How to build a macroeconomics course: a complete guide for lecturers

Central question: What do GDP, inflation, unemployment and related indicators actually measure, and what can they miss?

Start by making measurement a decision problem rather than a definitions lecture. Students need a shared language for real GDP, nominal GDP, price indexes, unemployment, participation and cyclical position before they can evaluate policy. The lecturer should also make data limitations explicit: revisions, base effects, informal activity, quality change and differences between headline and underlying measures can materially alter a macroeconomic story.

What this concept covers

• Nominal versus real GDP, GDP growth and GDP per capita.
• CPI, GDP deflator, headline versus core inflation and base effects.
• Unemployment, labour-force participation, employment rates and underemployment.
• Output gaps, potential output and the distinction between level and growth-rate effects.
• Data revisions, measurement error and the limits of aggregate indicators.

Learning outcomes

• Calculate and interpret common macroeconomic indicators.
• Distinguish levels, growth rates and percentage-point changes.
• Critique a macro claim by checking the measure, denominator, period and benchmark.
• Explain how data revisions or base effects can change a narrative.

Supports the course learning outcomes through progressively more applied evidence.

How to teach it

• Give students a one-page data dashboard and ask them to write the headline before revealing the country.
• Use a short exercise where nominal GDP rises while real GDP falls, forcing students to separate prices from quantities.
• Ask teams to reconcile a falling unemployment rate with weak employment growth by examining participation.

Case-style example

Northstar Republic reports nominal GDP of $520 billion, up from $500 billion, while its GDP deflator rises from 100 to 108. Unemployment falls from 6.2% to 5.7%, but labour-force participation falls from 66% to 64%. Students compute approximate real growth, then write two 80-word briefing notes: one arguing the economy is improving and one arguing it is weakening. They discover that the same headline indicators can support different narratives until measurement choices are made explicit.

Where students struggle

Students often treat a published number as the phenomenon itself. They also confuse a fall in inflation with a fall in the price level, and a lower unemployment rate with an unambiguously stronger labour market.

Reading and a quick check

Use Blanchard, Amighini and Giavazzi on the macroeconomic data framework. Quick check: give four statements containing “inflation fell”, “prices fell”, “growth fell” and “GDP fell” and ask students to identify which are equivalent and which are not.

Where a simulation fits

A short dashboard or data-interpretation workshop is better here than a simulation. The purpose is to establish measurement discipline before students face strategic or financial decisions.

Where it leads

Once students can read the macro dashboard, they can ask what determines living standards and the economy's productive capacity over the long run.

Central question: Why do some economies become persistently richer, and what can policy realistically do about productivity?

Growth should connect the production side of macroeconomics to business-school questions about investment, technology and institutions. A good treatment separates capital deepening from productivity growth, and makes students distinguish one-off level effects from changes in the long-run growth rate. This is also where demographic change, human capital and new technologies can be brought into the course without turning the session into a technology survey.

What this concept covers

• Growth accounting, labour productivity and total factor productivity.
• Capital accumulation, saving, investment and diminishing returns.
• Technology, human capital, institutions and diffusion of innovation.
• Demographics, participation and the distinction between GDP and GDP per capita.
• Convergence, productivity slowdowns and policy trade-offs around long-run supply.

Learning outcomes

• Decompose growth into labour, capital and productivity contributions.
• Explain why capital accumulation alone cannot sustain permanently higher growth rates.
• Evaluate a supply-side reform by identifying the mechanism through which it affects potential output.
• Distinguish policies that raise the level of output from those that may raise its growth rate.

Supports the course learning outcomes through progressively more applied evidence.

How to teach it

• Use a growth-accounting worksheet for two fictional economies with the same GDP growth but different productivity contributions.
• Ask students to rank reforms by likely time horizon: infrastructure, education, competition policy, migration and investment incentives.
• Connect AI to productivity using a measured, evidence-led discussion rather than asking whether “AI will raise growth”.

Case-style example

Meridian and Aster both grow real GDP by 3.0%. Meridian's hours worked rise 2.0%, capital services 2.5% and measured productivity 0.5%. Aster's hours rise 0.3%, capital services 1.2% and productivity 2.1%. Students compute a simple contribution table, then advise which economy offers the stronger five-year outlook to an international investor. One group can favour Meridian's near-term demand; another can favour Aster's productivity path. They discover why equal headline growth can conceal very different supply stories.

Where students struggle

Students often equate investment with productivity, or assume any policy that raises current GDP also raises long-run growth. They also understate the importance of implementation lags and institutional capacity.

