This dataset provides a comprehensive collection of key economic indicators for India, encompassing various aspects of the economy. It includes data on Gross Domestic Product (GDP), inflation rates, employment statistics, trade balances, foreign exchange reserves, and more. The dataset is formatted as CSV files, ensuring ease of use for data analysis and visualization.

FocusEconomics' economic data is provided by official state statistical reporting agencies as well as our global network of leading banks, think tanks and consultancies. Our datasets provide not only historical data, but also Consensus Forecasts and individual forecasts from the aformentioned global network of economic analysts. This includes the latest forecasts as well as historical forecasts going back to 2010. Our global network consists of over 1000 world-renowned economic analysts from which we calculate our Consensus Forecasts. In this specific dataset you will find economic data for India.

TThe ERS International Macroeconomic Data Set provides historical and projected data for 181 countries that account for more than 99 percent of the world economy. These data and projections are assembled explicitly to serve as underlying assumptions for the annual USDA agricultural supply and demand projections, which provide a 10-year outlook on U.S. and global agriculture. The macroeconomic projections describe the long-term, 10-year scenario that is used as a benchmark for analyzing the impacts of alternative scenarios and macroeconomic shocks.

Explore the International Macroeconomic Data Set 2015 for annual growth rates, consumer price indices, real GDP per capita, exchange rates, and more. Get detailed projections and forecasts for countries worldwide.

Annual growth rates, Consumer price indices (CPI), Real GDP per capita, Real exchange rates, Population, GDP deflator, Real gross domestic product (GDP), Real GDP shares, GDP, projections, Forecast, Real Estate, Per capita, Deflator, share, Exchange Rates, CPI

Afghanistan, Albania, Algeria, Angola, Antigua and Barbuda, Argentina, Armenia, Australia, Austria, Azerbaijan, Bahamas, Bahrain, Bangladesh, Barbados, Belarus, Belgium, Belize, Benin, Bhutan, Bolivia, Bosnia and Herzegovina, Botswana, Brazil, Brunei, Bulgaria, Burkina Faso, Burundi, Côte d'Ivoire, Cabo Verde, Cambodia, Cameroon, Canada, Central African Republic, Chad, Chile, China, Colombia, Congo, Costa Rica, Croatia, Cuba, Cyprus, Denmark, Djibouti, Dominica, Dominican Republic, Ecuador, Egypt, El Salvador, Equatorial Guinea, Eritrea, Estonia, Eswatini, Ethiopia, Fiji, Finland, France, Gabon, Gambia, Georgia, Germany, Ghana, Greece, Grenada, Guatemala, Guinea, Guinea-Bissau, Guyana, Haiti, Honduras, Hungary, Iceland, India, Indonesia, Iran, Iraq, Ireland, Israel, Italy, Jamaica, Japan, Jordan, Kazakhstan, Kenya, Kuwait, Kyrgyzstan, Laos, Latvia, Lebanon, Lesotho, Liberia, Libya, Lithuania, Luxembourg, Madagascar, Malawi, Malaysia, Maldives, Mali, Malta, Mauritania, Mauritius, Mexico, Moldova, Mongolia, Morocco, Mozambique, Myanmar, Namibia, Nepal, Netherlands, New Zealand, Nicaragua, Niger, Nigeria, Norway, Oman, Pakistan, Panama, Papua New Guinea, Paraguay, Peru, Philippines, Poland, Portugal, Qatar, Romania, Russia, Rwanda, Samoa, Saudi Arabia, Senegal, Serbia, Seychelles, Sierra Leone, Singapore, Slovakia, Slovenia, Solomon Islands, South Africa, Spain, Sri Lanka, Sudan, Suriname, Sweden, Switzerland, Syria, Tajikistan, Tanzania, Thailand, Togo, Tonga, Trinidad and Tobago, Tunisia, Turkey, Turkmenistan, Uganda, Ukraine, United Arab Emirates, United Kingdom, Uruguay, Uzbekistan, Vanuatu, Venezuela, Vietnam, Yemen, Zambia, Zimbabwe, WORLD Follow data.kapsarc.org for timely data to advance energy economics research. Notes:

Developed countries/1 Australia, New Zealand, Japan, Other Western Europe, European Union 27, North America

Developed countries less USA/2 Australia, New Zealand, Japan, Other Western Europe, European Union 27, Canada

Developing countries/3 Africa, Middle East, Other Oceania, Asia less Japan, Latin America;

Low-income developing countries/4 Haiti, Afghanistan, Nepal, Benin, Burkina Faso, Burundi, Central African Republic, Chad, Democratic Republic of Congo, Eritrea, Ethiopia, Gambia, Guinea, Guinea-Bissau, Liberia, Madagascar, Malawi, Mali, Mozambique, Niger, Rwanda, Senegal, Sierra Leone, Somalia, Tanzania, Togo, Uganda, Zimbabwe;

