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以内容为本的研究方法参考文库——每种方法是什么、如何运作、源自何处。

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方法统计学1,836人工智能与机器学习1,661决策科学932研究方法1,354测量1,745因果与证据532研究实践118
1,411 种方法 · 统计学清除
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econometrics

Three-Stage Least Squares

Three-Stage Least Squares is a system estimator for simultaneous-equation models that accounts for the correlation of error terms across equations. Introduced by Zellner and Theil in 1962, it combines two-stage least squares with the seemingly-unrelated-regression idea to estimate all equations jointly and more efficie

1 个来源1962
econometrics

Threshold and Smooth-Transition VAR

Threshold VAR and Smooth-Transition VAR are nonlinear multivariate time-series models in which the coefficients of a vector autoregression switch between regimes according to a threshold variable. Building on Tsay's 1998 treatment of multivariate threshold models, they capture different dynamic structures across phases

2 个来源1998
econometrics

Threshold Panel VAR

The Threshold Panel VAR extends the standard vector autoregression framework to accommodate regime-switching behavior where relationships change when a threshold variable crosses a critical level. Introduced by Hansen (1996) and applied to panels by Caner and Hansen (2001), it allows different dynamic relationships acr

2 个来源1996
statistics

Thurstone Scaling

Thurstone Scaling, formally the Law of Comparative Judgment, is a psychometric model introduced by Louis Leon Thurstone in 1927 for deriving interval-level scale values from pairwise comparison data. By assuming that each stimulus evokes a normally distributed discriminal process on a psychological continuum, the metho

1 个来源1927
bayesian

Time series approximate Bayesian computation

Time series ABC is a likelihood-free Bayesian inference method that estimates the posterior distribution of model parameters for dynamical or time-indexed systems by comparing summary statistics of simulated trajectories to those of the observed series, bypassing the need to evaluate an analytic likelihood. It is parti

2 个来源2009
bayesian

Time series Bayesian hierarchical model

A time series Bayesian hierarchical model combines the hierarchical (multilevel) Bayesian framework with a dynamic state-space structure to analyse temporal data collected on multiple units or groups. Priors encode beliefs about both within-unit dynamics and cross-unit variation, and the posterior is obtained via MCMC

2 个来源1989
bayesian

Time series Bayesian inference

Time series Bayesian inference applies Bayes' theorem sequentially to time-ordered observations, maintaining a full probability distribution over hidden states and model parameters at every time step. This framework unifies state-space models, dynamic linear models, and particle filters, producing calibrated uncertaint

2 个来源1989
bayesian

Time series Bayesian model averaging

Time series Bayesian model averaging (TS-BMA) combines forecasts from an ensemble of time series models — such as AR, VAR, or state-space specifications — by weighting each model by its posterior probability given observed data. Rather than selecting one model and discarding uncertainty about which model is best, TS-BM

2 个来源1999
bayesian

Time Series Kalman Filter

The time series Kalman filter applies the Kalman filtering and smoothing algorithm within a state-space representation of time series models. It recursively extracts unobserved components — trend, seasonality, cycles, and irregular noise — from observed data, providing optimal filtered and smoothed state estimates toge

2 个来源1960
bayesian

Time series MCMC

Time series MCMC applies Markov chain Monte Carlo methods to Bayesian inference over time-ordered data. Rather than optimising a single parameter estimate, it draws samples from the full joint posterior of parameters and latent states, yielding probability distributions that honestly reflect uncertainty about dynamics,

2 个来源1994
bayesian

Time series particle filter

The time series particle filter is a Sequential Monte Carlo method that tracks the hidden state of a nonlinear, non-Gaussian state-space model as new observations arrive one at a time. It represents the evolving posterior distribution over the latent state as a weighted cloud of random samples (particles), updating the

2 个来源1993
bayesian

Time series sequential Monte Carlo

Time series sequential Monte Carlo (SMC), commonly called the particle filter, is a Bayesian simulation method that tracks the hidden state of a dynamical system as observations arrive one at a time. A cloud of weighted random samples — particles — is propagated forward through the system dynamics, reweighted by how we

