Policy Impact Forecasting

🔍 Evaluating Policy Impacts with Synthetic Control Methods: Recent Advances and Tools How do we know if the policies we create have the effects we desire? What if a policy is enacted in only one place (like a state) but not others? Enter the Synthetic Control Method (SCM) which offers a useful framework for these kinds of problems, especially when randomized experiments are infeasible (which is most of the time). 📘 Foundations of Synthetic Controls Introduced by Abadie and Gardeazabal (2003) and further developed by Abadie, Diamond, and Hainmueller (2010), SCM constructs a weighted combination of control units to approximate the counterfactual of a treated unit. If you are not familiar with SCM, a great place to start is Scott Cunningham's excellent book Causal Inference: The Mixtape: https://lnkd.in/g7YMb7KT. If you're interested in more advanced methods, keep reading. 1. Augmented Synthetic Control Method (ASCM): Combines SCM with regression adjustments to improve estimation accuracy. Reference: Ben-Michael, Feller, & Rothstein (2021). 2. Generalized Synthetic Control (GSC): Extends SCM to accommodate multiple treated units and time-varying effects. Reference: Xu (2017). 3. Synthetic Diff in Diff (synthdid): Accounts for staggered adoption or variable treatment times. Reference: Arkhangelsky et al. (2021). 🛠️ R Packages for Implementation Synth: https://lnkd.in/gQAc4NU3 augsynth: https://lnkd.in/gqzqs6EV gsynth: https://lnkd.in/gmAzuS3k synthdid: https://lnkd.in/gWVkRUhT tidysynth: https://lnkd.in/gSScV-ya 📚 Further Reading Abadie, A., Diamond, A., & Hainmueller, J. (2010). Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California’s Tobacco Control Program. Journal of the American Statistical Association, 105(490), 493–505. https://lnkd.in/g_jwdfri Arkhangelsky, D., et al. (2021). Synthetic Difference-in-Differences. American Economic Journal: Applied Economics, 13(2), 1–35. https://lnkd.in/gnWhCX4y Ben-Michael, E., Feller, A., & Rothstein, J. (2021). The Augmented Synthetic Control Method. Journal of the American Statistical Association, 116(536), 1789–1803. https://lnkd.in/gfSNPYih Xu, Y. (2017). Generalized Synthetic Control Method: Causal Inference with Interactive Fixed Effects Models. Political Analysis, 25(1), 57–76. Link Hazlett, C., & Xu, Y. (2018). Trajectory Balancing: A General Reweighting Approach to Estimating Treatment Effects in Synthetic Control Designs.  https://lnkd.in/g9d4_895 #CausalInference #SyntheticControl #PolicyEvaluation #RStats

To create good policy you need responsible foresight, enabling ethical, sustainble, accountable future design. AI now can massively enable human-centered responsible foresight, in helping address uncertainty, assess risks, and set policies for creating better futures. María Pérez Ortiz's new paper "From Prediction to Foresight: The Role of AI in Designing Responsible Futures" describes responsible foresight in policy and the role of computational foresight tools. Notable approaches to using AI in responsible foresight include: 🤝 Participatory Futures for Inclusive Planning. Engaging diverse stakeholders in foresight practices democratizes the future-planning process. AI tools streamline public participation by analyzing preferences, simulating collective decisions, and creating urban plans that reflect community values, fostering equity and resilience. 🧠 Superforecasting for Precision and Insight. Superforecasting uses disciplined reasoning and probabilistic thinking to predict uncertain events. AI-powered assistants improve human forecasting accuracy by 23%, aggregating data and refining predictions through collective intelligence and advanced analytical models. 🌐 World Simulation for Systemic Insights. Advanced modeling frameworks simulate interconnected global systems, enabling policymakers to test "what-if" scenarios. AI accelerates these simulations, providing precise forecasts and dynamic platforms to visualize the long-term consequences of policy decisions across economic, social, and environmental domains. ⚙️ Simulation Intelligence for Decision Optimization. By integrating AI with high-fidelity simulations, simulation intelligence explores complex systems to uncover optimal strategies. This tool assists in crafting effective policies for urban planning, sustainable agriculture, and climate resilience, offering actionable pathways for addressing systemic challenges. 📜 AI-Assisted Narrative Techniques. Large language models contribute to speculative futures by generating detailed "value scenarios" that integrate ethical, technological, and societal considerations. These AI-driven narratives enable policymakers to visualize desirable outcomes and evaluate potential trade-offs. 🔗 Hybrid Intelligence for Enhanced Foresight. Combining human creativity with AI’s computational strengths creates a robust foresight framework. Intuitive interfaces, explainable AI, and participatory design ensure that tools remain transparent and aligned with ethical considerations, empowering policymakers to navigate complex challenges collaboratively. ♻️ Iterative Foresight with Feedback Loops. Continuous monitoring and real-time adaptation enhance foresight processes. AI’s ability to process evolving data and generate actionable insights ensures policies remain responsive, flexible, and aligned with long-term objectives. The power of AI in assisting foresight is just beginning to come to fruition.

