Statistics — Page 36
Explore statistical methods, their assumptions, interpretation and limitations.
Research field
Method family
Prospective Old-Age Dependency Ratio
The prospective old-age dependency ratio (POADR) is a measure of population aging that redefines the onset of old age in terms of remaining life expectancy rather than a fixed chronological age such as 65. Proposed by Warren Sanderson and S...
QARDL
QARDL (Quantile Autoregressive Distributed Lag) combines quantile regression with ARDL modeling to estimate conditional relationships at different points of the distribution, revealing heterogeneous short-run and long-run effects. Introduce...
Quandt-Andrews Test
The Quandt-Andrews test, formalized by Andrews (1993), detects structural breaks in regression parameters when the breakpoint date is unknown a priori. It sweeps all candidate break dates within a trimmed interior of the sample, computes a...
Quantile Regression
Quantile regression models conditional quantiles of an outcome - the median, the 25th or 75th percentile, and so on - rather than the conditional mean that OLS targets. Introduced by Koenker and Bassett in 1978, it reveals how predictors ac...
Quantile VAR
Quantile VAR estimates impulse responses of multivariate systems conditional on different quantiles of the distribution, revealing how shocks propagate heterogeneously across the conditional distribution. Introduced by Koenker and Xiao (200...
Quantile-on-Quantile Regression
Quantile-on-quantile regression is a nonparametric technique that estimates how the quantiles of one variable depend on the quantiles of another. By combining standard quantile regression with local linear smoothing, it produces a full two-...
RANSAC Regression
RANSAC Regression is a robust linear regression method introduced by Fischler and Bolles in 1981 that fits a model to the inlier points of a dataset while automatically excluding outliers. Instead of fitting all the data at once, it repeate...
ROC analysis
ROC analysis evaluates how well a continuous or ordinal test variable discriminates between two binary outcome classes. By plotting the true positive rate (sensitivity) against the false positive rate (1 − specificity) across all decision t...
Radiation Model
The Radiation Model, introduced by Simini et al. in 2012, is a parameter-free model for predicting human mobility and migration flows between geographic locations. Drawing an analogy from radiation physics, it predicts trip volumes based so...
Ramsey RESET Test
The Ramsey RESET test, proposed by James Ramsey in 1969, is a general test for functional-form misspecification in a linear regression — for omitted nonlinear relationships between the response and the regressors. It adds powers of the fitt...
Ramsey-Cass-Koopmans Model
The Ramsey-Cass-Koopmans model, developed initially by Frank Ramsey in 1928 and formalized by David Cass and Tjalling Koopmans in 1965, is the workhorse model of macroeconomic growth theory. It describes how rational consumers optimize cons...
Random Effects Model
The Random Effects model is a panel-data regression that treats unobserved individual heterogeneity as a random component drawn from a common distribution, rather than a separate parameter for each unit. It is a standard estimator in panel...
Random Effects Panel Model
The random effects model is a panel data estimator that explains an outcome using both within-unit and between-unit variation, treating the unobserved unit-specific heterogeneity as a random, normally distributed term rather than a fixed pa...
Random Survival Forest
Random Survival Forest (RSF), introduced by Ishwaran, Kogalur, Blackstone, and Lauer in 2008, is an ensemble machine learning method that adapts the Random Forest algorithm to time-to-event (survival) data. Trees are grown using log-rank sp...
Randomization Inference
Randomization inference, introduced by Ronald A. Fisher in The Design of Experiments (1935), computes an exact p-value by evaluating a test statistic across all possible treatment assignments under Fisher's sharp null hypothesis. It is rega...
Real Business Cycle Model
The Real Business Cycle (RBC) model, developed by Finn Kydland and Edward Prescott in 1982, is a dynamic stochastic general equilibrium framework that explains macroeconomic fluctuations as rational responses to exogenous technological shoc...
Realized Volatility
Realized volatility estimates an asset's variance directly from high-frequency intraday returns rather than from a parametric latent process. The Heterogeneous Autoregressive (HAR) model of Corsi (2009), building on the realized-volatility...
Recidivism Survival Analysis
Recidivism survival analysis models the time from a release or index event until an individual reoffends, treating reoffending as a time-to-event ('failure') outcome with censoring for those not observed to fail. It applies survival methods...
Recreation Demand Travel Cost Model
The recreation demand travel cost model values a recreation site — a national park, beach, lake, or heritage attraction — by exploiting the fact that visitors reveal how much the experience is worth to them through the cost they incur to ge...
Recurrent Event Model
A recurrent event model is a survival analysis extension, formalised through the landmark contributions of Prentice, Williams and Peterson (1981), Andersen and Gill (1982), and Wei, Lin and Weissfeld (1989), that models time-to-event data w...
Redistribution Preference Analysis
Redistribution preference analysis examines why individuals support or oppose government efforts to reduce inequality. The self-interest baseline comes from Meltzer and Richard's 1981 model, in which the demand for redistribution falls with...
Reference Price Modeling
Reference price models capture the behavioral reality that consumers judge a price not in absolute terms but relative to an internal benchmark — a reference price they have formed from past prices. When the observed price falls below the re...
Regime-Switching Model
The Markov regime-switching model, introduced by James D. Hamilton in 1989, is a hidden-state time-series model in which financial series such as returns or volatility behave with different parameters across distinct economic regimes (bull/...
Regression Discontinuity Design
Regression Discontinuity Design is a quasi-experimental method that estimates a local causal effect around a threshold (cutoff) value, comparing units just below and just above the cutoff as if they were almost randomly assigned. It is the...