2 个来源1993
bayesian

Time series variational inference

Time series variational inference applies variational Bayes to sequential data, approximating the intractable posterior over latent states and parameters with a tractable family of distributions. By maximising the evidence lower bound (ELBO), it delivers fast, scalable Bayesian inference for state-space models, dynamic

2 个来源1999
survival

Time-Dependent Cox Regression

Time-dependent Cox regression is an extension of the standard Cox proportional hazards model, introduced through the counting-process formulation developed by Therneau and Grambsch (2000), that allows one or more predictor variables to take different values at different points in a subject's follow-up period. It is the

1 个来源1972
econometrics

Time-Series Cross-Validation

Time-series cross-validation is a resampling procedure designed for sequentially ordered data. Instead of randomly partitioning observations — which would destroy temporal structure and introduce data leakage — it advances a forecast origin one step at a time, fitting a model on all past data up to that origin and eval

1 个来源2012
econometrics

Time-varying parameter ADF unit root test

The time-varying parameter ADF (TVP-ADF) test extends the classical Augmented Dickey-Fuller framework by allowing the autoregressive coefficient to evolve over time. Rather than assuming a single fixed unit-root parameter throughout the sample, it models the persistence of a series as a stochastic process, making it se

2 个来源1990
econometrics

Time-varying parameter AR model

The Time-Varying Parameter Autoregressive (TVP-AR) model extends the classical AR model by allowing its autoregressive coefficients to drift over time, typically as a random walk. Cast as a state-space system, the model captures gradual structural change in the dynamics of a univariate time series without imposing a fi

2 个来源1976
econometrics

Time-varying parameter ARCH model

The Time-Varying Parameter ARCH (TVP-ARCH) model extends the classic ARCH framework by allowing both the conditional mean coefficients and the ARCH variance parameters to drift over time according to a random-walk or state-space process. This makes it possible to capture structural shifts in volatility dynamics without

2 个来源1980
econometrics

Time-varying parameter ARDL bounds test

The time-varying parameter ARDL bounds test extends the classic Pesaran-Shin-Smith (2001) bounds testing framework by allowing regression coefficients to evolve continuously over time. It detects whether a long-run cointegrating relationship between variables exists and whether that relationship has been stable or shif

2 个来源2010
econometrics

Time-varying parameter Arellano-Bond GMM

The time-varying parameter Arellano-Bond GMM (TVP-AB GMM) is a dynamic panel estimator that extends the classic Arellano-Bond difference GMM framework by allowing regression coefficients to evolve over time. It addresses both individual fixed effects and the endogeneity of lagged dependent variables, while accommodatin

2 个来源1990
econometrics

Time-varying parameter ARIMA model

The time-varying parameter ARIMA model extends the classical ARIMA framework by allowing its autoregressive and moving-average coefficients to evolve over time rather than remaining fixed. Cast in state-space form and estimated via the Kalman filter, it is designed for economic and financial time series whose dynamic s

2 个来源1976
econometrics

Time-varying parameter ARMA model

The time-varying parameter ARMA (TVP-ARMA) model extends the classical ARMA framework by allowing the autoregressive and moving-average coefficients to evolve over time. Embedded in a state-space representation and estimated via the Kalman filter, it captures structural change and parameter instability in time series w

2 个来源1976
econometrics

Time-varying parameter DCC-GARCH model

The TVP-DCC-GARCH model extends the Dynamic Conditional Correlation GARCH framework by allowing not only the pairwise correlations but also the underlying model parameters to evolve continuously over time. It captures structural shifts in volatility dynamics and cross-asset dependence, making it essential for financial

2 个来源2002
econometrics

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 endogeneity and lagged dependent variables while relaxing the assumption that structural r

2 个来源2000
econometrics

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 periods, making it well-suited for studying structural change, heterogeneous adjus

