QBearLab Retirement Methodology

Methodology maintained by: QBearLab Analytics Team
Methodology version: 1.1
Methodology last reviewed: August 2026
Tax rules: 2026 U.S. federal tax rules unless otherwise stated
Purpose: Educational retirement scenario analysis

QBearLab methodology is an analytical tool for exploring retirement trade-offs. It is not a prediction of future investment returns and does not provide individualized investment, tax, legal, insurance, or financial-planning advice.

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Purpose of the QBearLab Methodology

QBearLab case studies use a quantitative retirement projection framework to evaluate how spending, investment returns, inflation, taxes, Social Security, healthcare, real estate, and other financial events can interact over a retirement lasting several decades.

Rather than treating a single projection as a prediction, QBearLab combines deterministic cash-flow analysis with stochastic scenario modeling to evaluate how retirement plans behave under different economic and market conditions.

This page documents the core assumptions, calculation methods, reporting conventions, limitations, and data sources used in QBearLab analysis.

What the QBearLab Methodology Considers

A typical QBearLab retirement analysis may incorporate:

Current investment balances
Taxable, traditional retirement, and Roth accounts
Employment and pension income
Social Security
Annual spending and inflation
Federal and applicable state taxes
Capital gains
Roth conversions
Required minimum distributions (RMDs)
Medicare premiums and IRMAA
Pre-Medicare healthcare assumptions
Marketplace/ACA assumptions when explicitly modeled
Real-estate values and rental income
Mortgage balances
Planned property sales
One-time expenses
Estate and legacy goals
Alternative market and inflation scenarios
Case-specific stress tests

Individual case studies may use only a subset of these features. Each published case should disclose the assumptions that materially affect its results.

How the Methodology Fits Together

A QBearLab projection processes household assumptions year by year:

QBearLab Projection Flowchart

The purpose of this process is not to predict one exact future. It is to show how a retirement plan responds when important assumptions change.

How to Interpret QBearLab Results

A QBearLab projection should not be read as a forecast that a portfolio will contain exactly a stated dollar amount in a particular future year. Instead, the projection shows the mathematical consequence of the assumptions used in that scenario.

Deterministic results answer:

What happens if the stated base assumptions occur year after year?

Monte Carlo results answer:

How does the plan behave when market returns, inflation, and economic conditions arrive in different sequences?

A strong retirement plan is therefore not simply one with a high ending balance. Depending on the case, QBearLab also considers:

Liquidity
Sequence-of-returns risk
Tax exposure
Dependence on property sales
Spending flexibility
Healthcare costs
Legacy requirements
Resilience under adverse scenarios

A Monte Carlo success rate is the percentage of QBearLab-simulated paths that satisfy the success criterion defined for that case. It is model-dependent. It is not an actuarial probability, a prediction, or a guarantee of real-world retirement success.

Two Types of QBearLab Projections

Base-Case Projection

A deterministic projection asks:

If the stated assumptions occur each year, what does the household’s financial trajectory look like?

It applies specified assumptions—such as portfolio returns, inflation, income, spending, tax rules, and major financial events—year by year to provide a transparent baseline.

Monte Carlo / Stochastic Analysis

The stochastic analysis asks:

What happens when market returns, inflation, and economic conditions do not arrive in a smooth sequence?

By varying the sequence of economic regimes, returns, and inflation, the model evaluates sequence risk and adverse scenarios that straight-line projections cannot capture.

Monte Carlo and Uncertainty Modeling

The Monte Carlo engine is designed to stress-test retirement plans against a range of potential macroeconomic paths.

Why Regime Switching

Financial markets do not exhibit constant returns or volatility. Periods of relatively normal growth can be interrupted by recessions, inflation shocks, or combinations of weak growth and elevated inflation.

QBearLab represents these changing conditions using a discrete-time Markov regime-switching framework. The model is conceptually informed by regime-switching methods in economic research, including James D. Hamilton’s work on discrete-state changes in economic time series.

