal help truncreg , or dtobit Cong model is estimated.

One of the parameters u, c, or p, corresponding to the unconditional, conditional, and probability marginal effects, respectively, is required for dtobit. Note that the standard errors, confidence intervals, t-statistics, and p-values are transformed as well. Using the margin option with multiple-equation models can be tricky.

The marginal effects of variables that are used in several equations are printed repeatedly for each equation because the equations per se are meaningless for mfx. To display the effects for certain equations only, specify the meqs option. Alternatively, use the keep and drop options to eliminate redundant rows. The equations option might also be of help here. As of Stata 11, the use of mfx is no longer suggested, since mfx has been superseded by margins. Results from margins can directly be tabulated by estout as long as the post option is specified with margins.

Alternatively, you may add results from margins to an existing model using estadd margins or estpost margins. The first token in string is used as the symbol. The default is: discrete " d " for discrete change of dummy variable from 0 to 1 To display explanatory text, specify either the legend option or use the discrete variable see the Remarks on using -variables. Use nodiscrete to disable the identification of dummy variables as such. The default is to indicate the dummy variables unless they have been interpreted as continuous variables in all of the models for which results are reported for dprobit and dtobit, however, dummy variables will always be listed as discrete variables unless nodiscrete is specified.

Specifying this option does not affect how the marginal effects are calculated. If you use the equations option to match equations, be sure to refer to the matched equation names and not to the original equation names in the models. The default text is " dropped ". The scalarlist may contain numeric e -scalars such as, e.

In addition, the following statistics are available: aic Akaike's information criterion bic Schwarz's information criterion rank rank of e V , i. The rules for the determination of p are as follows note that although the procedure outlined below is appropriate for most models, there might be some models for which it is not : 1 p-value provided: If the e p scalar is provided by the estimation command, it will be interpreted as indicating the p-value of the model.

This p-value corresponds to the standard overall F test of linear regression. This p-value corresponds to the Likelihood-Ratio or Wald chi2 test. Type ereturn list after estimating a model to see a list of the returned e -scalars and macros see help ereturn. Use the estadd command to add extra statistics and other information to the e -returns. Use: fmt fmt [ fmt Note that the last specified format is used for the remaining scalars if the list of scalars is longer than the list of formats.

Thus, only one format needs to be specified if all scalars are to be displayed in the same format. If no format is specified, the default format is the display format of the coefficients. If specified, the labels are used instead of the scalar names. For example:. Use the label suboption to rename such statistics, e. An alternative approach is to use estout's substitute option see the Layout options.

The stars are attached to the scalar statistics specified in scalarlist. If scalarlist is omitted, the stars are attached to the first reported scalar statistic. The printing of the stars is suppressed in empty results cells i. The determination of the model significance is based on the p-value of the model see above. Hint: It is possible to attach the stars to different scalar statistics within the same table. The default is to print the statistics in separate rows beneath one another in each model's first column.

Rows and cells that contain blanks have to be embraced in quotes. For example, Cells may contain multiple statistics and text other than the placeholder symbol is printed as is provided the cells' statistics are part of the model. Note that the number of columns in the table only depends on the cells option see above and not on the layout suboption. If, for example, the table has two columns per model and you specify three columns of summary statistics, the summary statistics in the third column are not printed.

The default placeholder is. Note that the thresholds must lie in the 0,1] interval and must be specified in descending order. Long names labels are abbreviated depending on the abbrev option and short or empty cells are padded out with blanks to fit the width specified by the user. Specifying low values may cause misalignment. If a non-zero modelwidth is specified, model names are abbreviated if necessary depending on the abbrev option and short or empty results cells are padded out with blanks.

In contrast, modelwidth does not shorten or truncate the display of the results themselves coefficients, t-statistics, summary statistics, etc. Specify a list of numbers in modelwidth to assign individual widths to the different results columns the list is recycled if there are more columns than numbers.

The purpose of modelwidth is to be able to construct a fixed-format table and thus make the raw table more readable. Be aware, however, that the added blanks may cause problems with the conversion to a table in word processors or spreadsheets. The default is to place the equations below one another in a single column. Summary statistics will be reported for each equation if unstack is specified and the estimation command is either reg3, sureg, or mvreg see help reg3 , help sureg , help mvreg.