2 个来源1990
econometrics

Time-varying parameter EGARCH model

The TVP-EGARCH model extends Nelson's (1991) Exponential GARCH by allowing the volatility equation's parameters — including the leverage effect coefficient — to drift continuously over time. This makes it possible to capture structural change and regime evolution in financial return volatility without imposing a fixed

2 个来源1991
econometrics

Time-varying parameter Engle-Granger cointegration

Time-varying parameter (TVP) Engle-Granger cointegration extends the classical two-step Engle-Granger framework by allowing the long-run relationship between integrated series to evolve over time. Instead of assuming a fixed cointegrating vector, the cointegrating coefficients are modelled as stochastic processes — typ

2 个来源1987
econometrics

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 heterogeneity. It is used when the effect of a predictor on an outcome is not constant acr

2 个来源1975
econometrics

Time-varying parameter GARCH model

The Time-Varying Parameter GARCH model extends the standard GARCH framework by allowing the conditional variance parameters — including the ARCH and GARCH coefficients — to change over time rather than remaining fixed throughout the sample. This makes it well-suited to financial and macroeconomic series where volatilit

2 个来源1982
econometrics

Time-varying parameter GLS

Time-varying parameter GLS extends generalized least squares to settings where regression coefficients are not fixed constants but evolve over time according to a stochastic process. By embedding the model in a state-space framework and applying GLS corrections for non-spherical errors, it captures structural change, r

2 个来源1976
econometrics

Time-varying parameter Granger causality

Time-varying parameter Granger causality extends the classical Granger causality framework by allowing the predictive relationships between time series to evolve across time. Instead of assuming fixed causal effects, the model estimates causal coefficients that can shift, capturing structural breaks, regime changes, or

2 个来源1969
econometrics

Time-varying parameter Hausman test

The time-varying parameter Hausman test extends Hausman's (1978) classic specification test to models whose coefficients are allowed to evolve over time. It compares an efficient estimator (e.g., OLS or GLS assuming constant parameters) with a consistent estimator from a time-varying parameter model, using the contrast

2 个来源1978
econometrics

Time-varying parameter Johansen cointegration

Time-varying parameter (TVP) Johansen cointegration extends the classic Johansen framework by allowing the cointegrating vectors and adjustment speeds to evolve over time. It is designed for integrated multivariate time series whose long-run equilibrium relationships are subject to structural change, regime shifts, or

2 个来源1999
econometrics

Time-varying parameter KPSS test

The time-varying parameter KPSS test extends the classic Kwiatkowski-Phillips-Schmidt-Shin (1992) stationarity test to settings where the deterministic or stochastic components of a series may shift over time. It tests the null hypothesis of stationarity while allowing the model's parameters to evolve, making it robust

2 个来源2000
econometrics

Time-varying parameter MA model

The time-varying parameter moving average (TVP-MA) model extends the standard MA model by allowing the moving-average coefficients to change over time. Cast as a state-space system, it is estimated via the Kalman filter and smoother, making it well suited for series where the shock-transmission dynamics evolve across t

2 个来源1990
econometrics

Time-varying parameter NARDL

The Time-Varying Parameter NARDL (TVP-NARDL) model extends the Nonlinear ARDL framework by allowing the coefficients on positive and negative partial sums of a regressor to change over time. This combination captures both asymmetric responses and structural instability in long-run and short-run relationships within a s

2 个来源2019
econometrics

Time-varying parameter OLS

Time-Varying Parameter OLS extends classical ordinary least squares to allow regression coefficients to change over time. Instead of assuming fixed slopes throughout the sample, the model treats each coefficient as a stochastic process, tracking how economic relationships evolve — making it well-suited for analysing st

2 个来源1976
econometrics

Time-varying Parameter Panel Data Analysis

Time-varying parameter (TVP) panel data analysis extends standard panel regression by allowing the slope coefficients to evolve over time for each unit. Instead of assuming a single fixed or random coefficient, the model lets each unit's relationship between predictors and outcome shift period by period, capturing stru