Emerging markets/5 Mexico, Brazil, Chile, Czech Republic, Hungary, Poland, Slovakia, Russia, China, India, Korea, Taiwan, Indonesia, Malaysia, Philippines, Thailand, Vietnam, Singapore

BRIICs/5 Brazil, Russia, India, Indonesia, China; Former Centrally Planned Economies

Former centrally planned economies/7 Cyprus, Malta, Recently acceded countries, Other Central Europe, Former Soviet Union

USMCA/8 Canada, Mexico, United States

Europe and Central Asia/9 Europe, Former Soviet Union

Middle East and North Africa/10 Middle East and North Africa

Other Southeast Asia outlook/11 Malaysia, Philippines, Thailand, Vietnam

Other South America outlook/12 Chile, Colombia, Peru, Bolivia, Paraguay, Uruguay

Indicator Source

Real gross domestic product (GDP) World Bank World Development Indicators, IHS Global Insight, Oxford Economics Forecasting, as well as estimated and projected values developed by the Economic Research Service all converted to a 2015 base year.

Real GDP per capita U.S. Department of Agriculture, Economic Research Service, Macroeconomic Data Set, GDP table and Population table.

GDP deflator World Bank World Development Indicators, IHS Global Insight, Oxford Economics Forecasting, as well as estimated and projected values developed by the Economic Research Service, all converted to a 2015 base year.

Real GDP shares U.S. Department of Agriculture, Economic Research Service, Macroeconomic Data Set, GDP table.

Real exchange rates U.S. Department of Agriculture, Economic Research Service, Macroeconomic Data Set, CPI table, and Nominal XR and Trade Weights tables developed by the Economic Research Service.

Consumer price indices (CPI) International Financial Statistics International Monetary Fund, IHS Global Insight, Oxford Economics Forecasting, as well as estimated and projected values developed by the Economic Research Service, all converted to a 2015 base year.

Population Department of Commerce, Bureau of the Census, U.S. Department of Agriculture, Economic Research Service, International Data Base.

Dataset

This dataset was created by Rupsha Kar

Contents

Monthly and long-term India economic indicators data: historical series and analyst forecasts curated by FocusEconomics.

The dataset contains All India Yearly Macroeconomic Aggregates at Constant Price from Handbook of Statistics on Indian Economy.

Note: 1. Data for 2020-21 are Third Revised Estimates for 2021-22 are Second Revised Estimates and for 2022-23 are First Revised Estimates. 2. Data for 2023-24 are Provisional Estimates.

Full Year GDP Growth in India decreased to 6.50 percent in 2025 from 9.20 percent in 2024. This dataset includes a chart with historical data for India Full Year GDP Growth.

The influence of macroeconomic indicators makes it important to study the relationship between macroeconomic indicators and stock market return.

Context

Consumer Price Indices (CPI) measure changes over time in general level of prices of goods and services that households acquire for the purpose of consumption. CPI numbers are widely used as a macroeconomic indicator of inflation, as a tool by governments and central banks for inflation targeting and for monitoring price stability, and as deflators in the national accounts. CPI is also used for indexing dearness allowance to employees for increase in prices. CPI is therefore considered as one of the most important economic indicators. For construction of CPI numbers, two requisite components are weighting diagrams (consumption patterns) and price data collected at regular intervals.

Content

The data refers to group wise all India Consumer Price Index for Rural & Urban with base year 2010.

Acknowledgements

• The dataset is published by Central Statistical Office.
• This data is sourced from India's Goverment's Open Data website: data.gov.in
• Released Under: National Data Sharing and Accessibility Policy (NDSAP)

Inspiration

This can be used for various purposes including tasks such as exploring growth/inflation in India over the time.

The systematic impact of macroeconomic variables on stock market returns makes it crucial to comprehend the link between macroeconomic variables and the stock market. The autoregressive distributed lag (ARDL) model was used in this study to examine the causal links between specific macroeconomic factors and Indian stock prices

Context

The dataset was created to predict market recession as inspired by assignment notebook in an online course, Python and Machine Learning for Asset Management by Edhec Business School, on Coursera. However, I aimed at doing this exercise for Indian economy but due to lack of monthly data for most indicators, I used FRED database similarly used in the course.

Content

The time period chosen is 1996-2020 according to most data available.

Acknowledgements

1. Python and Machine Learning for Asset Management by EDHEC Business School, www.coursera.org for recession period data formed according to NBER. 2.FRED, fred.stloiusfed.org for macro-economic indicator data
2. Business cycle dating, www.nber.org
3. Yahoo Finance for stock market data
4. Macrotrends.net for PE ratio
5. quandl.com for snp dividend yield

Inspiration

This dataset is inspired by the assignment notebook in the online course mentioned to predict market recession for portfolio management.

