Tracks

22 May 2024 1

1. Estimating the Natural Rate of Interest

Overview: This sub-track focuses on the methodologies and technologies used to estimate the natural rate of interest (r*), which is crucial for guiding monetary policy. The natural rate of interest is the equilibrium interest rate that supports the economy at full employment while keeping inflation stable.

Key Topics:

Advanced Econometric Techniques: Utilizing AI and ML for improving the accuracy of r* estimation.
Real-Time Data Analysis: Leveraging Big Data to refine real-time estimates of the natural rate of interest.
Historical and Comparative Studies: Applying blockchain technology to create transparent, immutable records of historical interest rate data for comparative studies.
Policy Implications: Discussing the implications of accurate r* estimation for monetary policy and economic stability.

Applications:

Dynamic Stochastic General Equilibrium (DSGE) Models: Incorporating AI-enhanced DSGE models for better r* estimates.
Macro-Financial Linkages: Examining how fintech innovations impact the natural rate of interest.

2. Development of the Near-Term Forecast of Inflation: Application of FAVAR and BVAR Models

Overview: This sub-track explores the use of Factor-Augmented Vector Autoregression (FAVAR) and Bayesian Vector Autoregression (BVAR) models for near-term inflation forecasting. These models integrate large datasets to provide more accurate and timely inflation predictions.

Key Topics:

Model Integration: Combining AI and Big Data techniques with FAVAR and BVAR models to enhance predictive performance.
Data Sources: Utilizing diverse datasets, including financial market data, consumer sentiment, and supply chain information.
Real-Time Forecasting: Implementing real-time data analysis for up-to-date inflation forecasts.
Model Validation: Using blockchain for transparent validation and verification of model predictions.

Applications:

Policy Decision-Making: Informing central banks and policymakers with accurate near-term inflation forecasts.
Economic Stability: Enhancing the ability to anticipate and mitigate inflationary pressures.

3. Forecasting Banking System Liquidity Using Payment System Data

Overview: This sub-track examines innovative approaches to forecasting banking system liquidity by analyzing payment system data. Understanding liquidity trends is essential for maintaining financial stability and effective monetary policy implementation.

Key Topics:

Data Analytics: Applying Big Data analysis to payment system transactions to forecast liquidity needs.
Machine Learning Models: Utilizing ML algorithms to detect patterns and predict liquidity fluctuations.
Integration with Fintech: Leveraging fintech platforms to gather and analyze real-time payment data.
Risk Management: Developing strategies to manage liquidity risk based on forecasted data.

Applications:

Central Bank Operations: Enhancing the effectiveness of central bank interventions in the money markets.
Banking Sector Stability: Improving banks' liquidity management practices through accurate forecasts.

4. Building Comprehensive Macroeconomic Forecasting Model

Overview: This sub-track is dedicated to the development of comprehensive macroeconomic forecasting models that integrate multiple advanced technologies. These models aim to provide holistic forecasts of economic variables, aiding policymakers, and researchers in decision-making.

Key Topics:

Model Frameworks: Constructing frameworks that integrate AI, ML, Big Data, and econometric models.
Data Integration: Combining diverse data sources, including financial, economic, and social data.
Scenario Analysis: Utilizing blockchain for secure and transparent scenario planning and simulation.
Predictive Accuracy: Enhancing model accuracy through continuous learning and adaptation.

Applications:

Economic Policy Formulation: Providing robust forecasts to guide economic policy decisions.
Risk Assessment: Enabling comprehensive risk assessments and contingency planning.
Business Strategy: Assisting businesses in strategic planning with accurate economic forecasts.

5. Applications in Engineering Economy

Overview: This sub-track focuses on methodologies and technologies for applications specifically within the context of engineering projects. Understanding the natural rate of interest is crucial for evaluating project viability, financing costs, and investment decisions.

