Statistics — Page 18
Explore statistical methods, their assumptions, interpretation and limitations.
Research field
Method family
Dynamic Difference-in-Differences
Dynamic Difference-in-Differences extends the classic DiD framework to settings where units adopt treatment at different times. Rather than collapsing all variation into a single 2x2 comparison, it estimates group-time average treatment eff...
Dynamic Entropy Balancing
Dynamic Entropy Balancing extends the entropy balancing reweighting approach to settings with time-varying treatments in panel or longitudinal data. It constructs unit weights at each time period such that the covariate distributions of tre...
Dynamic Event Study Design
The dynamic event study design extends the standard difference-in-differences framework by estimating treatment effects at each period before and after the event, rather than collapsing everything into a single post-treatment coefficient. B...
Dynamic Factor Model
A Dynamic Factor Model (DFM) extracts a small number of latent common factors from a large panel of economic time series and uses those factors to forecast or nowcast a target variable. Formalized for macroeconomic forecasting by James Stoc...
Dynamic Fuzzy Regression Discontinuity
Dynamic Fuzzy Regression Discontinuity Design extends the standard fuzzy RDD to a panel or multi-period setting, allowing researchers to estimate how the causal effect of a probabilistic threshold-based treatment evolves over time. By combi...
Dynamic Hamiltonian Monte Carlo
Dynamic Hamiltonian Monte Carlo — widely known as the No-U-Turn Sampler (NUTS) — is an adaptive extension of Hamiltonian Monte Carlo that automatically selects the number of leapfrog integration steps during each MCMC transition, removing t...
Dynamic Instrumental Variables
Dynamic Instrumental Variables estimation addresses endogeneity in panel models where the outcome depends on its own past values. By first-differencing to remove unit fixed effects and then using lagged levels as instruments for the differe...
Dynamic Interrupted Time Series
Dynamic Interrupted Time Series (Dynamic ITS) extends the standard ITS design by allowing intervention effects to build up, decay, or shift over multiple time lags rather than assuming a single instantaneous level change. It estimates how a...
Dynamic Inverse Probability Weighting
Dynamic Inverse Probability Weighting (Dynamic IPW) estimates the causal effect of a time-varying treatment sequence by reweighting observed data to mimic a hypothetical randomised trial. Developed by Robins and colleagues in the context of...
Dynamic Matching Estimator
The Dynamic Matching Estimator extends standard matching methods to settings where treatment is assigned sequentially over multiple periods. Instead of a single treatment decision, units receive or forgo treatment at each time point, and th...
Dynamic Metropolis-Hastings Algorithm
The Dynamic Metropolis-Hastings (Dynamic MH) algorithm applies the Metropolis-Hastings MCMC sampler to Bayesian state-space and time-varying parameter models. At each time step, latent states or evolving parameters are updated via proposal-...
Dynamic Monte Carlo Simulation
Dynamic Monte Carlo (DMC) simulation is a computational method that tracks the stochastic time evolution of a system by drawing random event sequences weighted by transition rates. Unlike static Monte Carlo sampling of equilibrium distribut...
Dynamic OLS
Dynamic OLS is a cointegrating-regression estimator introduced by Stock and Watson (1993) that recovers the long-run relationship between I(1) variables. It augments the static regression with leads and lags of the differenced regressors, c...
Dynamic Panel Data Model
The dynamic panel data model extends standard panel regression by including a lagged value of the outcome variable as a regressor, capturing persistence and adjustment dynamics. Because the lagged dependent variable is correlated with the u...
Dynamic Panel Event Study
The dynamic panel event study is a quasi-experimental method that uses panel data to trace out how a treatment effect evolves over time — before and after a defining event — by estimating a flexible regression of leads and lags around the t...
Dynamic Panel Models in Politics
Dynamic panel models for political science analyze time-series cross-section (TSCS) data — repeated observations on countries, dyads, states, or other units over many years — where the outcome today depends on its own past. By including a l...
Dynamic Particle Filter
A dynamic particle filter is a sequential Monte Carlo algorithm that tracks an evolving hidden state over time by maintaining a population of weighted random samples — particles — each representing a plausible trajectory. As new observation...
Dynamic Propensity Score Matching
Dynamic Propensity Score Matching (DPSM) extends classic propensity score matching to settings where treatment is assigned repeatedly over time and earlier treatment choices influence later ones. It estimates the causal effect of entire tre...
Dynamic Sequential Monte Carlo
Dynamic Sequential Monte Carlo (Dynamic SMC) is a Bayesian computational method that maintains and updates a population of weighted samples — particles — as new observations arrive over time. It propagates particles through a dynamic system...
Dynamic Synthetic Control Method
The Dynamic Synthetic Control Method extends the classic synthetic control framework to evaluate treatments that unfold over multiple periods or change in intensity over time. It constructs a weighted combination of untreated units that mat...
Dynamic Variational Inference
Dynamic variational inference extends the variational inference framework to sequential and time-series settings by positing a structured approximate posterior that respects the temporal ordering of latent states. It jointly learns a genera...
EFA
Exploratory factor analysis reduces a large set of observed variables into a smaller number of latent common factors. It is widely used in scale development and psychometrics to uncover the dimensional structure that underlies a set of corr...
EFA for Scale Development
Exploratory Factor Analysis for Scale Development is the psychometric application of EFA in which an item pool is administered and the resulting response data are analysed to discover the latent factor structure underlying the items. Origin...
EGARCH
EGARCH is an asymmetric GARCH variant, introduced by Nelson in 1991, that models the leverage effect in which bad news raises volatility more than good news of the same size. It captures the negative-shock asymmetry of financial return seri...