Nominal GDP in local currency (units of local currency; seasonally adjusted) - India - IMF - Quarterly
This series is part of the dataset: Nominal GDP by country (IMF)
Download Full Dataset (.xlsx)Latest updates. In India, seasonally-adjusted nominal GDP was 87,178,654,900,000 units of local currency in 2025-Q2, compared to 85,618,296,800,000 in the previous quarter. This constitutes a rise of 1.82 percent.
Sample. There are 117 data points overall in the quarterly series shown in the figure above. The span of time covered by the series is from June 1996 to June 2025.
History. Here’s a quick look at some simple statistics computed on the whole sample: GDP hit a peak of 87,178,654,900,000 units of local currency in June 2025; it recorded a bottom of 3,336,151,000,000 in June 1996; it had an average value of 27,498,262,657,265.
Latest values
| Date | Value - Units of local currency |
|---|---|
| 2024-12-31 | 83663346800000.0 |
| 2025-03-31 | 85618296800000.0 |
| 2025-06-30 | 87178654900000.0 |
Hint. A plus of using FetchSeries is that we give you accurate metadata. Find it below to better understand the characteristics of the time series that you are exploring.
Not for investment purposes. Time series and other data published on FetchSeries are not suitable for investment purposes or other financial decisions. Users should ask for expert advice and perform independent analysis before pledging money to any investment.
Series Metadata
| Field | Value |
|---|---|
| Description | Gross Domestic Product (GDP) in domestic currency |
| Country | India |
| 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.