Reading and a quick check

Brynjolfsson, Rock and Syverson (2021), “The Productivity J-Curve,” is useful once the basic growth framework is established. Quick check: ask whether a temporary investment boom changes the level or long-run growth rate of output and why.

Where a simulation fits

No simulation is necessary. A country-comparison case or growth-accounting workshop preserves the macro focus better than forcing a product simulation into the session.

Where it leads

Long-run productive capacity sets the background, but actual economies can operate above or below that capacity. Labour-market dynamics are the next bridge between potential and realised output.

Central question: How should students interpret unemployment, vacancies, participation and wages when the labour market is changing?

Labour-market teaching is strongest when it moves beyond “unemployment rises in recessions”. Students should see flows into and out of employment, matching frictions, participation, wage bargaining and the Beveridge curve. The managerial connection is immediate: wage pressure, hiring difficulty and labour supply feed directly into costs, capacity and inflation, while policy makers must distinguish cyclical slack from structural change.

What this concept covers

• Unemployment stocks and labour-market flows.
• Natural unemployment, matching frictions and the Beveridge curve.
• Participation, demographics, migration and labour supply.
• Nominal versus real wages, productivity and wage bargaining.
• Labour-market tightness as an input into inflation and monetary-policy judgement.

Learning outcomes

• Interpret unemployment and participation jointly.
• Use vacancy and unemployment data to assess labour-market tightness.
• Explain how productivity and labour supply affect sustainable real-wage growth.
• Distinguish cyclical unemployment from structural mismatch.

Supports the course learning outcomes through progressively more applied evidence.

How to teach it

• Plot vacancies and unemployment for a fictional economy and ask where the Beveridge curve may have shifted.
• Give teams the same labour data but different mandates: a central bank, a retailer and a government finance ministry.
• Ask what additional data each team would request before calling the labour market “tight”.

Case-style example

Arcadia has unemployment of 4.2%, vacancies equal to 6.5% of employment and nominal wage growth of 5.6%. Inflation is 3.8%, productivity growth 1.2% and participation remains 1.5 percentage points below its pre-shock level. Students calculate approximate real wage growth and assess whether wage pressure is consistent with inflation returning to 2%. A central-bank team may favour cooling demand, while a labour-ministry team may prioritise participation. Students discover that the same tightness indicators imply different policy responses depending on the diagnosis.

Where students struggle

Students tend to treat the unemployment rate as sufficient. They also jump from strong wage growth to “wage-price spiral” without checking productivity, participation, vacancies, expectations and sectoral composition.

Reading and a quick check

Use a recent labour-market note alongside Blanchard's labour-market chapters. Quick check: ask what would move along a Beveridge curve and what might shift the curve itself.

Where a simulation fits

A structured role briefing works well here. The listed simulations do not directly model labour-market flows, so application should remain a macroeconomic data exercise.

Where it leads

Labour tightness becomes especially important once the course turns to inflation and the Phillips-curve relationship.

Central question: What drives inflation, how persistent is it, and when should policy makers worry about expectations becoming unanchored?

Inflation is where students most need causal discipline. The session should separate relative-price shocks, demand pressure, wage dynamics and expectations rather than presenting one universal story. A modern Phillips-curve treatment can be accessible without heavy mathematics: define the relationship, show why it can shift, and make students ask whether inflation is broad, persistent and demand-sensitive.

What this concept covers

• Headline, core and sectoral inflation; goods versus services inflation.
• Expected inflation, wage setting and price setting.
• Phillips-curve intuition, slack, nonlinearity and shifting relationships.
• Supply shocks, pass-through and second-round effects.
• Disinflation, credibility and the difference between lower inflation and deflation.

Learning outcomes

• Diagnose inflation using multiple candidate mechanisms.
• Explain the role of expectations and labour-market tightness.
• Evaluate when a supply shock should and should not trigger a strong demand response.
• Interpret disinflation without assuming the price level reverses.

Supports the course learning outcomes through progressively more applied evidence.

How to teach it

• Give students an inflation decomposition with energy, goods, housing and services contributions.
• Split teams into “transitory shock” and “persistent inflation” positions and require each to identify falsifying evidence.
• Use a short policy memo that forces students to state what they still do not know.

Case-style example

Lydon's inflation rises from 2.1% to 7.4%. Energy contributes 2.3 percentage points, imported goods 1.4, housing 1.2 and domestic services 2.5. One-year expectations rise to 5.0%, five-year expectations remain 2.4%, and unemployment is 3.9%. Students allocate the inflation increase across supply-sensitive and demand-sensitive components, then recommend whether a 100 basis point policy-rate increase is proportionate. Hawks can stress persistence; doves can stress temporary shocks. Students discover why the policy question is about persistence and propagation, not the headline rate alone.

