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.

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.

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 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

The Gross Domestic Product (GDP) in India expanded 7.40 percent in the first 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.

Historical chart and dataset showing India economic growth by year from 1960 to 2023.

• 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

Social Accounting Matrix for India 2017-18 accounts 112 sectors of Indian economy of which 39 sectors are accounted for agriculture and allied activities, 18 sectors are related to agriculture-based processing activities, 4 mining sectors, 24 manufacturing sectors other than agro-processing, 3 sectors related to utilities, 1 construction sector and 23 service sectors including transport and trade. The primary factor input has been classified into 8 types of labor, 4 types of capital, and one category of land. The categorization of labor is based on the level of education of the workers and geographical location i.e. rural and urban. The 4 types of capital are; crop, live animal, mining, and other financial capital. This SAM distinguishes households into three broad categories like rural farm households, rural non-farm households, and urban households. Households are further disaggregated into per capita expenditure quintiles. Therefore, this database is useful for scholars and policymakers who are interested to work on macroeconomic policy analysis for the Indian economy.

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.

ICICI Bank (IBNstock): Navigating the Indian Economic Landscape

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. Household Consumption Expenditure Survey 2023-24 will commence from August 2023.

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.

Sampling procedure

The latest survey on household consumption expenditure (previously known as household consumer expenditure survey) was conducted during the period August 2023 to July 2024 in which information was collected from each sampled household in three questionnaires, namely, Questionnaire: FDQ (Food Items), Questionnaire: CSQ (Consumables & Services) and Questionnaire: DGQ (Durable Items) in three separate monthly visits in a quarter. Apart from these, another questionnaire, namely, Questionnaire: HCQ was canvassed to collect information on household characteristics.

In HCES: 2023-24, a multi-stage stratified sampling design was used where villages/urban blocks or sub-units of these were regarded as the First Stage Units (FSU) and the households were the Ultimate Stage Units (USU). Both the FSUs and USUs were selected with Simple Random Sampling Without Replacement (SRSWOR). 18 sample households were canvassed within an FSU.

The survey period of HCES:2023-24was divided into 10 panels, each consisting of three months. In the first month of any panel,Questionnaire: HCQ along with any one of the questionnaires, i.e., FDQ/CSQ/DGQwere canvassed in the selected households. During the second month of the panel, any one from the remaining two questionnaires was canvassed and in the last month, the last questionnaire was canvassed. The sequence of the questionnaires to be canvassed in each month of a panel for a particular FSU was decided randomly to eliminate bias that may arise due to the adoption of a particular sequencing for canvassing. Thus, all six possible sequences, i.e., [(Q1, Q2, Q3), (Q1, Q3, Q2), (Q2, Q1, Q3), (Q2, Q3, Q1), (Q3, Q1, Q2) and (Q3, Q2, Q1)], where Q1 refers to FDQ, Q2 refers to CSQ and Q3 refers to DGQ, were canvassed at random in the sample households.

The sampling frame for urban sector is the list of Urban Frame Survey (UFS) blocks as per latest Urban Frame Survey and for rural sector, it is the list of villages as per Census 2011 updated by removing those villages which are urbanized and included in latest UFS (till the time of sample selection).Sometimes, with a view to ensure uniformity in the size of FSUs and operational convenience, large villages/UFS blocks are notionally divided into smaller units of more or less equal size, known as sub-units depending on a pre-defined criteria based on population in the village or number of households in the UFS block. The sector-specific criteria for sub-unit formation are as below:

Rural Sector (i) The number of SUs to be formed in the villages (with Census 2011 population of 1000 or more and except some States/UTs) are decided based on projected present population of the village. The criteria aregiven below:

Projected Population of the village No. of SUs to be formed
less than 1200 1
1200 to 2399 2 2400 to 3599 3
… …

(ii) For rural areas of Himachal Pradesh, Sikkim, Andaman & Nicobar Islands, Ladakh, Parts of Uttarakhand (except four districts Dehradun, Nainital, Haridwar and Udham Singh Nagar), Jammu and Kashmir (seven districts Poonch, Rajouri, Udhampur, Reasi, Doda, Kishtwar, Ramban) and Idukki district of Kerala; SU is formed in a village if population as per Census 2011 is more than or equals to 500. The criteria for the number of SU to be formed are as below:

Projected Population of the village Number of SUs to be formed
less than 600 1
600 to 1199 2
1200 to 1799 3
... ...

