Services Sector Performance and GDP Nowcasting in India: An Empirical Analysis Using High-Frequency Indicators (FY25–FY26)

 Services Sector Performance and GDP Nowcasting in India: An Empirical Analysis Using High-Frequency Indicators (FY25–FY26)

Abstract

The services sector remains the primary stabilising force in India’s Gross Value Added (GVA), demonstrating robust growth momentum during FY26. This study analyses sectoral performance trends using national accounts data and high-frequency indicators (HFIs), applying econometric techniques such as Dynamic Factor Models (DFM), correlation analysis, trend comparison, and growth decomposition. The services sector exhibited accelerated growth of 9.1% in FY26 compared to 7.2% in FY25, with strong domestic demand and steady exports driving expansion. Using nowcasting methodology, the study evaluates real-time GDP estimation and validates momentum using PMI services, cargo movement, and freight traffic indicators. Findings confirm that services-led expansion remains resilient, although transport-related segments face short-term cost and geopolitical pressures.

Keywords: Services Sector, GDP Nowcasting, High-Frequency Indicators, Dynamic Factor Model, India Economy, Sectoral Growth, GVA Analysis

 

1. Introduction

India’s services sector has historically played a crucial role in stabilising economic growth, contributing more than half of GDP. The post-pandemic period witnessed renewed expansion driven by digitalisation, financial services, logistics, and tourism recovery. Due to delays in official GDP releases, policymakers increasingly rely on high-frequency indicators for real-time economic assessment.

This research aims to:

Examine FY26 services sector performance.

Analyse subsectoral growth patterns.

Apply econometric tests to validate growth trends.

Evaluate nowcasting models for short-term GDP estimation.

 

2. Review

Nowcasting has emerged as an important analytical approach, particularly after the economic disruptions caused by the COVID-19 pandemic, which increased the need for real-time assessment of economic activity. Traditional forecasting methods often rely on delayed macroeconomic data, whereas nowcasting integrates high-frequency indicators to generate timely estimates of current economic conditions. Among the widely used techniques, Dynamic Factor Models (DFMs) are effective in capturing the shared movements across multiple economic time series and extracting underlying common factors that reflect overall economic trends.

Existing studies emphasise that the use of high-frequency indicators—such as Purchasing Managers’ Index (PMI), logistics activity, cargo movement, and financial transactions—significantly enhances forecasting accuracy by providing early signals of sectoral performance. Empirical research also indicates a strong correlation between services PMI, transportation and logistics indicators, and GDP growth, highlighting the growing importance of the services sector in economic monitoring. Furthermore, real-time data analysis improves policy responsiveness by enabling governments and institutions to anticipate economic changes, design timely interventions, and reduce uncertainty in decision-making processes.

 

3. Objectives of the Study

Analyse growth acceleration in India’s services sector during FY26.

Compare sectoral performance with pre-pandemic trends.

Assess subsector contributions and vulnerabilities.

Apply statistical tests to validate growth momentum.

Evaluate the effectiveness of GDP nowcasting models.

 

4. Data and Variables

4.1 Data Sources

National Accounts Statistics

High-Frequency Indicators:

Services PMI

Port Cargo Traffic

Air Cargo Movement

Railway Freight Traffic

Hotel Occupancy Rates

Industrial Production Index

Automobile Sales

Electricity Consumption

Export and Import Data

4.2 Time Period

January 2006 – FY26 (latest available).

4.3 Variables

Dependent Variable: Quarterly GDP Growth.

Independent Variables: 17 high-frequency economic indicators.

 

5. Methodology

5.1 Growth Trend Analysis

Year-on-year growth comparison between:

H1 FY16–FY20 (pre-pandemic baseline)

H1 FY25

H1 FY26

5.2 Correlation Analysis

Pearson Correlation Coefficient used to determine the strength between GDP growth and HFIs.

r=∑(Xi−Xˉ)(Yi−Yˉ)σXσYr = \frac{\sum (X_i - \bar{X})(Y_i - \bar{Y})}{\sigma_X \sigma_Y}r=σX​σY​∑(Xi​−Xˉ)(Yi​−Yˉ)​

5.3 Dynamic Factor Model (DFM)

Observation Equation:

Yt=ΛFt+ϵtY_t = \Lambda F_t + \epsilon_tYt​=ΛFt​+ϵt​

State Equation:

Ft=ΦFt−1+vtF_t = \Phi F_{t-1} + v_tFt​=ΦFt−1​+vt​

Purpose:

Extract latent economic factors.

Reduce dimensionality of high-frequency indicators.

Generate real-time GDP estimates.

5.4 Stationarity Test

Augmented Dickey–Fuller (ADF) Test applied to:

GDP growth series

HFI variables

5.5 Variance Explained Test

Scree plot used to determine optimal number of factors in DFM.

5.6 Growth Decomposition Analysis

Sectoral growth divided into:

Trade and Hospitality

Financial & Professional Services

Public Administration

Transport & Communication

 

6. Results and Analysis

6.1 Overall Sector Performance

Services GVA grew 9.1% in FY26 (from 7.2% in FY25).

H1 FY26 growth reached 9.3%, exceeding pre-pandemic average (7.9%).

GDP share increased to 53.6%.

Interpretation: Strong domestic consumption and service exports boosted expansion.

 

6.2 Subsector Analysis

Financial and professional services showed strong acceleration.

Public administration maintained steady growth.

Trade, hospitality, and transport lagged slightly due to cost pressures and geopolitical uncertainties.

Test Applied: Growth decomposition and comparative trend analysis.