Where students struggle

Students frequently assume every inflation episode is either “too much money” or “supply shock”. They also confuse expectations with forecasts and do not distinguish short-run inflation compensation from long-run credibility.

Reading and a quick check

Bernanke and Blanchard (2025), “What Caused the U.S. Pandemic-Era Inflation?” works well after the basic framework. Quick check: ask students what evidence would make them revise a supply-led diagnosis toward a demand-led one.

Where a simulation fits

No direct Finsimco simulation is needed. Preserve the macroeconomic diagnosis as a policy memo so later simulations can use the resulting assumptions.

Where it leads

Inflation and labour-market evidence now feed into the aggregate demand and aggregate supply framework for short-run fluctuations.

Central question: How do shocks move output and inflation together, and why do different shocks require different policy responses?

This concept integrates the first four sessions. The value of AD-AS is not the diagram itself but the discipline of asking which curve moved, why, and what observable evidence should follow. Students should compare demand shocks, supply shocks and mixed shocks, then connect the diagnosis to output gaps, inflation and policy trade-offs.

What this concept covers

• Short-run aggregate demand and supply intuition.
• Demand shocks, supply shocks and mixed disturbances.
• Output gaps, recession, overheating and recovery dynamics.
• Automatic stabilisers and policy transmission.
• Business-cycle indicators and scenario-based forecasting.

Learning outcomes

• Classify common macro shocks by their expected output and inflation effects.
• Use AD-AS to construct a coherent narrative from data.
• Explain why a policy appropriate for a demand shock may be costly after a supply shock.
• Build base, upside and downside macro scenarios.

Supports the course learning outcomes through progressively more applied evidence.

How to teach it

• Use four shocks: tax cut, oil disruption, banking stress and productivity improvement. Ask teams to predict output, inflation and interest-rate implications.
• Require each team to state one indicator that would falsify its diagnosis.
• End with a scenario table that is reused later in portfolio and financing sessions.

Case-style example

Helios suffers a 15% energy-price jump while household confidence falls 12 points. Industrial output contracts 1.8%, services demand remains strong and headline inflation rises from 3.0% to 5.6%. Students draw two competing AD-AS narratives: a dominant supply shock versus a mixed supply-and-demand shock. They then forecast output and inflation six months ahead under each view. They discover that policy cannot be evaluated until the underlying shock is identified, and that mixed shocks can make simple diagrams genuinely ambiguous.

Where students struggle

Students often force every event into one curve movement and then read the diagram mechanically. They need to treat models as disciplined simplifications that generate testable implications.

Reading and a quick check

Baqaee and Farhi (2022), “Supply and Demand in Disaggregated Keynesian Economies,” can support an advanced cohort. Quick check: ask why the same fall in output can be inflationary in one shock and disinflationary in another.

Where a simulation fits

A case workshop is preferable at this stage. The macro scenarios built here become inputs for later Portfolio Management and Debt Financing applications.

Where it leads

With the cycle framework in place, students can examine how money, banks and financial conditions transmit policy into spending and investment.

Central question: How do interest rates, bank balance sheets, credit spreads and liquidity transmit macroeconomic shocks?

Avoid turning this session into a taxonomy of monetary aggregates. The central teaching objective is transmission: how central-bank actions and financial-market conditions affect borrowing costs, asset prices, exchange rates, bank lending and ultimately aggregate demand. Business-school students benefit from seeing the bridge between policy rates and the rates faced by households and firms.

What this concept covers

• Functions of money and the modern monetary system.
• Bank balance sheets, deposits, lending and liquidity.
• Policy rates, yield curves, credit spreads and broader financial conditions.
• Bank-lending, interest-rate, asset-price and expectations channels.
• Financial stress, risk premia and feedback between finance and the real economy.

Learning outcomes

• Trace a policy-rate change through multiple transmission channels.
• Explain why bank lending rates may move by more or less than the policy rate.
• Interpret a yield curve and credit-spread move as macro information.
• Identify how financial stress can amplify an otherwise ordinary slowdown.

Supports the course learning outcomes through progressively more applied evidence.

How to teach it

• Give students a “100 bp rate rise” and ask them to trace six channels into consumption, housing, investment and exchange rates.
• Compare a policy-rate cut with a simultaneous 150 bp widening in corporate spreads.
• Ask whether monetary policy is easier or tighter when the policy rate is unchanged but financial conditions move.

