Nominal GDP in local currency (units of local currency; seasonally unadjusted) - Italy - IMF - Quarterly
This series is part of the dataset: Nominal GDP by country (IMF)
Download Full Dataset (.xlsx)Latest updates. In Italy, seasonally-unadjusted nominal GDP stood at 556,133,100,000 units of local currency in 2025-Q2, compared to 533,179,200,000 in 2025-Q1. This marks an increase of 4.31 percent.
Sample. There are 122 records overall in the quarterly time series presented in the chart above. The series covers the time span going from March 1995 to June 2025.
History. Here's a glimpse of a few summary statistics computed on the whole sample: GDP hit a minimum of 228,297,600,000 units of local currency in March 1995; it recorded its maximum of 584,288,900,000 in December 2024; it averaged 392,853,880,328.
Latest values
| Date | Value - Units of local currency |
|---|---|
| 2024-12-31 | 584288900000.0 |
| 2025-03-31 | 533179200000.0 |
| 2025-06-30 | 556133100000.0 |
Nugget of wisdom. One of the pros of our data visualization and download service is that we provide well-crafted metadata. Find it below to gain insights on the characteristics of the indicators that you are exploring.
Not for investment purposes. Data and analyses released on FetchSeries are not not supposed to be used for investment purposes or other financial decisions. Users should consult 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 | Italy |
| Economic concept | Flow |
| Data type | Nominal aggregate |
| Seasonally adjusted | No |
| 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.