DLX provides the fastest and easiest method of maintaining data in-house on your company's LAN or PC. DLX has many features that can help you to be much more productive with your data management effort. You can choose to maintain complete databases, custom databases or a combination of both.
 

UPDATING

• With a click of an icon, DLX will call in to retrieve only the data series that have changed and send only the datapoints that have been updated.
• You can be assured that your database will have all of the available history and all of the revisions.
• The Auto-update feature can provide you with updates within seconds after the data is available.

• Easily view or print the data in table and graph formats.
• With one keystroke you can calculate growth rates and n-period moving averages, create log scales, recession shading and aggregations.
• Graphs create two independent scales that can be adjusted.
• Export graphs into Word or Powerpoint Versions 7.0  or higher.
• Print multiple graphs on a page.

• DLX Add-ins to all versions of Excel provide instant updates of your spreadsheets.
• Using DLXFIND or DLXVG3 to locate variable codes from within your spreadsheet, you can easily create new spreadsheets for analysis.

• The DLX software comes with a utility to convert the data to be used by many different database managers and econometric packages.

EViews Econometric Software by Quantitative Micro Software is a powerful Windows based modeling program that offers a variety of sophisticated regression and forecasting tools.

You can set up the latest EViews software (4.0 Enterprise Edition) to retrieve up-to-date time series directly from our DLX databases by simply using Haver data codes in any model or formula. EViews versions 2.0 and higher allow direct retrieval of DLX data into a workfile via the HFETCH command.

Rats Software

The RATS econometric software from Estima can read data directly from the Haver Analytics databases in the DLX format. To read data from a DLX file, use instructions of the form:

Stata

Stata, an integrated statistical package is designed by Stata Corporation for Windows, Macintosh, and Unix. Stata for Windows can load data from a Haver Analytics database as follows:

haver use reads Haver variables into memory

haver use varlist using filename [, ext_options ]

haver use filename [, ext_options ]

ext_options are

{ tin([constant], [constant]) | twithin([constant], [constant]) }

tvar(varname) hmissing(misval) fill clear 

haver describe lists the characteristics for the specified variables in a Haver dataset and provides a convenient method for browsing data before loading it.

haver describe filename [, detail]

haver describe varlist using filename [, detail]

For more information on the haver command visit the "Stata help for haver" page on the STATA website.

SAS

The SASEHAVR interface engine is a seamless interface between HAVER and SAS data processing that enables SAS users to read economic and financial time series data residing in a HAVER ANALYTICS DLX (Data Link Express) database.

SASEHAVR engine uses the LIBNAME statement to enable you to specify how you would like to subset your HAVER data and how you would like to aggregate the selected time series to the same frequency. The SAS DATA step can then be used to perform further subsetting and to store the resulting time series into a SAS data set described below:

Using the SAS DATA Step

You can store your selected time series in a SAS data set by using the SAS DATA step. You can further subset your data by using the WHERE, KEEP, or DROP statements in your DATA step.

For more efficient subsetting of time series by HAVER variables, by HAVER groups, or by HAVER sources, you can use the corresponding KEEP=, GROUP=, and SOURCE= option on the LIBNAME libref SASEHAVR statement. For your convenience, wildcarding is supported in these options.

There are three wildcard symbols: ’*’, ’?’ and ’’. The ’*’ wildcard corresponds to any character string and will include any string pattern that corresponds to that position in the matching variable name. The ’?’ stands for any single alphanumeric character. Lastly, the ’’ wildcard corresponds to a single numeric character.

You can also deselect time series by HAVER variables, by HAVER groups, or by HAVER sources, by using the corresponding DROP=, DROPGROUP= or the DROPSOURCE= option. These options also support wildcarding to facilitate deselection based on variable names, group names, or source names. Once your selected data is stored in a SAS data set, you can use it as you would any other SAS data set.

For more information, visit: The SASEHAVR Interface Engine