The economic-regime parameters below are QBearLab scenario assumptions. They are designed to generate varied stress-test paths and should not be interpreted as forecasts of the probability, timing, or severity of future recessions or inflation events.

The Five Economic Regimes

For each regime, parameters define shifts from baseline expected return, volatility, and inflation.

1. Baseline

Description: A normal, healthy economic environment.
Return Shift: 0.0 percentage points
Volatility Shift: 0.0 percentage points
Base Inflation: 2.5%

2. Mild Recession

Description: A standard economic contraction or market correction.
Return Shift: -3.0 percentage points
Volatility Shift: +1.0 percentage points
Base Inflation: 2.0%

3. Severe Recession

Description: A major financial contraction with materially weaker returns and elevated volatility.
Return Shift: -8.0 percentage points
Volatility Shift: +5.0 percentage points
Base Inflation: 1.5%

4. Stagflation

Description: High inflation combined with poor economic growth and pressured asset returns.
Return Shift: -4.0 percentage points
Volatility Shift: +3.0 percentage points
Base Inflation: 6.0%

5. Inflation Spike

Description: A rapid rise in prices without as severe an impact on growth as the stagflation regime, but with elevated volatility.
Return Shift: -2.0 percentage points
Volatility Shift: +2.0 percentage points
Base Inflation: 5.0%

Transition Matrix

The model uses the following transition matrix to determine the likelihood of moving from one regime to another in the next modeled year.

These transition probabilities are QBearLab scenario-generation assumptions. They are not estimates of the actual probability of a future recession, inflation spike, or other macroeconomic event.

Current Regime \ Next Year	Baseline	Mild Recession	Severe Recession	Stagflation	Inflation Spike
Baseline	75%	15%	3%	5%	2%
Mild Recession	60%	25%	10%	3%	2%
Severe Recession	30%	40%	25%	3%	2%
Stagflation	50%	20%	5%	20%	5%
Inflation Spike	60%	10%	2%	15%	13%

Portfolio Return Assumptions

When a case does not specify another return profile, QBearLab may use the following default nominal return and volatility assumptions:

Portfolio / Asset Category	Expected Nominal Return	Annual Volatility
Default Equity Growth	8.0%	15.0%
Default Bond Yield	4.0%	5.0%
Default Generic Portfolio	7.0%	10.0%

These values are QBearLab modeling assumptions, not forecasts or guaranteed returns.

The Default Generic Portfolio is a simplified blended return/volatility assumption used when a case does not specify a more detailed portfolio return profile. It does not, by itself, imply that the household owns a particular fixed stock/bond allocation.

Unless a case states otherwise:

Return assumptions are expressed in nominal terms.
Regime shifts are applied to the applicable baseline return and volatility assumptions.
Case-specific assumptions override these defaults.
Investment-management fees are not separately deducted unless the case explicitly includes them.
Published case assumptions should disclose any materially different return, volatility, asset-allocation, or fee assumption.

Common Market-Factor Model

The engine employs a Markov Regime-Switching Model with a Common Systematic Market Factor.

Within each simulated year, the model generates a common systematic shock represented by a standard normal random variable. That shock affects modeled investment-account buckets such as taxable, pre-tax, and Roth accounts.

Each account bucket can have its own expected return and volatility while sharing exposure to the common economic shock.

This is a simplifying assumption. In the current model, account buckets are effectively perfectly correlated with respect to the common market shock. The model therefore does not attempt to reproduce the full diversification benefit that can arise from imperfect correlations among stocks, bonds, international assets, and other investments.

Inflation Modeling

The base inflation rate is determined by the current macroeconomic regime. A stochastic component is then added:

[ \text{Inflation} = \text{Base Inflation} + \epsilon, \qquad \epsilon \sim \mathcal{N}(0, 0.005) ]

In plain English, the model adds a normally distributed annual inflation shock with a standard deviation of 0.5 percentage points around the regime-specific inflation rate.

Because real-world inflation can behave differently from a normal distribution, QBearLab treats this as a modeling approximation rather than a forecast.

