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.

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.

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

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.

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.

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.

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.

The Gross Domestic Product (GDP) in India expanded 7.80 percent in the second quarter of 2025 over the same quarter of the previous year. This dataset provides - India GDP Annual Growth Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

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

• The Indian Volatility Index data is downloaded using the yearly historical data. • The Data on Indian and the U.S. Macroeconomic announcements are compiled from the archives of dates of release of the announcements

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

The India Automotive Pneumatic Actuators Market is experiencing robust growth, driven by the increasing demand for passenger cars and commercial vehicles within the country. The market's Compound Annual Growth Rate (CAGR) exceeding 7.80% from 2019 to 2024 indicates a significant upward trajectory. This growth is fueled by several key factors. Firstly, the rising adoption of advanced driver-assistance systems (ADAS) and the increasing focus on improving fuel efficiency are creating a significant demand for pneumatic actuators in various applications, including throttle control, braking systems, and fuel injection. Secondly, the burgeoning automotive industry in India, characterized by increasing vehicle production and sales, is a major contributor to the market expansion. Furthermore, government initiatives promoting vehicle safety and emission reduction standards are indirectly driving the demand for high-performance and reliable pneumatic actuators. Segmentation reveals strong demand across passenger cars and commercial vehicles, with throttle actuators, fuel injector actuators, and brake actuators being the primary application types. Key players like Denso, Delphi, Magneti Marelli, and Bosch are actively participating in this growing market, leveraging their technological expertise and established distribution networks to cater to the evolving needs of the automotive sector. While challenges such as fluctuating raw material prices and stringent emission norms exist, the overall positive outlook for the Indian automotive industry suggests continued expansion for the pneumatic actuator market in the coming years. The forecast period (2025-2033) is expected to witness further expansion, building upon the strong foundation established in the historical period. The market's size in 2025 is estimated at [Estimate a reasonable market size in millions based on the available CAGR and 2019-2024 data, e.g., 150 million USD]. This projection considers continued growth in vehicle production, sustained investment in automotive technology, and the ongoing adoption of pneumatic actuators across different vehicle segments. Despite potential restraints, the positive macroeconomic indicators for India and the sustained growth of the automotive sector are expected to outweigh these challenges, contributing to a consistent rise in market value throughout the forecast period. Competitive dynamics will play a critical role, with existing players focusing on product innovation and new entrants seeking to capture market share through competitive pricing and localized production. Recent developments include: September 2023: Nidec Corporation announced an investment of USD USD 55 million to enhance its manufacturing facilities in India. Through this expansion, the company will expand its motion and energy business across the country., August 2022: ZF Commercial Vehicle Control Systems India announced the expansion of manufacturing capacity in India. The new manufacturing facility was set up to improve productivity across plants by capitalizing on lean and frugal engineering capabilities., July 2022: Vitesco Technologies AG inaugurated their new manufacturing plant at Talegaon in Pune, India. The company invested about USD 34.94 million in infrastructure, buildings, and plants. Through this investment, the company expanded its product portfolio, including actuators across the country.. Key drivers for this market are: Rise in demand for Vehicle Comfort and Safety System. Potential restraints include: High Raw Material Prices May One of The Factors That Hindering Target Market Growth.. Notable trends are: Growing Demand for Throttle Actuators.

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

Abstract

The National Sample Surveys (NSS) are being conducted by the Government of India since 1950 to collect socio-economic data employing scientific sampling methods. The Household Consumption Expenditure Survey (HCES) is designed to collect information on consumption of goods and services by the households. Information collected in HCES is used for analyzing and understanding the consumption and expenditure pattern, standard of living and well-being of the households. Besides, the data of the survey provides budget shares of different commodity groups that is used for preparation of the weighting diagram for compilation of official Consumer Price Indices (CPIs). The data collected in HCES is also utilized for deriving various other macroeconomic indicators.

Geographic coverage

The survey covers the whole of the Indian Union except the villages in Andaman and Nicobar Islands which are difficult to access. Total 15016 FSUs was surveyed for the central sample at all-India level.

Analysis unit

Households and Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

A multistage stratified sampling design, considering villages/urban blocks as the first stage units has been used in the survey. The households are the ultimate stage units. Simple Random Sampling Without Replacement (SRSWOR) method is used for selecting the samples.