Permutable AI’s India macroeconomic sentiment dataset delivers structured analytics on GDP, inflation, interest rates, retail sales, and fiscal policy decisions. The dataset transforms multilingual Indian and global news into actionable sentiment scores, updated every five minutes. Coverage spans RBI monetary policy, government budget measures, and credit growth, alongside political intelligence on elections, coalitions, and reform momentum. Trade and geopolitical modules assess India’s role in BRICS, sanctions, and cross-country alignments. Real-time disaster monitoring tracks floods, droughts, and cyclones impacting agriculture, energy, and logistics. With ten years of hourly structured data, the dataset supports backtesting systematic trading strategies across India’s dynamic market cycles via the Co-Pilot API.

This attachment contains data linked to charts in the research article titled "Is it time to recast India's fiscal and monetary policy frameworks?"The data contains trends on fiscal and monetary indicators of the Indian economy, historical and projected debt level relative to GDP for central, state and combined governments, trends in macroeconomic indicators in the Indian economy such as real GDP growth, GDP-deflator based inflation, CPI inflation, central government's gross tax revenues.

Consumer Price Indices (CPI) measure changes over time in general level of prices of goods and services that households acquire for the purpose of consumption. CPI numbers are widely used as a macroeconomic indicator of inflation, as a tool by governments and central banks for inflation targeting and for monitoring price stability, and as deflators in the national accounts. CPI is also used for indexing dearness allowance to employees for increase in prices. CPI is therefore considered as one of the most important economic indicators. For construction of CPI numbers, two requisite components are weighting diagrams (consumption patterns) and price data collected at regular intervals. The data refers to group wise all India Consumer Price Index for Rural & Urban with base year 2010. The dataset is published by Central Statistical Office and released on 12th of every month.

This dataset contains** India**’s quarterly **Gross Domestic Product (GDP) **and Gross Value Added (GVA) from 2011–12 to 2022–23 (Q1), based on official releases from the Ministry of Statistics and Programme Implementation (MOSPI), Government of India. All values in this dataset are expressed in** Indian Rupees (₹) crore at constant 2011–12 prices**, ensuring that the figures are inflation-adjusted and comparable across years.

What the dataset includes

1. Sector-wise GVA

The dataset provides quarterly GVA values for major sectors of the Indian economy, including:

• Agriculture, forestry and fishing
• Mining and quarrying
• Manufacturing
• Electricity, gas, water supply and other utility services
• Construction
• Trade, hotels, transport, communication and services related to broadcasting
• Financial, real estate and professional services
• Public administration, defence and other services

These sectors represent the key components of India’s economic structure and their contribution to quarterly growth.

2. GDP Expenditure Components

The dataset also includes the primary expenditure-side components used to compute GDP:

• PFCE (Private Final Consumption Expenditure)
• GFCE (Government Final Consumption Expenditure)
• GFCF (Gross Fixed Capital Formation)
• CIS (Changes in Stocks)
• Valuables
• Exports of goods and services
• Imports of goods and services
• Discrepancies
• Aggregate GDP Each quarter provides a complete macroeconomic snapshot of the Indian economy.

Description:

Consumer Price Indices (CPI) measure changes over time in the general level of prices of goods and services that households acquire for the purpose of consumption. CPI numbers are widely used as a macroeconomic indicator of inflation, as a tool by governments and central banks for inflation targeting and for monitoring price stability, and as deflators in the national accounts. CPI is also used for indexing dearness allowance to employees for increases in prices. CPI is therefore considered one of the most important economic indicators. For the construction of CPI numbers, two requisite components are weighting diagrams (consumption patterns) and price data collected at regular intervals. The Central Statistics Office (CSO), Ministry of Statistics and Programme Implementation releases Consumer Price Indices (CPI) on base 2010=100 for all-India and States/UTs separately for rural, urban and combined every month with effect from January 2011.

The data is published by Central Statistical Office and released on the 12th of every month.

Data Source: https://data.gov.in/catalog/state-level-consumer-price-index-ruralurban

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

IDX Composite: A Barometer of India's Economic Health?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Dataset

This dataset was created by MAHESRAM P S

Contents

This study develops an integrated forecasting framework that combines Machine Learning (Random Forest), Deep Learning (LSTM), and Econometric (VECM) approaches to analyse the dynamic behaviour of Indian pepper and turmeric prices. The models incorporate major macroeconomic determinants, including GDP, Consumer Price Index (CPI), exchange rate, gold price, interest rate, trade volume, and foreign institutional investments (FII), to capture both non-linear and long-term relationships. Model performance was evaluated using RMSE, MAE, and MAPE metrics, alongside SHAP-based feature explainability analysis.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

JPMorgan (JII) Stock: Is the Indian Investment Trust a Winner?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data