Number of Simulations

For published case studies that report a Monte Carlo success rate, the QBearLab publication standard is 10,000 simulated retirement paths per scenario.

If a case uses a different number of simulation paths, the case should disclose the run count. Monte Carlo results should not be presented with more numerical precision than the simulation design reasonably supports.

Definition of Success

The baseline QBearLab success criterion is:

A simulated path succeeds when all modeled spending obligations can be met through the specified target projection age without exhausting the financial resources designated as available for retirement.

Unless a case specifies an additional requirement, no minimum ending portfolio balance is required beyond remaining solvent through the target projection age.

A case may define a stricter success criterion, including:

Maintaining a specified legacy amount
Preserving a primary residence
Maintaining a minimum liquid reserve
Avoiding a planned property sale
Meeting another explicitly defined financial objective

Treatment of Real Estate in the Success Criterion

Real estate is not automatically assumed to be available for retirement spending.

A property is treated as a potential source of retirement capital only when the case explicitly allows its sale.

If a primary residence is sold, the analysis should account for the modeled household’s continuing housing need—such as replacement-home cost, rent, or another post-sale housing assumption—before treating remaining net sale proceeds as available retirement capital.

This prevents a primary-home sale from being treated as if the household no longer needs housing.

Portfolio and Glide-Path Assumptions

When configured, the model dynamically adjusts expected return and volatility as the primary planner ages, transitioning from a higher-return/higher-volatility profile toward a lower-return/lower-volatility profile.

The exact glide path is case-specific. When a glide path materially affects results, the relevant case should disclose:

Starting allocation or return profile
Ending allocation or return profile
Ages over which the transition occurs
Any override to the standard QBearLab return assumptions

Income and Spending Modeling

Household spending can be divided into essential and lifestyle targets and projected over time.

Unless a case specifies otherwise:

Spending assumptions should state whether they are expressed in current or future dollars.
Recurring spending is generally adjusted for modeled inflation.
One-time expenses are modeled separately when material.
Case studies should disclose major changes in spending over time, such as mortgage payoff, college costs, travel changes, or long-term-care expenses.

Social Security benefits can be modeled at scheduled benefit levels or under an optional reduced-benefit stress scenario.

When a Social Security solvency stress scenario is used, the case study should disclose:

The assumed reduction
The year the reduction begins
Whether later cost-of-living adjustments continue
The source used for the assumption

A reduced-benefit scenario is a stress test, not a prediction that a specific future benefit reduction will occur.

Healthcare Modeling

Healthcare can materially affect an early-retirement plan, particularly before Medicare eligibility. QBearLab therefore treats healthcare assumptions separately from ordinary lifestyle spending when they are material to the case.

Pre-Medicare Healthcare

A case may model:

Health-insurance premiums
Deductibles and expected out-of-pocket costs
A healthcare-specific inflation rate
Employer-subsidized coverage for part of retirement
Marketplace/ACA coverage where applicable

Healthcare assumptions are case-specific and should be disclosed in the case-study assumptions.

Marketplace / ACA Assumptions

When Marketplace coverage or Premium Tax Credit effects are explicitly modeled, QBearLab may consider modeled household income and the applicable tax-year rules.

Because Marketplace subsidies depend on household circumstances, income, law, and available plans, QBearLab treats ACA-related results as scenario estimates rather than guaranteed subsidy amounts.

Medicare and IRMAA

Beginning at the modeled Medicare eligibility age, a case may incorporate:

Base Medicare premiums
Medicare Part B and/or Part D income-related adjustments
The applicable two-year income lookback used for IRMAA
Case-specific out-of-pocket healthcare assumptions

Medicare costs and thresholds can change annually. Published cases should identify the tax/premium year used when those figures materially affect results.

Long-Term Care

Long-term-care expenses are included only when a case explicitly models them.