Urban Sector: (i) SUs are formed in those UFS blockshaving more than or equal to 250 households. The number of SUs to be formed within the UFS blocks is decided by the following criteria:

Number of Households in UFS Block Number of SUs to be formed
less than 250 1
250 to 499 2
500 to 749 3
… …
Thus, the list of Villages / UFS Blocks / Sub-Units (for those villages or UFS blocks where sub-units are formed within) together formed the sampling frame for First Stage Unit selection.

Mode of data collection

Face-to-face [f2f]

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.

PhonePe: The Future of Digital Payments in India

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

This is master data set used for paper " Monetary policy and Food Inflation: A Case study from India"

The global cable floor (net floor) market is experiencing robust growth, driven by the increasing demand for flexible and adaptable workspace solutions across various sectors. The market's expansion is fueled by the rising adoption of raised access flooring systems in data centers, offices, and healthcare facilities. These systems offer superior cable management capabilities, facilitating easy access for maintenance and upgrades, a crucial aspect in today's dynamic technological landscape. The preference for aesthetically pleasing and ergonomic office spaces further contributes to the market's growth, with plastic network flooring and all-steel OA network flooring catering to diverse needs and budgets. While the market faces challenges like high initial investment costs and potential material limitations, ongoing innovations in material science and design are mitigating these constraints. The Asia-Pacific region, particularly China and India, is anticipated to dominate the market due to rapid urbanization, infrastructure development, and a burgeoning IT sector. North America and Europe maintain significant market shares, driven by robust IT infrastructure and a strong focus on workplace optimization. The projected Compound Annual Growth Rate (CAGR) indicates a sustained upward trajectory in market value throughout the forecast period (2025-2033), promising lucrative opportunities for established players and new entrants alike. The competitive landscape is characterized by both domestic and international players, with companies like Huilian, Chenxin, and Shenyang Aircraft Industry Group holding prominent positions. Successful strategies involve focusing on product innovation, expanding geographical reach, and forging strong partnerships within the construction and IT industries. The segment analysis highlights the strong performance of both online and offline sales channels, indicating the market's accessibility to a wide range of customers. Future growth hinges on the ability of companies to offer customized solutions, adapt to evolving technological needs, and maintain sustainable manufacturing practices. The market's expansion is closely linked to broader economic trends, particularly those affecting construction, technology, and industrial development. Therefore, careful monitoring of macroeconomic indicators is crucial for accurately forecasting future market performance. The continued adoption of sustainable building practices also presents opportunities for manufacturers developing eco-friendly cable flooring solutions.

This paper provides a review of the developments which has been occurring in the labour market in the making of neoliberal India beginning early 1980s.The macroeconomic data shows that the share of wages in the total gross value added has been constantly falling along with increase in the incidence of unemployment and informalisation of the formally employed workforce. Numerous ethnographic studies provide the evidence that there has been no abatement in precarious work in the informal sector of the economy in the post-reforms period compared to the pre-reforms period. Both, the macroeconomic and ethnographic studies, reach to the conclusion that these developments in India's labour market are not the result of the natural market forces but to a great extent due to the occurrence of primitive accumulation in the economy. The capitalist class has been using primitive accumulation as a tool to extract the surplus by embedding it in the growth process in India's economy.

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.

Sensex to Surge, Experts Predict Strong Growth for the Indian Index

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

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.

PCGH ZDP: Is Zydus Pharmaceuticals (India) poised for growth?

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