Warehouse / Glossary
40 plain-language definitions of the terms that recur across macro and monetary economics — a starting point if you're new to the field.
Looking for how a specific paper uses a term? See the full paper-scoped Key Concepts index — 40 curated here, thousands more there.
A model built from the ground up out of the decisions of individual households and firms — how much to consume, save, invest, or charge — that are then combined to see what happens for the economy as a whole. "Dynamic" means decisions today depend on expectations about the future; "stochastic" means the model includes random shocks (a surprise productivity gain, an oil-price spike); "general equilibrium" means every market (goods, labor, capital) clears simultaneously. DSGE models are the workhorse of modern macroeconomic policy analysis.
A New Keynesian model (see below) in which households aren't all identical — some are rich, some are poor, some can borrow easily and some can't. This matters because a policy like an interest-rate cut affects a cash-strapped household very differently than a wealthy one, so HANK models can speak to questions about inequality and distribution that a single "representative household" model cannot.
The empirical relationship between inflation and economic slack (often measured by unemployment or the output gap): when the economy is running hot, prices tend to rise faster; when it's slack, inflation tends to cool. It's the theoretical backbone of most models used to think about the tradeoff a central bank faces between fighting inflation and supporting employment — though how strong and stable this relationship actually is has been fiercely debated for decades.
The question of whether a study can actually isolate the causal effect it claims to measure, as opposed to picking up something else that happens to move alongside it. For example, showing that interest-rate cuts and rising output tend to occur together doesn't prove cuts cause the growth — a strong economy might be why rates were cut in the first place. "Identification strategy" is the specific piece of variation or evidence a paper leans on to rule out these alternative explanations.
A tool for identification (see above): a variable that affects the outcome you care about only through the specific channel you're studying, not through any other back door. If you want to know whether higher income causes better health, and richer people also tend to have other advantages, you might use something like a lottery win as an instrument — it changes income for reasons that have nothing to do with a person's underlying health or discipline.
A statistical model that tracks how several economic variables (say, output, inflation, and interest rates) move together over time, with extra assumptions added to separate out genuine cause-and-effect shocks (like a monetary policy surprise) from mere correlation. It's one of the most common tools for asking "what happens to the economy after a specific kind of shock?"
An alternative to SVARs for tracing out how the economy responds over time to a shock: instead of estimating one big model of how everything interacts, you run a separate, simple regression for each time horizon you care about (what happens after 1 month, after 6 months, after a year). It's more flexible and often easier to interpret, at some cost in statistical efficiency.
The practical floor on how low a central bank can push its policy interest rate — once it's near zero, cutting further offers little extra stimulus and can even backfire (people would rather hold cash than accept a negative return). Some central banks have pushed slightly negative, which is why the more general term "effective lower bound" is often used instead of a hard zero.
QE is when a central bank buys large quantities of government bonds (or other assets) to push down longer-term interest rates and pump money into the financial system, typically used once short-term rates are already near zero. QT is the reverse — letting those holdings run off or selling them — used to withdraw stimulus once it's no longer needed.
The gap between the price a firm charges and its cost of producing one more unit, usually expressed as a percentage above cost. Rising markups across the economy are one candidate explanation for why wages haven't kept pace with productivity, and for a flatter Phillips curve — if firms have more pricing power, prices respond less to changes in demand.
The part of economic output that isn't explained just by adding more workers or more machines — it captures how efficiently those inputs are combined (better technology, smarter organization, more innovation). TFP growth is the main long-run driver of rising living standards in most growth models.
An analysis that accounts for every market adjusting at once, rather than looking at one market in isolation. A tax cut, for instance, doesn't just change take-home pay — it can shift labor supply, prices, interest rates, and investment too, and general equilibrium models try to capture all of those knock-on effects together.
A modeling approach that allows different households or firms to differ from one another — in wealth, income, age, productivity — rather than assuming everyone is a copy of one "representative" household. This lets a model speak to distributional questions (who benefits from a policy, who bears the cost) that representative-agent models average away.
The observation that firms don't change their prices constantly — a menu, a catalog, or a posted price often stays fixed for weeks or months even as costs and demand shift. Sticky prices are the key ingredient that lets monetary policy affect the real economy (output, employment) in the short run, not just prices, in most modern macro models.
A central bank communicating its future policy intentions — "we expect to keep rates low until inflation reaches our target" — as a tool in its own right, separate from actually moving rates today. Because expectations about future policy affect decisions made now (a mortgage, a business investment), credible guidance about the future can influence the economy immediately.
How much extra economic output is generated per dollar of government spending (or per dollar of tax cut). A multiplier above 1 means the spending pays for itself many times over in extra activity; a multiplier near zero means it mostly displaces private spending instead of adding to it. The size of the multiplier — and how much it depends on the state of the economy — is one of the most contested numbers in macroeconomics.
A family of macro models that combines the general-equilibrium, forward-looking logic of modern macroeconomics with Keynesian ingredients like sticky prices — the reason monetary policy can move real output, not just prices, in these models. It's the dominant framework central banks use for policy analysis today, usually implemented as a form of DSGE model.
The assumption that people form their expectations about the future using all the information available to them and an understanding of how the economy actually works, rather than by simple rules of thumb or systematically repeating past mistakes. It doesn't mean people have perfect foresight — surprises still happen — just that their forecast errors aren't predictable in advance.
