Statistics — Page 47
Explore statistical methods, their assumptions, interpretation and limitations.
Research field
Method family
Structural Break Toda-Yamamoto Causality
The structural break Toda-Yamamoto causality test extends the standard Toda-Yamamoto modified Wald (MWALD) procedure to accommodate one or more structural breaks in the time series. By identifying break dates first and then including dummy...
Structural Break VAR Model
The Structural Break VAR model extends the standard Vector Autoregression (VAR) framework by allowing coefficient matrices and error covariance to shift at one or more unknown break dates. It is designed for multivariate time series where e...
Structural Break WLS
Structural Break WLS combines Weighted Least Squares estimation with explicit detection and correction for structural breaks — abrupt regime shifts — in the data. By identifying break points and assigning observation-level weights that acco...
Structural Time Series Model
The Structural Time Series Model, in its Basic Structural Model (BSM) form, is Andrew Harvey's state-space approach that decomposes a series into separate stochastic trend, seasonal, cyclical, and irregular components. Developed in Harvey's...
Structural VAR
Structural VAR extends the reduced-form VAR by imposing economic theory-based restrictions that identify orthogonal structural shocks. This allows researchers to disentangle the causal effects of distinct economic disturbances — such as sup...
Structural break DCC-GARCH
Structural break DCC-GARCH extends Engle's Dynamic Conditional Correlation GARCH framework by explicitly allowing the correlation and volatility structure to shift at one or more structural break points in the sample. It models time-varying...
Structural break Engle-Granger cointegration
The structural break Engle-Granger cointegration test, most commonly implemented via the Gregory-Hansen (1996) procedure, extends the classical Engle-Granger two-step test to allow for a single unknown structural break in the long-run coint...
Structural break Johansen cointegration
The structural break Johansen cointegration test extends the standard maximum-likelihood Johansen procedure to settings where the multivariate time series exhibits level shifts or trend breaks. By incorporating dummy variables or shift regr...
Structural break PP unit root test
The structural break Phillips-Perron (PP) unit root test extends the classical PP framework to allow for one or more discrete shifts in the level or trend of a time series. By endogenously or exogenously identifying break dates and controll...
Structural break SVAR model
The structural break SVAR model extends the standard Structural Vector Autoregression by allowing one or more discrete shifts in the system's parameters across time. It simultaneously identifies causal (structural) shocks and accounts for r...
Structural break VECM
The Structural Break VECM extends the standard Vector Error Correction Model to allow the cointegrating relationships, adjustment speeds, or short-run dynamics to shift at one or more known or estimated break dates. It preserves the long-ru...
Structural break Zivot-Andrews test
The Zivot-Andrews test is an endogenous structural break unit root test that determines the break point from the data rather than imposing it externally. It tests for a unit root against the alternative of stationarity around a single struc...
Student Growth Percentiles
Student growth percentiles (SGPs) describe how much a student grew academically relative to peers with similar score histories. Introduced by Damian Betebenner in 2009, the method fits a series of conditional quantile regressions of a curre...
Sullivan Method
The Sullivan method is a simple, widely used technique for estimating health expectancy — the average number of years a person can expect to live in a given health state, such as free of disability. Introduced by Daniel Sullivan in 1971, it...
Surname-Based Mobility Analysis
Surname-based mobility analysis estimates how strongly social status is inherited across generations without linking a single parent to a single child. Developed by Gregory Clark, it exploits the fact that surnames cluster: certain names we...
Survival Analysis Power Analysis
Power analysis for survival studies determines how many participants — and how many observed events — are required so that a log-rank test or Cox regression has a sufficient probability of detecting a clinically meaningful difference in sur...
Survival Analysis of First Migration
Survival analysis of first migration treats the move out of one's place of origin as a timed event and asks not whether but when a person first migrates. Rather than modeling a binary 'migrated or not' outcome, it follows individuals from t...
Survival Regression
Survival regression models the time until an event occurs — such as death, failure, or relapse — as a function of covariates. Unlike ordinary regression, it properly accounts for censored observations (cases where the event had not yet occu...
Synthetic Difference-in-Differences
Synthetic Difference-in-Differences (SDID) combines synthetic control and difference-in-differences approaches to estimate treatment effects when a policy or intervention affects one unit (country, firm) at a point in time. Introduced by Ar...
System GMM
System GMM is a generalized method of moments estimator for dynamic panel models that contain a lagged dependent variable. Introduced by Blundell and Bond (1998), building on Arellano and Bover, it augments the differenced equation of the e...
TAR / SETAR
TAR and SETAR are nonlinear autoregressive models introduced by Howell Tong (1990) that allow a time series to follow different linear dynamics in distinct regimes, separated by one or more threshold values. SETAR is the self-exciting varia...
TBATS
TBATS is an innovations state space forecasting model, introduced by De Livera, Hyndman and Snyder (2011), that combines a Box-Cox transformation, ARMA errors and trigonometric (Fourier) seasonal terms. It is built to handle continuous time...
TGARCH model
The Threshold GARCH (TGARCH) model extends the standard GARCH framework by allowing positive and negative return shocks to have asymmetric effects on conditional variance. Negative shocks — bad news — typically amplify volatility more than...
TVP-FAVAR
TVP-FAVAR is a hybrid framework combining factor-augmented VARs with time-varying parameter estimation via Kalman filtering. Introduced by Bernanke et al. (2005) and refined by Primiceri (2005), it extracts latent economic factors (e.g., a...