See Claudio, Heinisch  und Holtemöller (2020)* for details on the calculation of quarterly East German GDP and nowcasts for the current year:

GDP is a key indicator for the analysis and monitoring of regional economic development. 

The main data source for GDP at regional level is the release of a working group formed by the statistical offices of the federal states on regional accounts (Arbeitskreis Volkswirtschaftliche Gesamtrechnungen/ working group “Regional Accounts”). Based on gross value added calculations, recent GDP figures are only available at annual frequency and are published with a delay of three months after the end of the reference period. 

Updates for the first half of a year are published in the summer of the corresponding year. The working group has stopped producing quarterly data for the time after 1999. However, the IWH provides quarterly data for East German GDP (with and without Berlin). We apply temporal disaggregation, benchmarking and reconciliation methods to the official annual and semiannual data for East Germany in order to compute quarterly GDP. Below, we describe the general approach and a detailed description is provided in Appendix A. 

For the calculation of East German GDP (including Berlin), we start by using official statistics published by the German Federal Statistical Office for the period 1991–1994. These comprise quarterly GDP as well as gross value added for East German states. For the period 1995–2015, the quarterly shares are based on a bottom-up approach (based on gross value added components). For the period since 2016, we use monthly indicators to temporally disaggregate the annual series. This procedure is complicated by the fact that official regional statistics for monthly and/or quarterly indicators are rare and only published with some delay. 

We use the ECOTRIM package provided by Eurostat that implements the Chow and Lin (1971) method for temporal disaggregation of time series. This econometric approach captures the relationship between indicators and the target variable very well. Currently, the number of employees contributing to the social security system and turnover in manufacturing are the most important indicators for the quarterly breakdown. In line with the European Statistical System (ESS) guidelines on temporal disaggregation, benchmarking and reconciliation techniques. After disaggregation, the data is adjusted for calendar and seasonal effects using the X-13-ARIMA method. In this particular case, we use the calendar and seasonal factors for Germany as a whole and apply these to the series for eastern and western Germany.

For information: 

The quarterly national accounts data for eastern and western Germany available here are based on the data published by the Federal Statistical Office for Germany as a whole in February 2025. The annual data for eastern and western Germany correspond to the publication of the working group on national accounts of the federal states from March 2025.

In this csv file format, a semicolon is used as a separator and a comma as a decimal point.

*Reference:
Claudio, J.C., Heinisch, K. und Holtemöller, O., Nowcasting East German GDP growth: a MIDAS approach, Empirical Economics 2020, https://doi.org/10.1007/s00181-019-01810-5.