In order to ensure proper representation of households of different economic categories, all the households of a selected village/urban block are classified into three groups depending on a criterion based on (i) land possessed in rural areas and (ii) possession of car in urban areas as on the date of the survey. A total of 18 households with proportional representation from the three groups have been selected.

Note: The details of survey methodology and estimation procedure are provided in Appendix B of the survey report “Survey on Household Consumption Expenditure: 2022-23”.

Mode of data collection

Face-to-face [f2f]

Research instrument

In the HCES 2022–23, the consumption basket was categorized into three broad groups: (i) Food items, (ii) Consumables and Services, and (iii) Durable Goods. Based on this classification, three separate questionnaires were developed: the Food Questionnaire (FDQ), the Consumables and Services Questionnaire (CSQ), and the Durable Goods Questionnaire (DGQ). These were administered to selected households across three consecutive monthly visits, with each visit focusing on a different category.

Additionally, a separate Household Characteristics Questionnaire (HCQ) was used to collect demographic and other background information about the household members.

To minimize any potential bias from the order of questionnaire administration, the survey employed all six possible sequences of the three main questionnaires:

(FDQ, CSQ, DGQ)

(FDQ, DGQ, CSQ)

(CSQ, FDQ, DGQ)

(CSQ, DGQ, FDQ)

(DGQ, FDQ, CSQ)

(DGQ, CSQ, FDQ)

This approach ensured that no particular sequencing influenced the results.

Abstract

The Household Consumption Expenditure Survey (HCES) is designed to collect information on consumption of goods and services by the households. Information collected in HCES is used for analyzing and understanding the consumption and expenditure pattern, standard of living and well-being of the households. Besides, the data of the survey provides budget shares of different commodity groups that is used for preparation of the weighting diagram for compilation of official Consumer Price Indices (CPIs). The data collected in HCES is also utilized for deriving various other macroeconomic indicators.

Geographic coverage

8,684 FSUs in the rural areas and 6,143 in the urban areas have been surveyed in HCES:2023-24. The total sample size of 14,827FSUs has been allocated to State/UTs in proportion to Census 2011 population, subject to a minimum allocation of 40 FSUs.

Analysis unit

Households and Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

The most recent Household Consumption Expenditure Survey (HCES), previously known as the Household Consumer Expenditure Survey, was conducted from August 2023 to July 2024. Data was collected from each selected household using three separate questionnaires—FDQ (Food Items), CSQ (Consumables & Services), and DGQ (Durable Items)—administered during three distinct monthly visits within a quarter. In addition, a separate questionnaire, HCQ, was used to gather information on household characteristics.

The HCES 2023–24 employed a multi-stage stratified sampling design. The First Stage Units (FSUs) were villages or urban blocks (or their sub-units), and the Ultimate Stage Units (USUs) were the households. Both FSUs and USUs were selected using Simple Random Sampling Without Replacement (SRSWOR). Within each FSU, 18 sample households were surveyed.

The survey period was divided into 10 panels, each spanning three months. In the first month of each panel, the HCQ questionnaire and one of the three main questionnaires (FDQ, CSQ, or DGQ) were administered to the selected households. The remaining two questionnaires were administered in the second and third months, respectively. The order of administering the FDQ, CSQ, and DGQ was randomized for each FSU to avoid sequencing bias. All six possible permutations of the three questionnaires—(FDQ, CSQ, DGQ), (FDQ, DGQ, CSQ), (CSQ, FDQ, DGQ), (CSQ, DGQ, FDQ), (DGQ, FDQ, CSQ), and (DGQ, CSQ, FDQ)—were used across the sample households.

For the urban sector, the sampling frame consisted of Urban Frame Survey (UFS) blocks from the latest UFS. For the rural sector, it comprised villages listed in the 2011 Census, excluding those reclassified as urban in the latest UFS at the time of sample selection. To maintain uniformity in FSU size and facilitate operational convenience, large villages or UFS blocks were sometimes subdivided into smaller, roughly equal units—referred to as sub-units—based on predefined criteria, such as population size or the number of households.

Mode of data collection

Face-to-face [f2f]

Research instrument

In the HCES 2023–24, the consumption basket was categorized into three broad groups: (i) Food items, (ii) Consumables and Services, and (iii) Durable Goods. Based on this classification, three separate questionnaires were developed: the Food Questionnaire (FDQ), the Consumables and Services Questionnaire (CSQ), and the Durable Goods Questionnaire (DGQ). These were administered to selected households across three consecutive monthly visits, with each visit focusing on a different category.

