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Summary

This paper explores how central bank digital currencies (CBDCs) could enhance the delivery of social safety nets (SSNs). It assesses CBDC design features and their implications for payment administration and delivery. Findings suggest that using CBDCs solely as payment delivery solutions offers limited advantages over existing systems such as faster payment systems. However, leveraging CBDCs as payment administration platforms—with peer-to-peer transfers, decentralized ledger access, and advanced programmability—could transform SSN delivery by enabling agencies to automate transfers, operate independently from private financial intermediaries, and monitor transactions directly. These benefits come with significant challenges, including privacy concerns, compliance risks, and infrastructure requirements. The paper emphasizes that realizing CBDCs’ full potential for SSNs will depend on thoughtful integration with existing systems and a clear understanding of their comparative advantages. Aimed at social protection policymakers and finance specialists, it highlights the need for collaboration between CBDC developers and SSN administrators to ensure that digital currencies effectively support inclusive and efficient benefit delivery.

Subject:

Blockchain and DLT,
Central Bank digital currencies,
Smart contracts,
Technology

Keywords:

Blockchain and DLT,
Central Bank digital currencies,
Central Bank Digital Currencies,
Financial Inclusion,
Fintech,
Government Transfers,
Payment Systems,
Smart contracts,
Social Safety Nets

• In recent days, Webus International Limited announced its Wetour platform will integrate Expedia Group’s newly launched AI-powered Trip Planner and B2B APIs, aiming to boost hotel booking efficiency and personalized travel experiences for global users.

• This move highlights increasing adoption of Expedia’s AI-driven capabilities by partners, signaling expanding influence of its tools across the online travel industry.

• We’ll examine how Wetour’s adoption of Expedia’s AI suite could enhance B2B growth and reshape Expedia Group’s investment narrative.

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Expedia Group’s investment story centers on the belief that artificial intelligence and deepening B2B alliances can drive sustained revenue and margin expansion, particularly as digital booking trends accelerate globally. The recent Wetour integration of Expedia’s AI-powered Trip Planner underscores technological differentiation and growing demand from partners, but does not materially offset persistent reliance on U.S. travel health and consumer spending, factors that still dominate near-term catalysts and risk. Investors may view this as a long-term growth lever rather than a near-term game-changer.

Among recent announcements, the October rollout of Smart Trip AI™ and suite of B2B APIs is highly relevant, as it forms the technical foundation for Expedia’s new partnerships like Wetour. This aligns with analyst expectations for higher B2B and recurring revenue streams, underpinning net margin stability, but also reflects broader efforts to cushion the business from cyclical consumer softness while increasing technology-driven differentiation.

But while these advances are promising, investors should still be mindful of increasing competition and the risk that more AI-enabled travel platforms could…

Read the full narrative on Expedia Group (it’s free!)

Expedia Group’s outlook forecasts $16.9 billion in revenue and $2.1 billion in earnings by 2028. This projection assumes a 6.4% annual revenue growth rate and a $1.0 billion increase in earnings from the current $1.1 billion.

Uncover how Expedia Group’s forecasts yield a $224.30 fair value, in line with its current price.

Eight members of the Simply Wall St Community set fair value estimates for Expedia Group ranging from US$132.67 to US$423.46, covering multiple viewpoints. With competition rising across AI travel platforms, considering this wide spectrum of expectations may offer context for your own assessment.

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This article by Simply Wall St is general in nature. We provide commentary based on historical data and analyst forecasts only using an unbiased methodology and our articles are not intended to be financial advice. It does not constitute a recommendation to buy or sell any stock, and does not take account of your objectives, or your financial situation. We aim to bring you long-term focused analysis driven by fundamental data. Note that our analysis may not factor in the latest price-sensitive company announcements or qualitative material. Simply Wall St has no position in any stocks mentioned.

Companies discussed in this article include EXPE.

Have feedback on this article? Concerned about the content? Get in touch with us directly. Alternatively, email editorial-team@simplywallst.com

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Summary

This paper evaluates three approaches to address parameter proliferation issue in nowcasting: (i) variable selection using adjusted stepwise autoregressive integrated moving average with exogenous variables (AS-ARIMAX); (ii) regularization in machine learning (ML); and (iii) dimensionality reduction via principal component analysis (PCA). Utilizing 166 variables, we estimate our models from 2007Q2 to 2019Q4 using rolling-window regression, while applying these three approaches. We then conduct a pseudo out-of-sample performance comparison of various nowcasting models—including Bridge, MIDAS, U-MIDAS, dynamic factor model (DFM), and machine learning techniques including Ridge Regression, LASSO, and Elastic Net to predict China’s annualized real GDP growth rate from 2020Q1 to 2023Q1. Our findings suggest that the LASSO method outperform all other models, but only when guided by economic judgment and sign restrictions in variable selection. Notably, simpler models like Bridge with AS-ARIMAX variable selection yield reliable estimates nearly comparable to those from LASSO, underscoring the importance of effective variable selection in capturing strong signals.

Summary

This paper presents the Mauritius Quarterly Projection Model (QPM), the semi-structural analytical tool that underpins the modernized Forecasting and Policy Analysis System of the Bank of Mauritius (BOM). The model is designed to capture the salient features of the domestic economy, including key monetary policy transmission channels and the recently introduced flexible inflation targeting framework. Relative to canonical QPM structures, it also incorporates a parsimonious fiscal block and a labor market block, providing key insights on broader macroeconomic dynamics and enriching the policy advice. The model optimally balances theoretical consistency—evident in coherent shock propagation and policy responses—and empirical reliability, as reflected in its strong in-sample forecasting performance. The practical use of the Mauritius QPM in the context of the BOM’s regular forecasting cycles for the production of baseline projections, counterfactual simulations and alternative scenarios, together with the corresponding model-based economic narratives, make it a critical component of the BOM’s forward-looking monetary policy formulation.

Subject:

Exchange rates,
Foreign exchange,
Inflation,
Labor,
Labor markets,
Output gap,
Prices,
Production,
Real effective exchange rates,
Real wages,
Wages

Keywords:

Exchange rates,
Forecasting and Policy Analysis,
Global,
Inflation,
Labor markets,
Mauritius,
Monetary Policy,
Output gap,
Quarterly Projection Model,
Real effective exchange rates,
Real wages,
Transmission Mechanism,
Wages