Case-style example

Orion Central Bank cuts its policy rate by 75 basis points, but banks widen lending spreads by 120 basis points after loan losses rise. Ten-year government yields fall 40 basis points while BBB corporate spreads widen 180. Students calculate indicative changes in a mortgage rate and corporate borrowing cost, then decide whether financial conditions have eased. One team can focus on sovereign yields; another on private credit. Students discover why the policy rate alone is not the stance of financial conditions.

Where students struggle

Students equate “money supply rises” with “credit is easy” and assume the central bank directly sets every market rate. They also underappreciate risk premia and bank-capital constraints.

Reading and a quick check

Use the financial-markets chapters in Blanchard and a current central-bank financial conditions note. Quick check: ask why a policy cut can coexist with tighter borrowing conditions.

Where a simulation fits

The Debt Financing Simulation can be used later, after students also understand monetary policy and fiscal conditions, to show how macro rates and credit risk feed into a complete financing package. Do not run it yet if students have not covered capital structure basics.

Where it leads

The transmission mechanism gives students the tools to evaluate how central banks set and communicate monetary policy.

Central question: How should a central bank set policy when inflation, output and financial stability point in different directions?

Monetary policy should be taught as a constrained judgement exercise. Students can use Taylor-rule intuition, neutral-rate concepts and transmission lags without pretending that policy is mechanical. The key is to make them defend a rate path using evidence on inflation persistence, slack, expectations, financial conditions and risks.

What this concept covers

• Central-bank mandates and inflation-targeting frameworks.
• Policy reaction functions, Taylor-rule intuition and the neutral real rate.
• Transmission lags, expectations and forward guidance.
• Quantitative easing, balance-sheet policy and effective lower-bound issues.
• Trade-offs between inflation control, activity and financial stability.

Learning outcomes

• Recommend a policy-rate stance using a transparent reaction framework.
• Explain the difference between nominal, real and neutral interest rates.
• Evaluate the role of credibility and communication.
• Defend a rate path under uncertainty rather than a single point decision.

Supports the course learning outcomes through progressively more applied evidence.

How to teach it

• Run a mock monetary-policy committee with each group receiving the same data but a different risk brief.
• Have students calculate a simple real policy rate and compare it with an assumed r-star range.
• Ask students to write the first paragraph of a policy statement and then challenge it with a surprise data release.

Case-style example

Caldera targets 2% inflation. Current inflation is 3.6%, one-year-ahead inflation is forecast at 2.7%, unemployment is 5.8% against an estimated natural rate of 5.2%, and the policy rate is 4.25%. R-star is estimated between 0.5% and 1.5%. Students compute a range for the ex-ante real rate and recommend hold, +25 bp or -25 bp. Hawks can stress sticky services inflation; doves can stress slack and lags. Students discover why uncertainty around r-star and persistence can dominate a neat rule.

Where students struggle

Students overfit Taylor rules, treat r-star as observed and assume “higher rates reduce inflation” without discussing lags, channels and the source of inflation.

Reading and a quick check

Brandao-Marques, Meeks and Nguyen (2024), “Monetary Policy with Uncertain Inflation Persistence,” is a strong extension. Quick check: ask why uncertainty can justify a different policy response even when the central forecast is unchanged.

Where a simulation fits

The Portfolio Management Simulation fits after this concept if the lecturer wants students to translate interest-rate and macro-news changes into portfolio decisions. The macro learning is strongest when students must explain which policy channel changed their expected returns and risk rather than simply trade on headlines.

Where it leads

Monetary policy cannot be evaluated in isolation. The next concept introduces fiscal stabilisation, debt dynamics and the interaction between the two policy arms.

Central question: When should governments use fiscal policy, how large are the effects, and when does debt constrain the available choices?

Fiscal policy brings stabilisation and intertemporal trade-offs together. Students should distinguish automatic stabilisers from discretionary action, temporary stimulus from permanent commitments, and short-run multipliers from long-run debt dynamics. A business-school course should also connect sovereign borrowing costs and fiscal credibility to private financing conditions.

What this concept covers

• Government spending, taxation and automatic stabilisers.
• Fiscal multipliers and the importance of slack, openness and monetary accommodation.
• Primary balances, interest-growth differentials and debt dynamics.
• Fiscal space, credibility and debt sustainability.
• Fiscal-monetary interaction and crowding-out or crowding-in channels.

Learning outcomes

• Estimate the direction and conditions affecting a fiscal multiplier.
• Explain debt dynamics using the primary balance and the interest-growth differential.
• Evaluate whether a fiscal package is temporary, targeted and credible.
• Identify when fiscal and monetary policy reinforce or offset one another.

Supports the course learning outcomes through progressively more applied evidence.