When included, the case should disclose:

Starting cost assumption
Start age or triggering scenario
Duration
Number of household members affected
Inflation treatment
Whether the figures are nominal or inflation-adjusted

Long-term-care assumptions are scenario inputs, not predictions of whether a household member will require care.

Tax Modeling

QBearLab includes a tax-estimation engine designed to model major federal and supported state tax rules that materially affect retirement cash flows.

The tax model is intended for scenario analysis. It is not tax-return preparation software and does not replace professional tax advice.

Current federal tax-rule baseline: 2026, unless a case states otherwise.

Progressive Federal and State Income Taxes

Ordinary income is modeled using applicable standard deductions and progressive tax brackets based on filing status.

State-tax treatment is case-specific. A published case should disclose the state-tax assumption used.

If the model does not explicitly support the relevant jurisdiction’s full tax treatment, the case should identify the state calculation as simplified or state that state income tax is excluded.

Local taxes are included only when explicitly modeled.

Capital Gains and Net Investment Income Tax

Long-term capital gains are stacked on top of ordinary income to determine applicable capital-gain tax brackets.

The model can also estimate Net Investment Income Tax (NIIT) when modeled modified adjusted gross income exceeds applicable thresholds.

The model estimates the taxable portion of Social Security using the IRS combined-income framework, including adjusted gross income, applicable tax-exempt interest, and 50% of Social Security benefits.

Depending on filing status and combined income, up to 85% of Social Security benefits may be included in taxable income under current federal rules.

Roth Conversions

QBearLab can model rule-based Roth conversions designed to use available capacity within a specified tax bracket or marginal-rate threshold.

This is a planning heuristic unless a case explicitly states that a separate optimization procedure was used. It should not be interpreted as proof that the modeled conversion amount is globally optimal for every possible future tax and market outcome.

Medicare IRMAA

The model can estimate Medicare Income-Related Monthly Adjustment Amount surcharges using the applicable two-year MAGI lookback.

Because Medicare premiums and thresholds change, case studies should disclose the year of the IRMAA parameters when material.

Required Minimum Distributions and Early-Distribution Taxes

RMDs are modeled using applicable IRS life-expectancy rules and current-law starting-age requirements.

The model can also incorporate the additional tax on certain early retirement-plan distributions and applicable exceptions.

For example, a Rule-of-55 exception is modeled only when the case assumptions satisfy the relevant conditions for a qualifying employer-sponsored plan. The Rule of 55 does not apply generally to IRA distributions.

Real-Estate Modeling

Real estate is modeled separately from liquid investment accounts because property values, mortgages, rental income, transaction costs, taxes, and housing needs can materially affect retirement results.

Property Valuation

Each published case should disclose the nominal property-appreciation assumption used for any real-estate projection that materially affects the result.

QBearLab does not treat a projected property value as a guaranteed future sale price.

If stochastic real-estate variation is enabled for a case, the case should disclose the relevant property-return or shock assumptions.

Mortgages

Mortgage balances are currently approximated using a simplified linear payoff trajectory rather than full payment-level amortization.

This is a known model simplification.

When mortgage timing materially affects the result, the case should disclose:

Current mortgage balance
Payoff year
Mortgage payment or debt-service assumption where relevant
Whether the model uses the simplified payoff approximation

Rental Income

Properties designated as rentals may generate modeled rental cash flow.

Rental modeling separates rent growth from general CPI inflation. Where material, a case should disclose:

Current gross or net rental income
Rent-growth assumption
Vacancy assumption, if modeled
Maintenance or operating expense assumptions, if modeled
Mortgage payoff effects
Property appreciation assumption

Property Sales and Liquidation

When a property is sold:

Modeled transaction costs and applicable taxes are applied.
Debt associated with the property is accounted for where relevant.
Required replacement-housing costs are modeled when the asset is a primary residence.
Remaining net proceeds are added to the household’s taxable liquid assets unless the case explicitly specifies another lawful transaction.

Adjusted Basis and Taxes

For property sales, the model estimates taxable gain using modeled sale proceeds, adjusted cost basis, selling costs, and applicable tax treatment.