2 个来源1960
econometrics

Time-varying parameter PP unit root test

The time-varying parameter PP unit root test extends the classical Phillips-Perron test by allowing the autoregressive coefficient to change over time. It detects stochastic non-stationarity in series whose persistence may shift across regimes or periods, offering more reliable inference when structural change is suspe

2 个来源1988
econometrics

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 distribution and across different time periods, uncovering dynamic, heterogeneous de

2 个来源2015
econometrics

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 periods, each coefficient is treated as a random draw that evolves, capturing gen

2 个来源1970
econometrics

Time-varying parameter SARIMA model

The Time-Varying Parameter SARIMA model extends the classical SARIMA framework by allowing autoregressive and moving-average coefficients to evolve over time. Cast as a state-space system and estimated with the Kalman filter, it captures both seasonal patterns and structural change within a single unified model.

2 个来源1990
econometrics

Time-varying parameter SVAR model

The Time-Varying Parameter Structural VAR (TVP-SVAR) model extends classical structural VARs by allowing both the reduced-form coefficients and the structural impact matrix to evolve continuously over time. Estimated via Bayesian MCMC, it captures shifting transmission mechanisms and heteroscedastic volatility — making

2 个来源2005
econometrics

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 time-varying coefficient structure, the method captures both the persistence of the

2 个来源1998
econometrics

Time-varying parameter TGARCH model

The TVP-TGARCH model extends Threshold GARCH by allowing its volatility parameters to evolve over time via a state-space representation. It captures both the leverage effect — that negative return shocks increase volatility more than positive ones — and structural change in that asymmetry, making it well-suited for lon

2 个来源1990
econometrics

Time-varying parameter Toda-Yamamoto causality

The TVP Toda-Yamamoto causality test combines Toda and Yamamoto's (1995) augmented VAR approach — which handles possibly integrated or cointegrated series without pre-testing for unit roots — with time-varying parameters, allowing causal relationships between variables to shift across different periods rather than rema

2 个来源1995
econometrics

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 dynamic relationships between macroeconomic or financial variables shift across

2 个来源2005
econometrics

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 integrated series while accommodating structural change, evolving policy regimes,

2 个来源1999
econometrics

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 data. It combines the flexibility of state-space coefficient evolution with the

2 个来源1976
econometrics

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, this approach lets the autoregressive dynamics and break timing adapt through

2 个来源1992
econometrics

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 data such as spending, wages, or duration.

1 个来源1958
econometrics

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 arbitrary order. By intentionally over-fitting the VAR with extra lags equal to the m

1 个来源1995
econometrics

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 VAR with extra lags equal to the maximum integration order, it restores the stan

2 个来源1995
economics

Travel Cost Method

The Travel Cost Method (TCM), developed by Harold Hotelling in 1949 and formalized by Marion Clawson and Jack Knetsch in the 1960s, is an econometric approach for valuing recreational sites and environmental amenities by inferring value from the travel costs (transportation, time, entry fees) that people incur to visit

3 个来源1949
statistics

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 t-test when the data are non-normal or contain outliers and the population vari

2 个来源1974
econometrics

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 'common monetary policy shock') from high-dimensional data while allowing VAR co

2 个来源2005
econometrics

TVP-VAR

TVP-VAR is a Bayesian multivariate time-series model in which both the VAR coefficients and the shock covariance matrix are allowed to evolve continuously over time as random walks. Introduced by Primiceri (2005) to study U.S. monetary policy transmission, the model captures structural changes and regime shifts without

1 个来源2005
statistics

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 distribution functions (CDFs) of the two samples and uses their maximum absolute distance

2 个来源1948
statistics

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 the broader framework of the analysis of variance established by Ronald A. Fisher

1 个来源intermediate1925
research statistics

Type I and Type II Errors

In hypothesis testing, two types of errors can occur: Type I error (false positive, rejecting a true null hypothesis) and Type II error (false negative, failing to reject a false null hypothesis). Formalized by Neyman and Pearson (1933), these errors are at the heart of statistical decision-making. The probability of T

3 个来源1933
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