How to teach it

• Use a debt-dynamics calculator with alternative r-g and primary-balance assumptions.
• Compare an untargeted tax rebate with public investment of the same headline size.
• Ask teams to recommend a package for recession while preserving a credible medium-term debt path.

Case-style example

Rivermark has public debt of 92% of GDP, a primary deficit of 2.0% of GDP, nominal interest costs averaging 4.5% and nominal GDP growth expected at 3.0%. A proposed 2% of GDP infrastructure package is estimated to have a first-year multiplier between 0.7 and 1.3. Students compute the approximate debt-pressure direction, then recommend proceed, resize or delay. Keynesian teams can emphasise slack and investment; fiscal-risk teams can emphasise r-g and refinancing. Students discover that “stimulus works” and “debt is high” are not complete arguments.

Where students struggle

Students often use debt-to-GDP thresholds mechanically, ignore maturity and currency composition, or assume all fiscal multipliers are stable constants.

Reading and a quick check

Bolhuis, Koosakul and Shenai (2024), “Fiscal R-Star: Fiscal-Monetary Tensions and Implications for Policy,” provides an advanced policy extension. Quick check: ask which combination of r, g and primary balance would stabilise debt and what could make the calculation misleading.

Where a simulation fits

The Debt Financing Simulation can serve as a later applied bridge to private credit conditions. Students can ask how sovereign rates, inflation expectations and credit spreads alter the borrower-lender negotiating range, while keeping the assessment focused on the financing package rather than pretending the simulation models fiscal policy.

Where it leads

Fiscal and monetary choices increasingly spill across borders, so the course now opens the economy to exchange rates, trade and capital flows.

Central question: How do exchange rates, trade balances, capital flows and policy choices interact in an open economy?

Open-economy macro should give students a usable framework for interpreting currencies rather than a list of parity conditions. The course can connect goods-market openness, financial flows, interest differentials and exchange-rate regimes, then ask how depreciation affects inflation, competitiveness, debt service and business planning.

What this concept covers

• Current account, capital flows and balance-of-payments accounting.
• Nominal and real exchange rates and purchasing-power intuition.
• Interest differentials, expectations and exchange-rate movements.
• Fixed, managed and floating exchange-rate regimes.
• Currency pass-through, imported inflation and foreign-currency debt.

Learning outcomes

• Interpret basic balance-of-payments relationships.
• Explain multiple channels through which depreciation affects output and inflation.
• Compare policy autonomy under different exchange-rate regimes.
• Assess how global dollar conditions can transmit across countries.

Supports the course learning outcomes through progressively more applied evidence.

How to teach it

• Give teams a currency depreciation and ask for separate effects on exporters, importers, households, the central bank and foreign-currency borrowers.
• Use a simple current-account and capital-account reconciliation exercise.
• Compare a floating regime with a defended peg under capital outflow.

Case-style example

Estovia runs a current-account deficit of 5% of GDP and has foreign-currency corporate debt equal to 35% of GDP. Its currency depreciates 18%, export volumes rise 6% and import prices rise 14%. The central bank can raise rates 150 bp or allow further depreciation. Students estimate which sectors gain and lose, then recommend a policy mix. One team can defend competitiveness; another can stress inflation and balance-sheet effects. Students discover why depreciation is neither automatically expansionary nor automatically harmful.

Where students struggle

Students often think current-account deficits are always bad, confuse nominal depreciation with real competitiveness, and ignore balance-sheet effects of foreign-currency debt.

Reading and a quick check

Gopinath et al. (2020), “Dominant Currency Paradigm,” is a strong advanced reading. Quick check: ask why a depreciation may not raise export volumes as much as a simple textbook model predicts when trade is priced in a dominant currency.

Where a simulation fits

The PESTLE Analysis Simulation becomes increasingly relevant from here because students now have the economic vocabulary to distinguish inflation, rates, growth, trade, currencies and policy risk from the other PESTLE dimensions. Use it after the macro content, not as a substitute for teaching open-economy theory.

Where it leads

Students can now evaluate how external shocks propagate through domestic inflation, output, policy and business decisions.

Central question: How should students analyse shocks that cut across inflation, growth, trade, policy and business conditions at the same time?

This concept makes the course contemporary without turning it into a news seminar. The analytical discipline is to map an external event into macro channels: supply, demand, prices, financial conditions, confidence, trade and policy. PESTLE is useful here because it forces students to distinguish economic mechanisms from political, technological, environmental and legal drivers.

What this concept covers

• Commodity and energy shocks.
• Supply-chain disruptions and sectoral bottlenecks.
• Geopolitical, trade-policy and sanctions shocks.
• Climate-transition and physical-risk channels at a macro level.
• Scenario design, second-round effects and policy trade-offs.