Large language models: a primer for economists (https://lnkd.in/eJschCjr) & Systematic Interpretation of Central Bank Communication Large Language Models (LLMs) have revolutionized economic research by enabling advanced analysis of unstructured textual data such as policy statements, financial reports, and news articles. These models transform text into structured numerical representations, facilitating tasks like sentiment analysis, forecasting, and topic modeling. Their contextual understanding, enabled by transformer-based architectures, makes them particularly effective in analyzing economic narratives. For instance, LLMs can evaluate market sentiment or interpret the tone of central bank communications, offering valuable insights into monetary policy impacts. A study of US equity markets demonstrated this by analyzing over 60,000 news articles to identify key drivers such as fundamentals, monetary policy, and market sentiment, linking these themes to stock market movements. Before the explosion of LLMs, I conducted research with my colleagues at Morgan Stanley to systematically analyze central bank communication using earlier machine-learning techniques. Specifically, we trained a Convolutional Neural Network (CNN) to assess the degree of hawkishness or dovishness in FOMC communications. This effort led to the development of the MNLPFEDS Index, which proved to be a powerful tool for anticipating monetary policy actions up to a year in advance. The index provided valuable insights into potential inflection points in the monetary cycle and their effects on rates, the yield curve, and the USD. This work highlighted the predictive power of communication analysis, even before the advent of the sophisticated transformer models now driving advancements in LLMs. LLMs and earlier machine-learning approaches, like CNN-based analysis, each bring unique strengths to understanding monetary policy and market dynamics. While LLMs excel in processing vast and complex datasets with contextual depth, their capabilities can be further enhanced through fine-tuning for domain-specific tasks. This adaptability allows LLMs to specialize in areas like central bank communication, where nuances in tone and context are crucial. Combined with the foundational contributions of earlier models like the MNLPFEDS Index, fine-tuned LLMs provide economists with a comprehensive toolkit to analyze qualitative insights and integrate them into robust quantitative frameworks, enriching the understanding of policy effects and broader economic trends. #EconomicResearch #MonetaryPolicy #CentralBankCommunication #MachineLearning #ArtificialIntelligence #NaturalLanguageProcessing #LLMs #DeepLearning #EconomicForecasting #SentimentAnalysis #TextAnalysis #DataScience #MacroEconomics #QuantitativeResearch

The Economic Ripple Effect: Simulating the Impact of U.S. Tariffs In our latest analysis, we examined the impact of U.S. tariffs on key economic sectors across major economies, using World Bank and U.S. Census data. Our study simulated 10% and 25% tariffs on agriculture, industry, and services, assessing their effects on GDP contributions and trade balances. Key Insights: ✅ A 10% tariff led to measurable declines in sectoral GDP contributions, with industry and agriculture being hit the hardest. ✅ A 25% tariff had severe repercussions on exports and imports, potentially disrupting global supply chains. ✅ Trade-dependent nations like China, Germany, and Mexico faced significant GDP contractions in key sectors. ✅ Forecast simulations predict a prolonged negative impact on labor force participation and employment in affected industries. Our study highlights the delicate balance of trade policies and the necessity for strategic decision-making in global commerce. Tariffs don’t just impact one country—they create ripple effects across industries worldwide. Data Sources: World Bank API, U.S. Census Bureau, and simulated economic models. #TradeImpact #USTariffs #GlobalEconomy #EconomicAnalysis #DataDriven #TradeWars #GDPImpact #EconomicPolicy #WorldBankData

🔍 How should we measure the impact of economic policy? Using VARs or Local Projections (LPs)? 📈 Estimating the dynamic effects of a shock—like an interest rate hike or a fiscal expansion—is at the heart of applied macroeconomics. Two methods dominate the field: ✔️ Vector Autoregressions (VARs) ✔️ Local Projections (LPs) A new NBER working paper by Olea, Plagborg-Møller, Qian, and Wolf (2025) offers a clear and rigorous guide: 🎯 If your goal is to make credible causal inference—for example, to assess the effect of monetary policy on inflation or employment—use LPs or VARs with many lags. 🚫 Avoid short-lag VARs when reporting uncertainty: their confidence intervals are often too narrow and unreliable, even if the point estimates look sharp. 👀 Why does this matter for policy? Because when central banks, ministries of finance, or international institutions need to decide how much to adjust interest rates or public spending, they don’t just need forecasts—they need robust causal answers. LPs provide a better foundation for that: ✅ Lower bias ✅ More robust to model misspecification ✅ Reliable uncertainty quantification 📊 If you work in economic research or policy analysis, this paper is absolutely worth your time. 📎 Paper: “Local Projections or VARs? A Primer for Macroeconomists” 🧠 Authors: José Luis Montiel Olea, Mikkel Plagborg-Møller, Eric Qian, Christian K. Wolf 🔗 https://lnkd.in/eY9_UGK9

