America Is Borrowing $43.5 Billion a Week: How to Understand It with Excel and Financial Modeling
America’s fiscal position is deteriorating at a pace that is hard to grasp without numbers in front of you. In the first four months of the current fiscal year, the US borrowed roughly $43.5 billion every week. If this trajectory continues and interest rates remain elevated, annual interest payments on the national debt are on track to exceed $1 trillion by 2026.
For anyone working in finance, policy analysis, investing, or even just trying to understand what this means for markets and the economy, financial modeling in Excel becomes more than a technical skill — it’s a way to make sense of these staggering figures.
Below is a complete guide to building strong financial modeling skills in Excel, plus how to apply them to questions like US debt sustainability and interest costs.
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Why Financial Modeling Matters When Debt Explodes
When a country is borrowing tens of billions a week and facing trillion‑dollar interest bills, you need structured models to answer questions like:
– What happens to interest expense if rates stay high for five more years?
– How fast does total debt grow if borrowing continues at the current pace?
– At what point does interest crowd out other government spending?
– How do different growth, inflation, and rate scenarios change the outcome?
Excel is still the dominant tool for building these kinds of projections. Understanding how to construct clear, flexible, and auditable models will help you:
– Analyze government debt dynamics and fiscal risk
– Value bonds and fixed‑income instruments tied to sovereign credit
– Assess macroeconomic scenarios for investment decisions
– Present complex fiscal stories in a way decision‑makers can follow
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Core Best Practices for Financial Modeling in Excel
1. Keep Your Workbook Clean and Consistent
A professional model should be easy to navigate, even for someone opening it for the first time.
– Use a clear, logical structure: Inputs, Calculations, Outputs on separate sheets.
– Standardize your formatting: one color for inputs, another for formulas, another for links.
– Use consistent naming conventions for tabs, ranges, and scenarios.
– Avoid clutter: remove unused sheets, orphaned formulas, and random tests.
The cleaner the workbook, the easier it is to trace how you get from assumptions (e.g., interest rates, borrowing levels) to results (e.g., total interest cost, debt-to-GDP).
2. Prefer Simple, Transparent Formulas
Complicated, deeply nested formulas are a major source of errors. It’s better to:
– Break complex logic into multiple helper columns.
– Use one operation per formula where possible (e.g., separate growth, indexing, and aggregation steps).
– Avoid unnecessary array formulas unless they truly add value.
A rule of thumb: five easy-to-read columns are better than one unreadable formula with five nested functions. In a debt model, for example, you might separate:
1. Starting balance
2. New borrowing
3. Principal repayments
4. Interest rate
5. Interest expense
Then combine them in a separate column for ending balance.
3. Master the Essential Excel Functions for Modeling
To build robust financial models, you should be completely comfortable with the following functions and concepts:
– LOOKUP functions:
– `INDEX` + `MATCH`
– `XLOOKUP`
– `VLOOKUP` (still widely used, even if not ideal)
– Conditional aggregations:
– `COUNTIFS`
– `SUMIFS`
– `AVERAGEIFS`
– Dynamic lists and filtering:
– `UNIQUE`
– `FILTER`
These functions let you:
– Pull the right assumptions for each year or scenario
– Aggregate data across time, entities, or categories
– Dynamically slice and filter cash flows, rates, or instruments
In the context of US debt, they help you:
– Match each debt issue to the correct interest rate and maturity
– Sum interest costs by year, by instrument, or by scenario
– Filter for specific categories (e.g., short-term vs long-term debt)
4. Automate Recurring Tasks
Many finance roles involve repetitive reporting: weekly dashboards, monthly results, quarterly forecasts. Manual work is not only slow — it’s prone to mistakes.
Whenever you perform the same steps more than a couple of times, ask:
– Can I convert this into a repeatable template?
– Can I use formulas instead of copy-paste?
– Can I automate data imports and cleaning?
Investing time in automation upfront saves hours later and reduces the risk of error in high-stakes outputs like debt forecasts or interest cost projections.
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Going Beyond Basics: Power Query and Power Pivot
As datasets grow — for example, when you work with large series of Treasury issuance, yield curves, and historical budget data — traditional Excel tables become harder to manage.
This is where Power Query and Power Pivot become extremely useful:
– Power Query
– Automates data import from files, folders, and databases
– Cleans, reshapes, and merges data without manual manipulation
– Lets you refresh everything with one click when new data arrives
– Power Pivot
– Allows data modeling with relationships between tables
– Enables fast calculations across large datasets
– Supports advanced measures using DAX formulas
If you regularly analyze recurring, structured data (e.g., monthly debt figures, interest payments, economic indicators), learning these tools early dramatically increases your efficiency and the sophistication of your models.
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Learning from Real Financial Models and Resources
To move from theory to practice, it helps to study how professionals structure their work. Several platforms and names are frequently recommended as starting points for financial modeling and valuation:
– Financial modeling and valuation content:
– Wall Street Oasis
– Damodaran Online
– Private Equity Bro
– Structured courses and certifications:
– CFI FMVA
– Wall Street Prep
– Breaking Into Wall Street
These provide step‑by‑step instruction on building three-statement models, DCFs, M&A and LBO models — skills that translate directly to building debt sustainability models and macro projections.
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Excel Add‑Ins That Boost Modeling Productivity
Power users often rely on professional add‑ins that streamline repetitive tasks, standardize formatting, and enhance analysis. Among popular options:
– Macabacus
– Endex AI
– S&P CapIQ
These tools can:
– Automate common formatting and auditing tasks
– Speed up formula building and error checking
– Integrate financial and market data directly into Excel
While not strictly necessary to get started, they can be a meaningful productivity boost in demanding roles.
