Statistics — Page 50
Explore statistical methods, their assumptions, interpretation and limitations.
Research field
Method family
Time-varying parameter VAR model
The Time-Varying Parameter VAR (TVP-VAR) model extends the standard vector autoregression by allowing the coefficients and error covariances to evolve gradually over time. Estimated via Bayesian methods and MCMC simulation, it captures how...
Time-varying parameter VECM
The Time-Varying Parameter Vector Error Correction Model extends the standard VECM by allowing the adjustment speeds, cointegrating vectors, and short-run dynamics to drift over time. It captures long-run cointegrating relationships among i...
Time-varying parameter WLS
Time-Varying Parameter WLS is a regression technique for time-series data in which the slope and intercept coefficients are allowed to change over time while observations are weighted to account for heteroscedasticity or to discount distant...
Time-varying parameter Zivot-Andrews test
The time-varying parameter Zivot-Andrews test extends the classic Zivot-Andrews (1992) structural break unit root test by allowing the regression coefficients to evolve over time. Rather than assuming fixed parameters across the full sample...
Time-varying parameter difference GMM
Time-varying parameter difference GMM combines the Arellano-Bond first-difference GMM estimator for dynamic panels with a state-space or local-smoothing framework that allows regression coefficients to drift over time. It handles endogeneit...
Time-varying parameter dynamic panel data model
The time-varying parameter dynamic panel data model combines lagged dependent variables with coefficients that evolve over time across panel units. It extends conventional dynamic panel models by allowing slope parameters to shift across pe...
Time-varying parameter fixed effects model
The time-varying parameter fixed effects (TVP-FE) model extends the classical two-way fixed effects panel regression by allowing one or more slope coefficients to change over time while still controlling for unobserved individual heterogene...
Time-varying parameter quantile-on-quantile regression
TVP-QQ regression extends the quantile-on-quantile (QQ) framework by allowing the slope coefficients to evolve over time. It maps how the quantiles of a predictor variable affect the quantiles of an outcome differently across the joint dist...
Time-varying parameter random effects model
The time-varying parameter random effects model extends the classic random effects panel framework by allowing regression coefficients to change over time and across units. Rather than imposing a single fixed slope for all individuals and p...
Time-varying parameter system GMM
Time-Varying Parameter System GMM extends the Blundell-Bond System Generalized Method of Moments estimator to allow regression coefficients to change over time. By combining the instrument-based correction for dynamic endogeneity with a tim...
Tobin's Q Firm Value Analysis
Tobin's q is the ratio of a firm's market value to the replacement cost of its assets, and it serves in strategy and industrial organization as a forward-looking measure of value creation and economic rent. A q above one means the market va...
Tobit Model
The Tobit model is a regression for outcomes that are censored at a threshold, estimating the relationship by maximum likelihood. Introduced by James Tobin in 1958, it addresses the pile-up of observations at a limit (typically zero) in dat...
Toda-Yamamoto Causality
The Toda-Yamamoto (TY) causality test, introduced by Toda and Yamamoto (1995), provides a robust procedure for testing Granger non-causality in vector autoregressive (VAR) models when the variables may be integrated or cointegrated of arbit...
Toda-Yamamoto causality test
The Toda-Yamamoto (TY) causality test is a modified Wald procedure for testing Granger causality in vector autoregressions (VARs) estimated in levels, even when variables are nonstationary or cointegrated. By intentionally over-fitting the...
Total Factor Productivity
Total factor productivity (TFP), also called multifactor productivity, measures how much output an economic unit produces from a given bundle of all its inputs taken together — capital, labour, and often intermediate materials. It is the ef...
Tourism Almost Ideal Demand System
The Almost Ideal Demand System (AIDS), introduced by Angus Deaton and John Muellbauer in 1980, is a system of demand equations grounded in consumer theory that models how a budget is allocated across competing goods through their expenditur...
Tourism Demand Elasticity Modeling
Tourism demand elasticity modeling estimates how responsive tourist demand is to changes in its key drivers, above all source-market income and the price of travel. The income elasticity measures the percentage change in demand for a one-pe...
Tourism Demand Forecasting
Tourism demand forecasting predicts future tourist arrivals, overnight stays, or expenditure from historical data, supporting planning by destinations, airlines, hotels, and policymakers. The field spans two broad model families. Time-serie...
Tourism Product Conjoint Analysis
Tourism product conjoint analysis is a decompositional preference-measurement technique that breaks travellers' overall judgments of holiday packages into the separate contributions, or part-worths, of each package attribute. Building on th...
Tourism Seasonality Index
Tourism seasonality measurement summarizes how unevenly tourism demand is distributed across the year. Destinations rarely receive visitors at a constant rate; arrivals, overnight stays, and revenue cluster in peak months and thin out in th...
Trimmed Mean Test
The trimmed mean test compares two groups using trimmed means, which discard a fixed proportion of the most extreme observations in each tail before averaging. Introduced by Karen K. Yuen in 1974, it is a robust alternative to the classical...
Two-Sample Kolmogorov-Smirnov Test
The two-sample Kolmogorov-Smirnov test is a nonparametric procedure that asks whether two independent groups are drawn from the same continuous distribution. Building on Smirnov's 1948 tables, it compares the empirical cumulative distributi...
Two-Way ANOVA
Two-Way ANOVA is a parametric hypothesis test that simultaneously examines the main effects of two independent categorical factors and their interaction effect on a single continuous dependent variable. The technique was developed within th...
U-MIDAS
U-MIDAS (Unrestricted MIDAS) is a regression framework designed to handle mixed-frequency data—when explanatory variables arrive at different sampling frequencies (e.g., monthly GDP mixed with daily stock returns). Introduced by Ghysels and...