Key Topics:

Advanced Econometric Techniques: Utilizing AI and ML to refine the estimation of the natural rate of interest for engineering projects.
Project-Specific Data Analysis: Leveraging Big Data to analyze historical and real-time data relevant to engineering projects, such as cost overruns, project delays, and market demand fluctuations.
Historical Comparisons: Applying blockchain technology for maintaining transparent records of historical interest rate data related to engineering projects.
Implications for Project Financing: Discussing how accurate r* estimation can influence the financing and economic feasibility of large-scale engineering projects.

Applications:

Investment Decision Making: Enhancing investment strategies by integrating refined natural rate of interest estimates.
Project Feasibility Studies: Improving the accuracy of feasibility studies for engineering projects through better interest rate estimation.
Cost-Benefit Analysis: Utilizing accurate interest rate estimates to perform better.

6. Rational Expectations

Overview: The sub-track on Rational Expectations in the context of AI, Machine Learning (ML), and Big Data will delve into the intricate relationships between economic theories, particularly the Rational Expectations Hypothesis (REH), and the transformative impacts of AI and ML technologies. As economies increasingly leverage big data and advanced computational models, understanding how expectations are formed, revised, and acted upon becomes crucial. This sub-track aims to explore these dynamics and their implications for economic modeling, policymaking, and market behaviors.

Key Topics:

Rational Expectations and AI/ML Integration:

Theoretical foundations of rational expectations in the age of AI.
Comparative analysis of traditional economic models vs. AI-enhanced models in predicting economic outcomes.
Case studies on the application of AI/ML in forecasting and policy simulations.

Big Data and Expectation Formation:

The role of big data in shaping and updating rational expectations.
Methods for extracting meaningful insights from big data to inform expectation models.
Impact of real-time data processing on the accuracy and timeliness of expectation adjustments.

Behavioral Economics and Rational Expectations:

Interaction between AI-driven behavioral insights and rational expectations.
Modifying rational expectations models to incorporate behavioral anomalies detected through AI.
Implications for market efficiency and stability.

7. Technology Embedded Sustainability and Business for Economic and Social Development

Smart Moves: Engineering the Future of Integrated Mobility
Profit and Purpose: The Finance of Sustainability
Quantum computing: Impact on energy efficiency and sustainability.
Blockchain-based secure optimized traceable scheme for smart and sustainable food supply chain.
AI-Driven Green Revolution: Accelerating Sustainable Transformation
IoT and Smart Cities: Building Sustainable Urban Ecosystems.
AI-Driven Climate Modelling: Predicting and Mitigating Climate Change
Carbon Countdown: Accelerating the Net-Zero Transition
Beyond Fossil Fuels: Navigating the New Energy Landscape
Emergence of Technologies in Sustainability
Triple Bottom Line Analytics: Quantifying Sustainability Performance
Sustainable Supply Chains: From Global to Local
Smart and climate resilient cities: Technologies and Innovations
AI-Driven Sustainability Management: Leveraging Machine Learning for Environmental Decision Support
Clean Energy Vectors: Innovative Production and Applications of Carbon-Neutral Fuels.
Carbon Capture and Storage (CCS): Capturing and Storing Carbon Emissions from Industrial Processes.
Green Data Centers: Implementing Energy-Efficient Technologies in Data Centers.

8. Role of New Age Technologies in Social and Economic Development

New Age Technologies and its application for economic and social development
Smart Cities and economic development
Strategies of Emerging Countries in the era of New Age Technology
Future of Mobile Apps
Reaching Consumers through Social-Media
Skill set for Industry 5.0
Virtual Teams for Innovation
Smart Global Supply Chain Management
Corporate Governance in the New Age Technology Driven Economy

9. Integrating technology for value creation in society and economy

E-commerce, E-Value chain, Project Management,
Sustainability and technology, Decision support system,
Inclusive growth,
IOT & Knowledge Management,
SMEs for sustainable development,
The new age start-up, Techno Entrepreneurship

10. Emerging Trends in Communication Technology, Computational Science, Economics, Finance and Business

Generative AI
Cyber Security
Data Mining in Finance and Business
Software Engineering
Deep Learning
Machine Learning in Economics and AI
Internet of Things
Cloud Computing
Security and Privacy
Natural Language Processing
Cryptograph