Learning outcomes

• Translate an external event into a causal macro chain.
• Separate first-round price effects from persistence mechanisms.
• Identify which macro indicators should be monitored after a shock.
• Construct a scenario with explicit assumptions and signposts.

Supports the course learning outcomes through progressively more applied evidence.

How to teach it

• Give teams a common shock and require a one-page transmission map: event -> channels -> indicators -> policy -> business impact.
• Force students to distinguish “economic” PESTLE factors from political or environmental causes.
• Require probability, impact and time-horizon ratings rather than a list of headlines.

Case-style example

Astra Manufacturing sources 40% of key inputs from one region. A geopolitical disruption raises input costs 22%, shipping times double, the domestic currency weakens 8% and the central bank is already fighting 4.5% inflation. Students build base, upside and downside scenarios for GDP growth, inflation and rates, then recommend whether Astra should enter a new market. Growth teams can stress competitor disruption; risk teams can stress margin and demand pressure. Students discover that macro shocks matter through interacting channels, not through one headline variable.

Where students struggle

Students create long lists of risks without prioritising materiality, timing or causal links. They also duplicate the same fact across PESTLE, SWOT and Five Forces without explaining what each framework is meant to answer.

Reading and a quick check

IMF World Economic Outlook analysis on supply shocks can be used as current context, while Baqaee and Farhi provide the analytical base. Quick check: ask students to identify the difference between a shock, a transmission channel and an outcome.

Where a simulation fits

The PESTLE Analysis Simulation is the strongest fit in the course. Students assess all six PESTLE factors from shared evidence, weight their importance and defend a market-entry recommendation through contrasting Growth Strategy and Risk Management perspectives. The economic dimension should draw directly on the macro frameworks already taught.

Where it leads

The final analytical step is to connect macro scenarios to asset prices, credit conditions and firm-level strategy without collapsing macroeconomics into finance.

Central question: How do growth, inflation, rates and risk premia change the outlook for equities, bonds, credit and portfolio risk?

A business-school macro course benefits from one session that translates macro states into market implications. The goal is not to teach a full investments course. It is to show why the same growth or inflation news can have different asset-price effects depending on expectations, discount rates, margins, risk premia and what was already priced.

What this concept covers

• Macro surprises versus expected macro outcomes.
• Discount rates, bond yields and duration intuition.
• Growth, margins, equity earnings and valuation sensitivity.
• Risk premia, volatility and flight-to-quality dynamics.
• Diversification and portfolio rebalancing under changing macro regimes.

Learning outcomes

• Explain how a macro surprise can move multiple asset classes.
• Distinguish cash-flow effects from discount-rate effects.
• Connect inflation and policy scenarios to portfolio risk.
• Defend a rebalance using both quantitative and qualitative evidence.

Supports the course learning outcomes through progressively more applied evidence.

How to teach it

• Give teams three regimes: soft landing, stagflation and demand-led recession. Ask for directional implications before showing asset returns.
• Make students state what is already priced into the market.
• Use the Portfolio Management Simulation only after the macro scenario logic is explicit.

Case-style example

Falcon Pension Fund starts with 55% equities, 35% government bonds and 10% cash. Inflation surprises 1.5 percentage points higher, the yield curve shifts up 80 bp and consensus GDP growth is revised from 2.2% to 0.8%. Students recommend a rebalance and state whether they expect the shock to be persistent. One group can defend reducing duration; another can argue that recession risk makes government bonds attractive after the repricing. Students discover that macro views must be translated through valuation, timing and mandate.

Where students struggle

Students assume “higher rates = lower stocks” or “recession = buy bonds” without checking the inflation regime, starting valuations, client mandate and market expectations.

Reading and a quick check

Romer and Romer (2023) supports the policy-transmission side; a current central-bank or market note can supply the live scenario. Quick check: ask why identical GDP news can produce opposite equity reactions if inflation expectations differ.

Where a simulation fits

The Portfolio Management Simulation fits directly here. Hedge Fund and Pension Fund teams use CAPM, mean-variance optimisation and market updates to construct and rebalance portfolios. Require the debrief to identify which macro assumptions changed and how those assumptions affected expected returns, covariance, concentration and the relevant Alpha or Sharpe objective.

Where it leads

Students can now use macro analysis as one input to broader strategic and financing judgements in the capstone session.

Central question: Can students combine growth, inflation, labour, fiscal, monetary and external evidence into one defensible recommendation?