For more information on using such functions, see the description of the functions in help file. See the begin option above for further details. See the cells option for details. The default string is a single blank. The standard decimal symbol a period or a comma, depending on the input provided to set dp; see help format is replaced by string. The standard minus sign - is replaced by string.

Use nolz to advise estout to omit the leading zeros that is, to print numbers like 0. For example, extracols 1 adds an extra column between the left stub of the table and the first column. The wrap option is only useful if several parameter statistics are printed beneath one another and, therefore, white space is available beneath the labels.

The default is interaction " ". The string is printed at the top of the table unless prehead , posthead , prefoot , or postfoot is specified. In the latter case, the variable title can be used to insert the title. The string is printed at the bottom, of the table unless prehead , posthead , prefoot , or postfoot is specified. In the latter case, the variable note can be used to insert the note.

For example, the specification. Various substitution functions can be used as part of the text lines specified in strlist see the Remarks on using -variables. For example, hline plots a horizontal "line" series of dashes, by default; see the hlinechar option or M inserts the number of models in the table. M could be used in a LaTeX table heading as follows:. The default is hlinechar - , resulting in a dashed line. The substitute may also be helpful; see the Layout options.

The suboptions are: blist matchlist to assign specific prefixes to certain rows in the table body. Specify the matchlist as pairs of regressors and prefixes, that is: name prefix [name prefix Note that equation names cannot be used if the unstack option is specified. This option may, for example, be useful for separating thematic blocks of variables by adding vertical space at the end of each block.

#### For example, one might want to combine conflict data at the country-level from ACLED with data on health, climate change, gender, etc.

Merge multiple datasets in stata forex | Summary statistics will be reported for each equation if unstack is specified and the estimation command is either reg3, sureg, or mvreg see help reg3help sureghelp mvreg. Note that explicitly specified options take precedence over settings provided by a style. The span string returns the number of spanned columns if it is included in the label, prefix, or suffix. In both datasets, id does not uniquely identify observations. Specify the relax suboption to allow droplist to contain elements for which no match can be found. |

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Merge multiple datasets in stata forex | In the defaults file, the suboptions cannot be included in the definition of a higher-level option. In syntax 2, you specify just one number, say, 1 or 2, and that is shorthand for For example, the specification. The default is to match all first equations into one equation named main, if the equations have different names and match the remaining equations by name. Use: fmt fmt [ fmt The printing of the stars is suppressed in empty results cells i. |

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Durban july betting | The cells option Use the cells option to specify the parameter statistics to be tabulated and how they are to be arranged. The default is to use the equation names as stored by the estimation command, or to use the variable labels if the equation names correspond to individual variables and the label option is specified. Jann, B. Use nodiscrete to disable the identification of dummy variables as such. Be sure to refer to the matched equation names, and not to the original equation names in the models, when using the equations option to match equations. |

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Of those, , matched, which is The reason that the number of records from the using payroll data that were not matched is zero is because I specified option keep master match , meaning I discarded the unmatched payroll records.

Had I not, the number would have been in the low millions. For many in this situation, the story would stop right here. Not for me. I want to show you how to tear into multiple-key merges to reassure yourself that things really are as they appear. You realize, of course, that I manufactured this fictional data for this blog entry and I buried a little something that once we find it, would scare you if this were a real story.

Step 1: Following my own advice In Merging data, part 1 I recommended that you merge on all common variables, not just the identification variables. This blog entry is not going to rehash the previous blog entry, but I want to emphasize that everything I said in the previous entry about single-key merges applies equally to multiple-key merges. These two datasets share a variable recording the division in which the employee works, so I am included it among the match variables:.

These merged data are looking better and better. Imagine that all the data for certain persons were missing, or that all the data for certain dates were missing. That might not be a problem, but it would certainly raise questions. Depending on the answers, it may be worth a footnote or concerning enough to return the data and ask for a refund. Finding persons or dates that are entirely unmatched is a lot of work unless you know the following trick: Merge on one key variable at a time.