A chart showing how a variable (like output or inflation) is expected to evolve over time following a one-time shock (like a surprise interest-rate hike), holding everything else fixed. It's the standard way empirical macro papers report "what happens after X" — usually shown for several years ahead of the shock, sometimes with confidence bands showing how uncertain the estimate is.
The interest rate that would keep the economy running at full employment with stable inflation — neither stimulating nor restraining activity. It isn't directly observable and has to be estimated, but it's the benchmark central banks implicitly compare their actual policy rate against to judge whether policy is loose or tight.
The difference between how much the economy is actually producing and how much it could sustainably produce at full employment without overheating. A positive gap suggests the economy is running hot (risk of rising inflation); a negative gap suggests slack (risk of rising unemployment). Like the natural rate of interest, it must be estimated rather than observed directly.
Setting a model's parameters to match specific known facts about the real economy (say, the average share of income spent on consumption) rather than statistically estimating them from data. It's common in DSGE modeling, especially for parameters that are well established from outside evidence, and is usually paired with a separate check that the calibrated model also matches other facts it wasn't explicitly tuned to hit.
The tendency for people or firms to take on more risk once they're protected from the downside of that risk. A classic example: a bank that expects to be bailed out if it fails may take on riskier loans than it otherwise would, because it doesn't bear the full cost of those risks going bad.
A problem that arises when one side of a transaction knows something the other side doesn't, and that hidden information skews who chooses to participate. In insurance, for instance, people who know they're higher-risk are more likely to buy coverage, which can push prices up for everyone and drive lower-risk customers out of the market entirely.
The idea that monetary policy affects the economy partly through its effect on the availability and cost of credit — not just through the interest rate itself. A rate hike can make banks more cautious about lending or shrink the value of collateral borrowers can pledge, tightening credit conditions on top of the direct effect of higher rates.
The classical idea that, over the long run, growth in the money supply translates roughly one-for-one into inflation, with real output determined by other, non-monetary factors. It remains a touchstone in monetary economics even though most modern models add substantial short-run nuance — money and prices don't move in lockstep month to month.
The idea that an increase in the monetary base (the cash and reserves a central bank directly controls) leads to a larger increase in the broader money supply, as banks lend out deposits and that money gets redeposited and lent out again. In practice this mechanical relationship has proven far less stable than the simple textbook version suggests, especially since the 2008 financial crisis.
The idea — and, in most advanced economies, the legal structure — that keeps monetary policy decisions insulated from short-term political pressure, on the theory that politicians facing elections have an incentive to favor looser policy than is good for long-run price stability. Independence is usually paired with a mandate (like an inflation target) and transparency requirements that hold the central bank accountable in a different way.
A monetary policy framework in which a central bank publicly commits to keeping inflation near a specific numerical target (commonly 2%) and explains its actions in those terms. The idea is that a clear, credible target anchors the public's inflation expectations, which itself helps keep actual inflation low and stable.
An early style of DSGE model in which the economy's ups and downs are driven mainly by real shocks — especially swings in productivity — rather than by monetary factors or nominal frictions like sticky prices. RBC theory was highly influential in the 1980s and remains the ancestor of most modern DSGE models, even ones that add back the monetary and nominal features RBC left out.
A situation where interest rates are already at (or near) zero and further monetary stimulus loses its usual bite — people are willing to hold as much cash as they want at that rate, so pushing more money into the system doesn't spur additional spending. Liquidity traps are a major reason central banks turned to tools like quantitative easing and forward guidance after the 2008 crisis.
A rule of thumb, proposed by economist John Taylor, describing how a central bank should set its policy interest rate as a function of how far inflation is from target and how far output is from its potential. It's widely used both as a normative benchmark (what should the rate be?) and a descriptive one (does actual policy roughly follow this pattern?).
The roughly two-decade period (mid-1980s to 2007) of unusually low volatility in output and inflation across advanced economies, widely credited at the time to improved monetary policy — though the 2008 financial crisis that ended it prompted a lot of rethinking about how much of that calm was genuine policy skill versus luck.
A statistical problem where a variable you're using to explain an outcome is itself partly caused by that outcome (or by something else that also affects the outcome), which biases naive estimates of cause and effect. It's the general name for the problem that identification strategies and instrumental variables are designed to solve.
A research design that estimates a policy's effect by comparing the change over time in an affected group against the change over the same period in a similar, unaffected group — the idea being that the unaffected group's trend shows what would have happened anyway, absent the policy.
A point in time at which the underlying relationship a model is trying to estimate genuinely shifts — say, how strongly inflation responds to unemployment before and after a change in how the central bank operates. Ignoring a real structural break can make a model that fit history well suddenly perform badly going forward.
A statistical estimation technique that finds parameter values by matching theoretical predictions (like an average or a correlation implied by a model) to their real-world counterparts in the data, without needing to assume a full probability distribution for the data. It's especially common in finance and in structural macroeconomic estimation.
Data that tracks the same units — people, firms, countries — over multiple points in time, rather than a single snapshot (cross-section) or a single unit's history (time series). Panel data lets researchers control for differences that don't change over time within a unit, which is often central to a credible identification strategy.