The final concept is integration. A good macro course should end with a country or business-environment decision that cannot be solved by one model. Students should build scenarios, identify the dominant constraints, recommend a policy or strategic response and state what evidence would change their view. This creates a clean capstone for assessment and for applied simulations.

What this concept covers

• Country risk and macro scenario construction.
• Policy mix: monetary, fiscal and structural choices.
• Interactions between domestic and external constraints.
• Business implications of macro regimes.
• Decision memos, uncertainty, signposts and recommendation defence.

Learning outcomes

• Integrate multiple macro indicators into a coherent scenario.
• Prioritise the constraints that matter most to a policy or business decision.
• Defend a recommendation under uncertainty.
• Specify signposts that would trigger a change in strategy.

Supports the course learning outcomes through progressively more applied evidence.

How to teach it

• Use one country dashboard and require teams to build base, upside and downside paths before making a recommendation.
• Add a surprise midway through the session and ask teams to update rather than restart their analysis.
• Use short oral defence to test whether students own the assumptions in their written work.

Case-style example

Novara expects real GDP growth of 1.0%, inflation of 4.2%, unemployment of 6.8%, public debt of 88% of GDP and a current-account deficit of 4.5%. Policy rates are 5.25%, while an election-year budget proposes a 2% of GDP tax cut. A multinational is considering a $300 million investment. Students recommend invest, stage or delay, with a 12-month macro scenario and three signposts. Growth teams may value demand support; risk teams may fear inflation, currency and debt pressure. Students discover that good macro judgement is explicit about trade-offs and contingencies.

Where students struggle

Students either create a “data dump” or overcompress the situation into one forecast. They need to show causal links, probabilities, dependencies and the evidence that would change their recommendation.

Reading and a quick check

Obstfeld (2023), “Natural and Neutral Real Interest Rates: Past and Future,” can support advanced discussion of policy space. Quick check: ask students to name one policy recommendation that looks sensible under the base case but dangerous under the downside case.

Where a simulation fits

The PESTLE Analysis Simulation works as the main capstone if the decision is market entry. The SWOT Analysis and Porter's Five Forces Simulations can be used as optional extensions when the lecturer wants students to translate the macro scenario into firm-level strategic diagnosis or industry attractiveness. Keep the distinction explicit: macroeconomics explains the environment; SWOT and Five Forces answer different strategic questions within that environment.

Where it leads

The course closes with students able to move from macro data and models to a conditional, evidence-based recommendation rather than a single-point forecast.

10-week course structure

A 10-week version should compress adjacent concepts without removing the analytical lifecycle. Combine measurement with growth, combine money with monetary policy, and use the external-shock session to integrate PESTLE. Keep at least one applied session and a final integrated scenario.

14-week course structure

A 14-week version creates space to separate difficult mechanisms, use richer official data and give students more time to reflect after simulations. Use the extra sessions for inflation dynamics, open-economy depth and assessed defence rather than adding disconnected topics.

The PESTLE Analysis Simulation places students in Tesla's unresolved market-entry decision: whether to commit to building a Gigafactory in India or continue serving the market through imports. Growth Strategy and Risk Management teams assess the same briefing from opposing mandates, then bring their factor scores, weights and written rationales into paired Approval Committees. For a Macroeconomics course, the lecturer should insist that the Economic factor is built from the course models and data rather than generic business commentary.

Why it fits here

Students already know inflation, interest rates, growth, exchange rates, fiscal policy and external shocks, so the simulation can focus on materiality, weighting, evidence and strategic consequence. It turns a descriptive environmental scan into a recommendation under disagreement.

What students do

• Assess all six PESTLE factors against a common briefing and dataset.
• Assign scores and allocate factor weights that total 100%.
• Write a rationale explaining why each factor matters.
• Compare Growth Strategy and Risk Management perspectives in a paired Approval Committee.
• Submit a final PESTLE assessment and market-entry recommendation.

Recommended course placement

• At the end of Session 10 after open-economy macro and external-shock teaching.
• Inside a two-hour applied class with pre-read and a separate debrief where the timetable allows.
• Before the capstone so students can reuse the macro assumptions in their country or business recommendation.

Knowledge students should have before the simulation

• Real GDP growth, inflation and labour-market indicators.
• Interest-rate and monetary-policy transmission.
• Fiscal-policy and public-debt basics.
• Exchange rates, trade and capital-flow channels.
• The distinction between macro environment, firm capability and industry structure.

Delivery format

Suggested debrief questions

• Which “Economic” factor carried the most weight and what macro evidence justified it?
• Where did a political, legal or environmental factor change the economic outlook rather than merely sit beside it?
• Which Growth and Risk assumptions were genuinely different?
• Would your recommendation change under the downside macro scenario from Session 5?
• Which factor was double-counted, and how would you fix the framework?