In Washington, the tendency is to focus on headlines.  But the real impact of policy shifts often lies in the ripple effects. A lesson from 30 years of policy analysis:  Direct changes grab attention. Secondary effects determine outcomes. When building scenario plans for policy shifts, smart organizations look three layers deep: Layer 1: Direct Impact • New regulations • Tax changes • Compliance requirements Layer 2: Market Response ̐• Supplier reactions • Customer behavior shifts • Competitor repositioning Layer 3: Industry Evolution • Supply chain restructuring • Innovation incentives • Partnership dynamics Take financial regulation:  While everyone focuses on immediate compliance costs, the real transformation often comes from how the market adapts – creating new opportunities for those who planned ahead. Key to remember: The organizations that thrive through policy transitions aren't just preparing for change. They're positioning themselves to capitalize on the second and third-order effects that others miss.

A new study uses #Large_Language_Models to split #Fed_news into current actions and future hints. It finds that immediate policy news moves #Treasury prices fast, while forward-looking signals help predict short-term #US_Treasury_yields. This approach beats old word-list methods and enhances asset pricing, duration, and liquidity risk management. A novel LLM-based framework disentangles current vs forward-looking Fed news to predict short-term US Treasury yield dynamics with impressive accuracy.  Key Findings: 🔍 Policy Split: LLMs classify monetary news into immediate actions and forward-looking signals, with current info tightly co-moving with yields while forward-looking info offers genuine prediction power. 🔮 Yield Signals: Forward-looking news robustly forecasts next-day yield changes—particularly for 3-month and 2-year Treasuries—surpassing traditional dictionary methods. 🚀 Economic Impact: Bottom-to-top decile shifts in forward-looking sentiment translate into multi-basis-point moves in short-term yields that persist over several weeks. 💡 Practitioner Tips: Integrate LLM-derived forward-looking sentiment into duration and asset-pricing models, monitor daily policy-trend indices to capture evolving market expectations between Fed meetings, and combine these signals with traditional analytics (e.g., VIX, MOVE) for sharper yield forecasts within a more resilient risk-management framework. By Dr. Matthias Apel and Maximilian Stroh from Quoniam Asset Management GmbH 👉 Read the full study on SSRN:5227409 ✅ If you are interested in keeping up with new papers and research in Quant Finance/AI/LLMs, Sign-Up to our Monthly Quant Finance and AI/LLM Research Newsletter, link in the comments. #FinTech #AI #MachineLearning #LLM #Finance #TreasuryYields #FixedIncome #MonetaryPolicy #AssetPricing #DurationManagement #RiskManagement #CentralBank #EconomicInsights #YieldCurve #QuantFinance #DataScience #FinancialAnalytics #Innovation #MacroEconomics #PredictiveModeling