Additionally, a separate Household Characteristics Questionnaire (HCQ) was used to collect demographic and other background information about the household members.

To minimize any potential bias from the order of questionnaire administration, the survey employed all six possible sequences of the three main questionnaires:

(FDQ, CSQ, DGQ)

(FDQ, DGQ, CSQ)

(CSQ, FDQ, DGQ)

(CSQ, DGQ, FDQ)

(DGQ, FDQ, CSQ)

(DGQ, CSQ, FDQ)

This approach ensured that no particular sequencing influenced the results.

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.

Xiaomi and Dixon to Make Mobile Phones in India, Boosting Local Manufacturing

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

Macroeconomic databases for India: 140-sector Input-Output Table and 117-sector Social Accounting Matrix for base year 2012-13.

Snapshot img

AI In Economic Analytics Market Size 2025-2029

The ai in economic analytics market size is valued to increase by USD 39.38 billion, at a CAGR of 36.2% from 2024 to 2029. Intensifying demand for predictive and prescriptive economic intelligence will drive the ai in economic analytics market.

Major Market Trends & Insights

North America dominated the market and accounted for a 42% growth during the forecast period.
By Component - Solutions segment was valued at USD 2.03 billion in 2023
By Application - Macroeconomic forecasting segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 1.00 million
Market Future Opportunities: USD 39382.60 million
CAGR from 2024 to 2029 : 36.2%

Market Summary

In the dynamic economic landscape, AI's role in analytics has gained significant traction due to the intensifying demand for predictive and prescriptive insights. Advanced technologies, such as generative AI and large language models, are increasingly utilized to deliver sophisticated narrative and forecasting capabilities. However, the implementation of AI in economic analytics is not without challenges. Systemic deficiencies in data availability, quality, and governance pose significant hurdles. Despite these obstacles, the market's growth remains robust, with recent estimates suggesting it will reach a value of USD15.7 billion by 2026, according to a reputable market research firm.
This underscores the market's potential to revolutionize economic intelligence, enabling organizations to make data-driven decisions with greater precision and confidence. The future direction of this market lies in the continued refinement of AI algorithms, the integration of diverse data sources, and the establishment of robust data governance frameworks.

What will be the Size of the AI In Economic Analytics Market during the forecast period?

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How is the AI In Economic Analytics Market Segmented ?

The ai in economic analytics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Component

  Solutions
  Services


Application

  Macroeconomic forecasting
  Financial analytics
  Trade and investment analytics
  Labor market analytics
  Others


End-user

  Government and public institutions
  Financial institutions
  Corporates and enterprises
  Research and academia
  International organizations


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    Australia
    China
    India
    Japan


  South America

    Brazil


  Rest of World (ROW)

By Component Insights

The solutions segment is estimated to witness significant growth during the forecast period.

The market continues to evolve, with the solutions segment leading the charge. This segment encompasses advanced software platforms, tools, and high-performance hardware infrastructure essential for their operation. Machine learning, deep learning, natural language processing, and generative AI are now standard features, surpassing traditional econometric and statistical software. Real-time data processing, risk assessment models, and predictive economic modeling are becoming the norm. For instance, machine learning models can process big data analytics to provide AI-driven insights for portfolio risk management and demand forecasting methods.

Moreover, data mining techniques and predictive modeling enable economic scenario generation, what-if scenario planning, and policy impact simulation. Cloud computing infrastructure facilitates the deployment and scalability of these advanced solutions. As of 2022, the market is expected to reach a value of USD15.3 billion, underscoring its growing importance in business cycle analysis, financial market prediction, investment strategy optimization, and more.

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The Solutions segment was valued at USD 2.03 billion in 2019 and showed a gradual increase during the forecast period.

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Regional Analysis

North America is estimated to contribute 42% to the growth of the global market during the forecast period.Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

See How AI In Economic Analytics Market Demand is Rising in North America Request Free Sample

The market is experiencing significant growth and innovation, with North America leading the charge. This region's dominance is driven by the presence of leading technology corporations, a robust venture capital ecosystem, world-class academic institutions, and government support for technological advancemen