For a qualifying primary residence, a Section 121 exclusion may be applied when the modeled household satisfies the relevant eligibility assumptions. The exclusion is not applied solely because a property is labeled a primary residence.

Rental or business use can require additional basis and depreciation-related tax treatment. When those items materially affect a published result, the case should disclose the assumptions used.

Default Withdrawal Strategy

Unless a case specifies another strategy, the model uses the following configurable default withdrawal order:

Taxable accounts → Pre-tax accounts → Roth accounts → Real Estate

This ordering is a modeling convention/heuristic, not a recommendation.

It may be overridden when evaluating:

Roth conversions
Tax-bracket management
Marketplace/ACA income considerations
RMDs
Rule-of-55 access
Capital-gain realization
Other case-specific planning strategies

Different withdrawal orders can produce different tax and portfolio outcomes.

Nominal and Inflation-Adjusted Dollars

Unless otherwise labeled, QBearLab cash-flow projections are expressed in nominal future dollars.

Nominal future dollars include the effect of modeled inflation. Where useful, QBearLab also reports inflation-adjusted values to help readers understand future purchasing power.

Case-study charts and tables should identify whether displayed figures are:

Nominal future dollars
Inflation-adjusted dollars
Current-dollar inputs

Case-Specific Assumptions

The methodology described on this page defines how QBearLab performs calculations globally.

Individual case studies provide their own assumptions for items such as:

Income
Spending
Retirement age
Projection age
Portfolio return and volatility
Inflation
Asset allocation or return profile
Social Security claiming
Healthcare
State taxes
Real estate
Property appreciation
Rental income
Legacy goals
Major one-time expenses

When a case overrides a default QBearLab assumption, the case-specific assumption governs.

To make results auditable by readers, published quantitative case studies should include a visible assumptions section and identify the QBearLab model/methodology version used.

A Simplified Worked Example

The following example illustrates the mechanics of the framework. It is not a recommendation and is not intended to represent any particular QBearLab household.

Assume a hypothetical retiree has:

Input	Illustrative assumption
Retirement age	55
Starting investment portfolio	$1,500,000
Starting annual spending	$80,000
Social Security start age	67
Annual Social Security benefit at start	$30,000
Projection age	95
Base inflation	2.5%
Portfolio assumption	Default Generic Portfolio

Base-Case Interpretation

The deterministic model applies the stated assumptions year by year. It adjusts recurring spending for inflation, adds Social Security when it begins, estimates applicable taxes, and funds remaining cash-flow needs from the modeled portfolio according to the case’s withdrawal strategy.

The output answers:

If these assumptions occur as modeled, what financial trajectory results?

Monte Carlo Interpretation

A stochastic path may move through a sequence such as:

Baseline
   ↓
Baseline
   ↓
Mild Recession
   ↓
Severe Recession
   ↓
Mild Recession
   ↓
Baseline
   ↓
...

A different simulated path may experience adverse conditions much earlier or much later.

This allows QBearLab to test sequence-of-returns risk: two households with the same long-run average return can have very different outcomes when large losses occur at different times.

The Monte Carlo success rate reports the percentage of modeled paths that meet the case’s stated success criterion. It should be interpreted as a stress-test result under QBearLab’s assumptions, not as a guaranteed probability of retirement success.

Methodology Limitations and Sources of Uncertainty

Every retirement projection is sensitive to assumptions. QBearLab therefore publishes the following important limitations.