Let me explain. I began by using my sample data and keeping just one observation for every value of personid. I don't care which observation I keep, I just need to keep one and only one. Then I merged on personid, keeping 1 the records that match and 2 the records from the master that do not match. I have no interest in the resulting dataset; I just wanted to see the table merge would report.

Ergo, every value of personid that appears in sample. It would not have been an indictment of the data if two persons were not matched in their entirety, but I would certainly have looked into the issue. With the merged result in memory, I would have typed. Then I would have returned to my sample data and looked at the data I had on those two people:. If had been with the company all ten years, however, I would be back on the phone seeking an explanation.

If these were medical data, certainly you would want to know how a person who never reported for a follow-up visit is known to still be alive after ten years. So much for personid. Let's do the same for date:. Finally, let's look at division:. If we had only two key variables, we would be done. We, however, are performing the full merge on three variables, namely personid, date, and division, and so there is one more set of comparisons we should examine.

Step 3: Merge on every pair of key variables With three key variables, the possible pairs are personid, date , personid, division , and division, date. We have already looked at personid, date , so that just leaves personid, division and division, date. We also note that the variables we want from this dataset are in fact in the dataset. Lets assume that the datasets are all unsorted and that the id variable has the same name id in all three datasets.

Although we can use the data from a website easily within Stata, we cannot save it there. The syntax below opens each dataset, sorts it by id and then saves it in a new location with a new name. If the dataset were already on our computer, we could save it in the same location, and, possibly even under the same name replacing the old dataset , this is the users choice.

The merge command merges corresponding observations from the dataset currently in memory called the master dataset with those from a different Stata-format dataset called the using dataset into single observations. Assuming that we have data3 open from running the above syntax, that will be our master dataset. The first line of syntax below merges the data. Directly after the merge command is the name of the variable or variables that serve id variables, in this case id.

Next is the argument using this tells Stata that we are done listing the id variables, and that what follows are the dataset s to be merged. The names are listed, with only spaces no commas, etc. Note, if the names or paths of your datasets include spaces, be sure to enclose them in quotation marks, i. The next line of syntax saves our new merged dataset.

Note that merge does not produce output. This is important since problems with the merge process often result in too few, or more often too many, cases in the merged dataset. We also see a list of the variables, which includes all the variables we want. The merged dataset contains three extra variables. These variables tell us where each observation in the dataset came from, this is useful as a check that your data merged properly. Sometimes an observation will not be present in a given dataset, this does not necessarily mean that something went wrong in the merge process, but this is another place where one can often get clues about what might have gone wrong in the merge process.

We will discuss these variables in greater detail below, when we deal with datasets where not all cases are present in all datasets. Dropping unwanted variables It is not uncommon to find that a large dataset contains many variables you are not going to use in your analysis. You can just leave those variables in your datasets when you merge them together, however, there are several reasons you might not want to do this. First, there is a limit on the number of variables Stata can handle.

These limits may see high, but if you merge multiple datasets, each with a large number of variables, you may exceed the limit for your type of Stata. The second reason you might not want to leave unneeded variables in your dataset is that each variable in memory uses additional system resources.

Below we show several methods of eliminating extra variables. There is at least one additional option, you can open the datasets placing only those variables you need in memory. If I have a dataset containing a number of variables, but the only variables I need from it are id and read, I can add variable names to my use command as is shown in the first line of syntax below.

This is particularly useful with very large files which require a lot of memory to open. Once you have opened the desired subset of variables, all you have to do is save the subset of data under a new name. Assume that my analysis only requires the variables read and write, the only variables from dataset2 that are needed are those two and the variable id to merge the data with another dataset. Below are examples of the same sort of data preparation done above, using each of the techniques described.

These techniques are equivalent, in that they produce the same end result. The efficiency of each technique varies depending on the situation. As discussed above, they tell us which dataset s each case came from. This is important because a lot of values that came from only one dataset may suggest a problem in the merge process.

However, it is not uncommon for some cases to be in one dataset, but not another. In panel data this can occur when a given respondent did not participate in all the waves of the study. It can also occur for a number of other reasons.

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