CRE Industry - Podcast Notes Five industry podcasts from the past few weeks show a consistent shift from cautious observation to active positioning. Key themes include capital reallocation, policy-driven market dynamics, and selective opportunity identification. Capital Deployment Patterns Source: CBRE's "Weekly Take" * Multi-region strategies are now standard practice, driven by interest rate volatility and policy uncertainty * Class A office assets in core CBDs are returning as acquisition targets * Class B and C properties remain avoided * AI integration is accelerating underwriting processes * Capital allocation shifting from asset class focus to demand-driven geographic nodes Macro Risk Integration Source: Walker Webcast * Political volatility is now a core market variable * Policy impacts: Tariff policies and industrial strategy overriding conventional economic models * Underwriting assumptions must account for mid-cycle rule changes * ESG considerations increasingly framed through national security requirements * Potential 10-year yields above 5% complicating refinancing cycles * International capital being driven toward hard asset allocations Distressed Asset Dynamics Source: TreppWire * Office market distress: Record loan special servicing levels * Servicers favoring discounted note sales over REO procedures * SOFR pressure affecting debt service coverage on 2021-vintage loans * Credit market changes: Private credit expansion with reduced covenant structures * Industrial and data center CMBS maintain near-zero delinquency rates * Sector bifurcation between distressed office and resilient alternative properties Legislative Impact Analysis Source: America's CRE Show * "One Big Beautiful Bill" incentive structure... * Depreciation benefits: 30-year schedules for build-to-rent and EV manufacturing assets increase modeled IRRs by 120-180 basis points * Cost impacts: Federal procurement requirements may increase construction costs by 7% over two years * Capital markets: Tradable tax credit provisions could develop secondary market depth and reduce weighted average cost of capital for development projects Financial System Stress Points Source: NAIOP's Inside CRE * Community bank exposure through concentrated CRE portfolios as rates remain elevated through Q4 * Inflation scenarios: Tariff acceleration could drive inflation to 4%, increasing cap rates by 50-75 basis points * Credit markets: Private credit growth continues but with reduced transparency and compressed refinancing windows * Recommendation: Immediate refinancing over waiting for policy clarity Market Positioning Summary Industry consensus indicates: * Returning liquidity with altered parameters * Debt availability exists at higher costs * Policy decisions increasingly drive market outcomes independent of underlying fundamentals * Rising hard asset demand among operators capable of rapid repricing and repositioning strategies

We can expect inflation and tariffs to drive bigger forecast misses in 2025. Financial models should capture the impact of both. Here's how: 𝗖𝗼𝘀𝘁 𝗼𝗳 𝗚𝗼𝗼𝗱𝘀 𝗦𝗼𝗹𝗱 (𝗖𝗢𝗚𝗦) 𝗮𝗻𝗱 𝗜𝗻𝘃𝗲𝗻𝘁𝗼𝗿𝘆 The most obvious negative impact of inflation and tariffs is on the cost of imported goods or raw materials. While inflation drives the cost up over time, tariffs artificially inflate the cost of goods through what's essentially an import tax. Higher input costs from inflation and tariffs also increase the carrying cost of inventory. How can a financial analyst capture the impact appropriately? - Break out material costs into domestic and imported components. - Apply the inflation rates to both and apply the impact of tariffs to the imports. 𝗦𝗮𝗹𝗲𝘀 𝗥𝗲𝘃𝗲𝗻𝘂𝗲 If you think that tariffs will only penalize foreign businesses, think again. Higher input costs may force companies to raise rates, potentially impacting the price consumers pay. It may also have a profound impact on the supply and demand. It won't be a matter of consumers choosing domestic goods over foreign goods. It might be a permanent reduction in goods produced. How can a financial analyst capture the impact appropriately? - Don't just forecast sales in total. Make it the product of volume and price, allowing for each to be modeled independent of the other. - Consider contingency scenarios, which can be activated based upon customer responses and the implications on revenue targets. 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗻𝗴 𝗘𝘅𝗽𝗲𝗻𝘀𝗲𝘀 Inflation and tariffs can increase operating costs across most categories. This includes wages, utilities, and logistics among others. Financial planning & analysis professionals may need to aid in the restructuring of operations depending on the outcomes of their forecast models. How can a financial analyst capture the impact appropriately? - Include an inflation escalation factor for key operating expenses. - Separate tariff-related costs from inflation for greater visibility and influence. Inflation and tariffs in 2025 will have major disruptions on a global scale. Forecasting failure comes from a lack of consideration about future realities. Operational failure comes from a lack of imagination around how to deal with them. #BigIdeas2025

What if you could track the economic impact of major policy shifts before it hits earnings season? When new tariffs are announced, investors ask: 📉 Who’s exposed? 🏭 How will supply chains shift? 💸 Will companies pass on costs or absorb them? 🛒 How are consumers responding? 📊 What happens to company results? Most investors build a thoughtful thesis and make their investment, but then wait for lagging indicators to confirm if they were right. Alternative Data offers earlier answers, if it’s framed around the right investment questions. In Part I of a new two-part series, The Data Score outlines a causal linking model to break a policy shock into measurable stages—from announcement to sourcing changes, price shifts, consumer behavior, and corporate outcomes. We then apply a Bayesian framework to connect real-time data across that chain: • AIS shipping activity • Job postings • Web-mined pricing and inventory • Consumer transactions • And much more Each signal adds evidence as the policy impact causes measurable outcomes through the supply chain. Together, they build a probabilistic picture—weeks ahead of earnings or GDP reports. This approach turns big-picture debates into structured, observable behavior. 📬 Subscribers have Part I in their inbox now. If you’re not yet subscribed, the link to join is in my profile. Part II is planned for next week. #AlternativeData #DataDrivenDecisions #Tariffs #SupplyChain #MacroAnalysis