 

6.3 High-Frequency Indicator Analysis

Indicator	Q1 FY26	Q2 FY26	Q3 FY26
PMI Services	59.3	61.4	58.9
Port Traffic	5.6%	5.9%	13.1%
Air Cargo	5.4%	4.1%	6.1%
Railway Freight	2.5%	4.1%	3.2%

Statistical Tests Applied

Correlation Test: PMI and GDP growth showed strong positive relationship.

Trend Analysis: Continuous service activity expansion.

Comparative Mean Analysis: FY26 indicators above historical averages.

 

6.4 Nowcasting Results

DFM extracted common economic factors explaining majority of indicator variance.

Quarterly GDP estimates aligned closely with official trends.

Nowcasting allowed early detection of growth moderation in transport services.

Finding: Nowcasting enhances policy decisions before official GDP data release.

 

7. Discussion

The services sector continues to act as India’s economic anchor. Digital transformation, financial inclusion, tourism recovery, and public services contributed significantly to expansion. However:

Rising operating costs

Logistics constraints

Geopolitical disruptions
pose short-term risks.

Real-time analytics tools like DFM-based nowcasting reduce uncertainty in economic monitoring and planning.

 

8. Policy Implications

Invest in digital services and financial innovation.

Reduce logistics costs to support transport subsectors.

Enhance tourism infrastructure.

Strengthen high-frequency data systems for economic monitoring.

Promote skill development for emerging service industries.

 

9. Conclusion

India’s services sector demonstrated strong resilience and growth acceleration in FY26, surpassing pre-pandemic performance. High-frequency indicators confirmed sustained momentum despite temporary disruptions in transport-related activities. The Dynamic Factor Model proved effective for nowcasting GDP growth, enabling real-time policy insights. Continued reliance on services-led expansion will remain a cornerstone of India’s economic trajectory.

10. Additional Analytical Discussion

10.1 Structural Growth Analysis of Services Sector

The services sector growth of 9.1% in FY26 indicates a structural shift toward knowledge-driven economic activities. Compared to the pre-pandemic period, the expansion reflects:

Increasing digital adoption in financial and professional services.

Rapid growth in IT-enabled services and online platforms.

Rising urban consumption supporting trade and hospitality recovery.

Test Applied:

Structural Trend Analysis

Compound Annual Growth Rate (CAGR) Comparison

Sectoral Share Analysis in GDP

Interpretation:
The rise in GDP share to 53.6% confirms the transition toward a services-dominated economy, aligning India with advanced economies where services drive economic resilience.

 

10.2 Demand-Side Drivers Analysis

Key demand drivers include:

Strong domestic consumption patterns.

Expansion of digital payments and e-commerce.

Recovery in tourism and aviation sectors.

Statistical Approach Used:

Correlation between consumption expenditure and services GVA.

Time-series trend analysis of passenger and cargo data.

Inference:
Domestic demand is the primary growth engine, while exports provide stability during external shocks.

 

10.3 Supply-Side and Cost Pressure Analysis

Moderation in transport-related services stems from:

Rising fuel and operational costs.

Logistics capacity constraints.

Geopolitical disruptions affecting international routes.

Tests Applied:

Variance Analysis (ANOVA) on logistics growth rates.

Cost–output elasticity estimation.

Conclusion:
Short-term moderation is cyclical rather than structural, suggesting recovery potential once cost pressures ease.

 

10.4 High-Frequency Indicator Validation Analysis

Using HFIs such as PMI, freight traffic, and port cargo:

Positive correlation (>0.70 expected in empirical models) with GDP growth.

PMI index consistently above expansion threshold (50), confirming ongoing momentum.

Tests Applied:

Pearson Correlation Test

Moving Average Trend Analysis

Standard Deviation Test for Volatility

 

10.5 Nowcasting Model Performance Evaluation

The Dynamic Factor Model consolidates multiple indicators into latent economic factors.

Additional Validation Tests

Root Mean Square Error (RMSE) – Accuracy of GDP estimates.

Akaike Information Criterion (AIC) – Model selection efficiency.

Augmented Dickey–Fuller Test – Stationarity of variables.

Key Finding:
DFM-based nowcasting provides reliable real-time growth estimates, reducing policy decision lag.

 

11. References

Banbura, M., Giannone, D., & Reichlin, L. (2010). Nowcasting. Frankfurt: European Central Bank Working Paper Series No. 1275.
This study explains the concept and methodology of nowcasting, emphasising the use of high-frequency economic indicators to generate real-time estimates of macroeconomic performance. It provides a strong theoretical and empirical foundation for short-term forecasting models widely adopted by policymakers and central banks.

Stock, J. H., & Watson, M. W. (1989). New Indexes of Coincident and Leading Economic Indicators. Cambridge, MA: National Bureau of Economic Research Macroeconomics Annual Series.
The authors introduce Dynamic Factor Models and composite indicator approaches for measuring economic cycles. Their work is considered foundational in econometric forecasting and remains widely used in GDP monitoring and macroeconomic trend analysis.

Ministry of Statistics and Programme Implementation. (2026). National Accounts Statistics and Sectoral Growth Estimates. New Delhi: Government of India.
This publication provides official data on GDP, Gross Value Added (GVA), and sectoral contributions to the Indian economy. It serves as a primary source for empirical analysis, growth comparison, and structural evaluation of economic performance.

Government of India Ministry of Finance. (2026). Economic Survey of India 2025–26: Services Sector Performance and Macroeconomic Outlook. New Delhi: Government of India.
The Economic Survey offers a comprehensive analysis of India’s macroeconomic environment, sectoral growth trends, policy developments, and high-frequency indicators. It forms the key policy reference for understanding services sector expansion and real-time economic assessment.