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Online Tutorials and Practice Platforms
To build fluency, consistent practice matters more than passive watching. A practical progression:
1. Use general tutorial platforms like YouTube or LinkedIn Learning to learn functions and basic models.
2. Apply those skills to structured case studies and timed exercises. Tools marketed as “leetcode for financial modeling” (for example, Quantus Finance) can help you:
– Rebuild models from scratch under time pressure
– Work through realistic investment banking or PE-style tasks
– Get feedback on structure, logic, and formatting
3. Study real-world decks, such as anonymized M&A or board presentations, to see how professionals:
– Connect narrative and numbers
– Present complex models in a concise storyline
– Translate dense analysis into clear recommendations
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How to Ask for Guidance at Work
If you’re already in a finance role and have been told to “brush up on Excel,” be direct and practical:
– Ask your manager to share sample workbooks you will actually use.
– Request examples of typical reports, forecast models, or dashboards.
– Use those files as blueprints to guide your practice.
A simple message like, “Can you send me some of the Excel files I’ll use most often so I can tailor my learning to those formats?” can save you weeks of unfocused study.
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Building a Simple Model for US Debt and Interest Costs
To connect all of this to the current fiscal situation — borrowing around $43.5 billion per week and interest payments heading toward $1 trillion a year by 2026 — you can build a straightforward Excel model with:
1. Assumptions sheet
– Starting debt balance
– Weekly or monthly borrowing rate
– Average interest rate on new debt
– Assumptions for GDP growth and inflation
– Scenarios for future interest rates (base, high, low)
2. Debt schedule
– Beginning debt balance
– New net borrowing (e.g., 43.5 billion × weeks)
– Principal redemptions, if modeled
– Interest rate per period
– Interest expense per period
– Ending debt balance
3. Interest cost summary
– Total annual interest expense by year
– Interest expense as % of federal revenue
– Interest expense as % of GDP
4. Scenarios and sensitivity tables
– What happens if borrowing slows or accelerates?
– What if rates stay higher for longer?
– How quickly do we cross $1 trillion in annual interest under each case?
This kind of model turns headline figures into dynamic projections. Instead of simply reading that interest could top $1 trillion by 2026, you can see the path, test assumptions, and understand the underlying drivers.
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Interpreting the $43.5 Billion per Week Borrowing in a Model
Once you put that weekly figure into Excel, several insights emerge:
– Annualized borrowing:
– 43.5 billion per week × ~52 weeks ≈ over 2.2 trillion in annual net borrowing, if sustained.
– Compounding interest:
– New borrowing adds to the principal, which then accrues interest at whatever rate prevails.
– Interest rate sensitivity:
– A seemingly small change in average interest rate (say, from 3% to 4%) on tens of trillions of dollars in debt can mean hundreds of billions more in annual interest over time.
By modeling these relationships, you can quantify just how sensitive future interest costs are to policy, growth, and rate paths.
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Common Mistakes in Financial Modeling (and How to Avoid Them)
When dealing with numbers as large and politically sensitive as national debt and interest costs, errors have consequences. Frequent pitfalls include:
– Hard‑coding numbers in formulas instead of referencing assumption cells
– Mixing units (weekly vs annual, nominal vs real) without clear labels
– No version control, leading to confusion about which workbook is final
– Circular references introduced accidentally, breaking the model
– Poor documentation, making it impossible for others to review or audit
Avoid these by:
– Keeping all assumptions in one place, clearly labeled
– Using consistent units and documenting any conversions
– Adding a simple “version info” section in a cover sheet
– Using Excel’s formula auditing tools to track dependencies
– Commenting complex formulas or steps so your logic is transparent
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How to Progress from Beginner to Advanced Modeler
If your ultimate goal is to build or understand complex macro and debt models, you can follow a staged roadmap:
1. Foundations (Weeks 1–4)
– Learn fundamental Excel functions, formatting, and shortcuts
– Build simple models: loan amortization, single-company projections
2. Intermediate (Months 2–4)
– Three-statement financial models (income statement, balance sheet, cash flow)
– Basic valuation: DCF, sensitivity analysis, scenario analysis
3. Advanced (Months 4–9)
– Debt schedules with multiple tranches and varying maturities
– Integrated models including macro variables (rates, inflation, GDP)
– Complex scenario frameworks and Monte Carlo simulations (if needed)
4. Expert (Ongoing)
– Designing frameworks that can handle large datasets and frequent updates
– Connecting models to real decision-making in policy, investment, or corporate strategy
Throughout this path, constantly revisit real macro questions like the US fiscal outlook. Treat news headlines as prompts for new model features or scenarios.
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Applying Your Skills: From Classroom to Real-World Policy and Markets
Once you can comfortably build and interpret models, you’ll be better equipped to:
– Evaluate whether current borrowing levels are sustainable
– Understand how higher interest rates affect future tax burdens
– Assess potential impacts on inflation, currency strength, and risk premia
– Communicate complex fiscal risks convincingly to non-technical audiences
In an environment where interest on the national debt alone could surpass $1 trillion annually by 2026, the ability to rigorously analyze and clearly present fiscal dynamics is a significant professional advantage.
Financial modeling in Excel is not just about passing interviews or impressing colleagues — it’s a way to turn abstract, overwhelming numbers into structured insight, whether you’re analyzing a single company or an entire country’s balance sheet.