Statistics — Page 20
Explore statistical methods, their assumptions, interpretation and limitations.
Research field
Method family
Gregory-Hansen Test
The Gregory-Hansen test, introduced by Allan Gregory and Bruce Hansen in 1996, extends the standard Engle-Granger cointegration framework to allow for a single unknown structural break in the cointegrating relationship. It is designed for r...
Group-Based Trajectory Model
Group-based trajectory modeling (GBTM) is a finite-mixture method that identifies clusters of individuals who follow similar developmental paths of a behavior — most famously offending — over age or time. Introduced to criminology by Daniel...
Growth Accounting
Growth accounting is a production-function-based framework that decomposes the growth rate of aggregate output into the contributions of growth in measured inputs — typically capital and labour — and a residual that captures the growth in t...
HAR-RV Model
The HAR-RV model, introduced by Fulvio Corsi in 2009, forecasts realized volatility by decomposing it into daily, weekly, and monthly components. It is a simple linear regression that mirrors how market participants with different investmen...
HJM Framework
The Heath-Jarrow-Morton (HJM) framework (1992) is a general no-arbitrage approach to modeling the entire term structure of forward rates. Unlike short-rate models, HJM works directly with forward rates f(t,T) and specifies their volatility;...
Hamiltonian Monte Carlo
Hamiltonian Monte Carlo (HMC) is a gradient-based Markov chain Monte Carlo algorithm that uses the geometry of the log-posterior surface to make large, informed jumps through parameter space instead of the small random steps of classical MC...
Hamiltonian Monte Carlo with Measurement Error
Hamiltonian Monte Carlo (HMC) with measurement error is a Bayesian computational strategy for fitting models where one or more covariates are observed with noise. HMC samples jointly from the posterior over model parameters and the unobserv...
Hamiltonian Monte Carlo with Missing Data
Hamiltonian Monte Carlo with missing data extends the gradient-based HMC sampler to handle incomplete observations by treating missing values as additional unknown parameters. The posterior over model parameters and missing values is sample...
Hatemi-J Asymmetric Causality
The Hatemi-J asymmetric causality test, introduced by Abdulnasser Hatemi-J in 2012, extends the Granger causality framework to allow causal relationships between the positive and negative components of integrated time series to differ. By d...
Hatemi-J Cointegration Test
The Hatemi-J cointegration test, introduced by Abdulnasser Hatemi-J in 2008, tests for a long-run equilibrium relationship between integrated time series while allowing for up to two unknown structural breaks in the cointegrating vector. It...
Hausman Test
The Hausman test is a specification test, introduced by Jerry A. Hausman in 1978, that decides between the fixed-effects (FE) and random-effects (RE) estimators in panel data models. The null hypothesis is that the random-effects estimator...
Healthy Life Expectancy
Healthy life expectancy partitions ordinary life expectancy into the years a person can expect to live in good health and the years expected to be lived with disability or ill health. Building on the life table, the classic Sullivan method...
Heckman Selection Model
The Heckman selection model, introduced by James J. Heckman in 1979, is a two-step model that corrects sample selection bias when the outcome is only observed for a non-random subset of cases. A probit selection equation models who is obser...
Hedonic Pricing
The hedonic pricing model, developed by Sherwin Rosen in 1974 and building on Kevin Lancaster's characteristics theory (1966), is an econometric method for valuing the implicit prices of product attributes by regressing market prices on obs...
Heligman-Pollard Model
The Heligman-Pollard model is an eight-parameter parametric law that describes the age pattern of mortality across the entire human lifespan in a single equation. Introduced by Larry Heligman and John Pollard in 1980, it represents the odds...
Heteroscedasticity-Robust Standard Errors
Heteroscedasticity-robust standard errors are a correction to the covariance matrix of an OLS regression that yields valid inference when the error variance is not constant. Introduced by Halbert White in 1980 and refined into the finite-sa...
Hiemstra-Jones Causality
The Hiemstra-Jones test, introduced in 1994, is a nonparametric procedure for detecting nonlinear causal relationships between two time series after removing their linear interdependencies. Developed in the context of stock price and tradin...
Hierarchical Approximate Bayesian Computation
Hierarchical ABC is a likelihood-free Bayesian inference method designed for multilevel data structures in which individual-level parameters are themselves drawn from a population-level distribution. By combining simulation-based rejection...
Hierarchical Bayes Choice Model
Hierarchical Bayes (HB) choice models estimate a separate set of preference weights — partworths — for every individual respondent, while borrowing strength across respondents through a shared population distribution. The model has two leve...
Hierarchical Bayesian Inference
Hierarchical Bayesian inference is a probabilistic modeling framework that organises parameters into levels, placing priors on the group-level parameters and hyperpriors on the parameters governing those priors. It enables partial pooling o...
Hierarchical Bayesian Model Averaging
Hierarchical Bayesian model averaging (HBMA) combines Bayesian model averaging with hierarchical model structure, averaging posterior quantities over a set of candidate models weighted by each model's posterior probability. Rather than sele...
Hierarchical Bayesian Network
A hierarchical Bayesian network is a probabilistic graphical model that organizes variables across multiple levels of abstraction. Higher-level nodes govern the prior distributions of lower-level nodes through hyperparameters, enabling stru...
Hierarchical Bootstrap Simulation
Hierarchical bootstrap simulation is a resampling technique designed for data with nested or clustered structure — students within schools, patients within hospitals, repeated measures within subjects. It preserves the natural grouping of t...
Hierarchical Hamiltonian Monte Carlo
Hierarchical Hamiltonian Monte Carlo (Hierarchical HMC) applies Hamiltonian Monte Carlo sampling to Bayesian hierarchical models, addressing the severe geometric challenges those models pose. By combining non-centered parameterizations with...