Nominal GDP in local currency (units of local currency; seasonally adjusted) - Republic of Lithuania - IMF - Quarterly
This series is part of the dataset: Nominal GDP by country (IMF)
Download Full Dataset (.xlsx)Latest updates. In the Republic of Lithuania, seasonally-adjusted nominal GDP was 21,116,445,085 units of local currency in 2025-Q3, versus 20,940,553,932 in the previous quarter. This represents a rise of 0.84 percent.
Sample. There are 123 data points overall in the quarterly series displayed in the graph above. The series covers the period stretching from March 1995 to September 2025.
History. Take a look at some statistics we calculated on the whole sample: GDP reached its maximum of 21,116,445,085 units of local currency in September 2025; it reached a minimum of 1,760,560,668 in March 1995; it had a mean value of 8,401,622,263.
Latest values
| Date | Value - Units of local currency |
|---|---|
| 2025-03-31 | 20473891514.0 |
| 2025-06-30 | 20940553932.0 |
| 2025-09-30 | 21116445085.0 |
Tip. An advantage of using FetchSeries is that we provide accurate metadata. Find it below to gain insights on the characteristics of the indicators that you use in your research.
Not for investment purposes. Any data disseminated on FetchSeries are not meant for investment purposes or any other financial decision. Users should seek professional advice and do independent analysis before pledging money to any investment.
Series Metadata
| Field | Value |
|---|---|
| Description | Gross Domestic Product (GDP) in domestic currency |
| Country | Republic of Lithuania |
| Economic concept | Flow |
| Data type | Nominal aggregate |
| Seasonally adjusted | Yes |
| Deflation method | Current prices |
| Rescaling | None |
| Measure type | Level |
| Frequency | Quarterly |
| Unit | Units of local currency |
| Source | International Monetary Fund |
| Source type | International organization |
| Data licence | Free reuse subject to conditions |
| Other information | Not available |
| FSR temporal aggregation code | SM03 |
Series in the same data set
Discover the other time series included in this data set.