The Portfolio Management Simulation is a 3-6 hour team-based web simulation in which students construct and manage an equity portfolio across 25 global companies. Teams represent either a Hedge Fund, where the role objective is higher Alpha, or a Pension Fund, where the objective is a higher Sharpe Ratio. Students apply CAPM and mean-variance optimisation, then rebalance as company and market information changes over four quarters.

Why it fits here

By Session 11, students can explain how growth, inflation, interest rates and risk premia affect expected returns and discount rates. The simulation makes them convert that macro view into a portfolio while remaining constrained by a client mandate. The lecturer can then distinguish a good macro narrative from a good investment decision.

What students do

• Work in teams of 3-5 and receive a Hedge Fund or Pension Fund mandate.
• Use historical data and an Excel model to estimate expected returns, beta, covariance, volatility and portfolio weights.
• Apply CAPM and mean-variance optimisation to an initial portfolio.
• Trade and rebalance across later quarters as new company and market information arrives.
• Evaluate results using return, volatility and the relevant Alpha or Sharpe objective.

Recommended course placement

• After Session 11 once students have covered monetary policy, financial conditions and macro-to-market transmission.
• As one intensive 3-6 hour applied session or split across classes using pause/resume.
• Before a final individual memo that asks students which macro assumptions changed and how they altered the portfolio.

Knowledge students should have before the simulation

• Basic CAPM, Sharpe Ratio and mean-variance optimisation.
• Growth, inflation, monetary policy and financial-condition channels.
• Difference between a macro forecast and a priced market expectation.

Delivery format

Suggested debrief questions

• Which macro assumption most affected your expected-return view?
• Did the optimiser and the qualitative macro news point in the same direction?
• Was your rebalance a response to new information or simply to recent price movement?
• How did the client mandate change what counted as a good portfolio?
• Would the same inflation shock justify a different response for the other role?

The simulation result is evidence, not an automatic grade. Use a rubric that separates role-appropriate decision quality from luck, market outcome or team rank. Add an individual component where the mark must be attributable.

Grading with the PESTLE Analysis Simulation

Students analyse six external factors, weight them, write rationales and defend a market-entry recommendation from Growth Strategy and Risk Management perspectives.

• Accuracy and relevance of the macro evidence used in the Economic factor.
• Materiality and weighting across all six factors.
• Consistency between scores, written rationale and recommendation.
• Quality of challenge in the Approval Committee.
• Individual post-simulation explanation of which macro assumption mattered most.

Grading with the Portfolio Management Simulation

Teams use CAPM and mean-variance optimisation to construct and rebalance portfolios under Hedge Fund or Pension Fund mandates as new information arrives.

• Macro-to-market reasoning rather than final return alone.
• Consistency between client mandate and portfolio risk.
• Use of expected return, beta, covariance, volatility and portfolio weights.
• Quality of rebalancing decisions after new information.
• Individual explanation of model limits and macro assumptions.

Grading with the Debt Financing Simulation

Lender and borrower teams negotiate a complete refinancing package for Softie, including amount, pricing, maturity, repayment, security, guarantees, seniority and covenants.

• How macro rates and credit conditions are translated into a defensible negotiating range.
• Debt-capacity judgement and coherence of the full financing package.
• Recognition of borrower-lender incentive differences.
• Quality of concessions and downside reasoning.
• Individual reflection on which macro assumption would most change the terms.

Grading with the SWOT Analysis Simulation

Students work in executive teams on a high-stakes expansion decision, using strengths, weaknesses, opportunities and threats to build and defend a strategic recommendation.

• Correct separation of internal and external factors.
• Use of macro evidence only where it genuinely belongs under opportunities or threats.
• Prioritisation rather than list length.
• Consistency between SWOT diagnosis and expansion decision.
• Ability to explain how the recommendation would change under a different macro scenario.

Grading with the Porter's Five Forces Simulation

Growth and Risk teams assess the U.S. premium cafe industry, score the five forces, write evidence-based reasoning and decide Go, Conditional Go or Reject in an Investment Committee.

• Application of all five forces as one industry-attractiveness judgement.
• Quality of evidence supporting each force score.
• Clear separation of macro-environment evidence from industry-structure effects.
• Logical consistency between force analysis and final recommendation.
• Individual explanation of which macro change would alter one or more forces.

The platform records team decisions, terms, written reasoning or comparative outcomes depending on the simulation. That evidence supports your academic judgement; it does not replace it, and it does not establish which individual student made which argument.