Investment returns are uncertain. Historical relationships may not persist.
Regime parameters are modeling assumptions. They are not recession forecasts.
Normal distributions can understate extreme tail behavior.
Correlation assumptions simplify real-world diversification. The common-factor model does not reproduce a full asset-class correlation matrix.
Monte Carlo results are model-dependent. A reported success rate is not a real-world guarantee.
Simulation count limits precision. Published percentages should not imply greater precision than the simulation design supports.
Tax laws change. Future tax brackets, deductions, credits, and tax treatment cannot be predicted.
State and local taxes vary. Some jurisdiction-specific tax effects may be simplified or excluded.
Social Security rules and benefits can change.
Medicare and healthcare costs are uncertain.
ACA/Marketplace subsidy results depend on future law, household income, geography, and plan availability.
Investment fees may be simplified or excluded unless a case explicitly models them.
Household spending may change rather than remaining constant in real terms.
Longevity is uncertain.
Real estate is illiquid. Sale timing and valuation are uncertain.
Property-appreciation assumptions are not forecasts.
Property transaction costs and taxes are estimates.
Mortgage payoff is currently simplified rather than modeled with full payment-level amortization.
Primary-residence equity is not automatically spendable. Replacement housing must be considered when a sale is part of a retirement strategy.
Rental income is uncertain. Vacancy, repairs, insurance, taxes, and other operating costs may differ from assumptions.
Long-term-care costs and timing are highly uncertain.
Results are scenario analysis, not individualized financial advice or predictions.

Editorial and Source Standards

QBearLab distinguishes among three types of information:

1. Household or Case Inputs

These are assumptions specific to the hypothetical case, such as portfolio value, spending, retirement age, mortgage balance, or planned property sale.

2. QBearLab Modeling Assumptions

These include parameters such as expected return, volatility, regime transition probabilities, inflation assumptions, or a case-specific stress scenario.

QBearLab identifies these as assumptions, not established facts or forecasts.

3. External Financial and Regulatory Rules

For tax, Social Security, Medicare, healthcare, and other regulatory claims, QBearLab prioritizes primary sources such as the IRS, Social Security Administration, Medicare/CMS, HealthCare.gov, and U.S. Bureau of Labor Statistics.

When a rule or threshold changes over time, published case studies should identify the rule year when it materially affects the analysis.

QBearLab aims to correct material factual or modeling errors promptly. Readers can report a potential error through the QBearLab Contact page.

Sources and References

The sources below support regulatory rules and the broader modeling framework. QBearLab’s regime parameters, portfolio assumptions, and case-specific inputs remain QBearLab modeling assumptions unless otherwise stated.

Tax and Retirement Rules

Social Security Administration — Actuarial Publications and Trustees Reports

When a QBearLab case uses a Social Security solvency stress assumption, the case should identify the specific Trustees Report or other SSA source used.

Medicare and Healthcare

Inflation

U.S. Bureau of Labor Statistics — Consumer Price Index

Economic Methodology

Hamilton, James D. (1989). A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle. Econometrica, 57(2), 357–384. DOI: 10.2307/1912559

How QBearLab Articles Should Use This Methodology

To make quantitative analysis transparent and useful to readers, a QBearLab case study should, where applicable:

Identify the analysis as a hypothetical or illustrative case.
Display the case-specific financial inputs.
Display the assumptions that materially affect the result.
Identify whether dollar values are nominal or inflation-adjusted.
Identify the QBearLab model/methodology version used.
Link to this methodology page.
Identify primary regulatory sources for material tax, Social Security, Medicare, or healthcare claims.
Define any case-specific Monte Carlo success criterion or legacy requirement.
Disclose any material model override or simplification.
Distinguish model results from financial recommendations.

This consistency allows readers to understand not only what QBearLab concluded, but how the result was produced.

Methodology Version History

Version	Date	Change
1.0	Jul. 2026	Initial public model based deterministic analysis
1.1	Aug. 2026	Added public reporting standards for Monte Carlo analysis, strengthened success criteria, added replacement-housing treatment, healthcare modeling, real-estate assumption disclosures, state-tax qualifications, official source links, worked example, editorial/source standards, and expanded limitations

Educational Use

QBearLab is an independent educational analytics publication. Its models are intended to help readers understand financial trade-offs and sensitivity to assumptions.

QBearLab does not provide individualized investment recommendations, tax advice, legal advice, insurance advice, or a guarantee of future financial outcomes. Readers should evaluate important personal financial decisions using their own circumstances and, when appropriate, qualified professional advice.