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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
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statistics

Ordinary Least Squares

Ordinary Least Squares (OLS) is the canonical method for estimating the parameters of a linear regression model by minimizing the sum of squared differences between observed and predicted values. First published by Adrien-Marie Legendre in 1805 and independently developed by Carl Friedrich Gauss (who claimed priority f

4 个来源1805
economics

Overlapping Generations Model

The Overlapping Generations Model, pioneered by Paul Samuelson in 1958 and extended by Peter Diamond in 1965, is a macroeconomic framework where successive generations of individuals live for finite periods and coexist at any point in time. It addresses how consumption, savings, and capital accumulation evolve across g

2 个来源1958
research statistics

P-Value and Statistical Significance

The p-value is the probability of observing data as extreme as or more extreme than what was actually observed, assuming the null hypothesis is true. Introduced by Ronald Fisher in 1925, it is the foundation of frequentist hypothesis testing. Statistical significance is declared when the p-value falls below a pre-speci

3 个来源1925
statistics

Page's L Test

Page's L test is a nonparametric hypothesis test designed for repeated-measures (randomized complete block) designs in which the researcher has a specific, pre-stated ordering hypothesis across k ≥ 3 conditions. Introduced by Ellis Batten Page in 1963, it is more powerful than the Friedman test when the alternative hyp

2 个来源1963
statistics

Paired samples t-test

The paired samples t-test is a parametric hypothesis test that compares the means of two related measurements from the same subjects or matched pairs to determine whether the average difference is significantly different from zero. It leverages the dependency between observations to produce a more powerful test than it

2 个来源1908
statistics

Paired t-test

The paired samples t-test is a parametric hypothesis test that compares two measurements taken on the same subjects — such as a before and after reading — to decide whether the average change differs from zero. It rests on the t-distribution introduced by Student (W. S. Gosset) in 1908 and works on the within-subject d

2 个来源1908
finance

Pairs Trading

Pairs trading is a quantitative trading strategy that takes a long-short position on two cointegrated assets when the gap (spread) between their prices shows mean reversion. It was popularised as a relative-value arbitrage rule by Gatev, Goetzmann and Rouwenhorst (2006) and framed quantitatively by Vidyamurthy (2004).

2 个来源2006
econometrics

Panel ADF Unit Root Test

The Panel Augmented Dickey-Fuller (Panel ADF) unit root test extends the classical ADF framework to panel datasets. By pooling information across cross-sectional units it achieves substantially higher power than single-series ADF tests, allowing researchers to determine whether time-series variables are stationary or i

2 个来源2002
econometrics

Panel AR model

The Panel AR model extends the classical univariate autoregressive model to panel data, capturing how each unit's own past values predict its current value while controlling for unobserved individual heterogeneity through fixed or random effects. It is foundational for modelling dynamic persistence in micro or macro pa

2 个来源1980
econometrics

Panel ARDL Bounds Test

The Panel ARDL Bounds Test extends the Pesaran, Shin and Smith (2001) bounds testing procedure to panel data, allowing researchers to test for long-run cointegrating relationships between variables without requiring all series to be integrated of the same order. It is widely used in macro-panel studies where variables

2 个来源2001
econometrics

Panel Arellano-Bond GMM

The Arellano-Bond GMM estimator addresses the two core problems of dynamic panel models — individual fixed effects correlated with the regressors, and the endogeneity introduced by a lagged dependent variable — by first-differencing to remove fixed effects and then using lagged levels of the dependent variable as inter

2 个来源1991
econometrics

Panel ARIMA model

The Panel ARIMA model extends the classical Box-Jenkins ARIMA framework to panel data, fitting autoregressive integrated moving-average dynamics to multiple cross-sectional units observed over time. It accommodates unit-specific short-run dynamics and non-stationarity, making it suitable for forecasting and dynamic ana

2 个来源1970
econometrics

Panel ARMA model

The Panel ARMA model extends the classical Autoregressive Moving Average (ARMA) framework to panel data, allowing each cross-sectional unit to carry an individual effect while the within-unit error dynamics follow an ARMA(p, q) process. It captures both autocorrelation and moving-average dependence in panel residuals,

2 个来源1980
econometrics

Panel Cointegration Tests

Panel cointegration tests check whether a set of integrated variables share a stable long-run equilibrium relationship across a panel of cross-sectional units. Pedroni (1999, 2004) provides heterogeneous-panel tests with seven statistics, Kao (1999) gives an ADF-based homogeneous-panel test, and Westerlund (2007) adds

2 个来源2004
econometrics

Panel Data Analysis

Panel data analysis models data that track multiple units — countries, firms, individuals — over time, enabling researchers to control for unobserved unit-level heterogeneity that would otherwise bias cross-sectional or time-series estimates. The two core specifications are fixed effects and random effects, selected vi

2 个来源1966
causal inference

Panel Data Placebo Test

A panel data placebo test is a falsification procedure used to assess the credibility of causal estimates in quasi-experimental panel designs. By applying the same estimation strategy to a period, group, or outcome where no true effect should exist, researchers verify that the observed treatment effect is not merely an

2 个来源2004
econometrics

Panel DCC-GARCH

The Panel DCC-GARCH model extends Engle's (2002) Dynamic Conditional Correlation GARCH framework to panel data settings, jointly modelling time-varying volatility and cross-sectional correlations across multiple units (countries, firms, or assets) over time. It allows pairwise correlations to vary dynamically in respon

2 个来源2002
econometrics

Panel DF-GLS

Panel DF-GLS extends the Elliott, Rothenberg, and Stock (1996) GLS unit-root test to panel data, combining cross-sectional and time-series information to test whether variables contain unit roots. Introduced by Hadri and colleagues (2005), it is more powerful than standard panel unit-root tests (IPS, LLC) due to its GL

2 个来源1996
econometrics

Panel Dynamic Panel Data Model

The dynamic panel data model extends standard panel regression by including one or more lagged values of the outcome variable as regressors. Because past outcomes directly predict current outcomes, the model captures persistence and adjustment dynamics — but it also introduces a correlation between the lagged dependent

2 个来源1991
econometrics

Panel EGARCH

Panel EGARCH extends Nelson's (1991) Exponential GARCH model to a panel setting, allowing conditional variance to evolve asymmetrically over time for each cross-sectional unit. The log specification ensures non-negative variance without parameter constraints, and the leverage term distinguishes whether negative shocks

2 个来源1991
econometrics

Panel Engle-Granger Cointegration

The Panel Engle-Granger cointegration test extends the classic two-step Engle-Granger procedure to panel data, allowing researchers to detect long-run equilibrium relationships among integrated variables across multiple cross-sectional units simultaneously. Pedroni (1999) developed panel statistics that pool informatio

2 个来源1999
econometrics

Panel Fixed Effects

The Panel Data Fixed Effects model estimates relationships from panel data (the same units observed over several time periods) while controlling for unit- and/or time-specific effects, supporting causal inference. It is developed as the within estimator in standard treatments such as Hsiao's Analysis of Panel Data (201

2 个来源2014
econometrics

Panel Fixed Effects Model

The panel fixed effects (FE) model controls for all time-invariant, unit-specific unobserved heterogeneity by absorbing it into individual intercepts. By sweeping out unit means through the within transformation, FE yields unbiased estimates of the effect of time-varying regressors even when omitted unit-level confound

2 个来源1978
econometrics

Panel GARCH model

The Panel GARCH model extends Bollerslev's (1986) Generalized Autoregressive Conditional Heteroscedasticity framework to panel data, allowing conditional variance to evolve over time for each cross-sectional unit. It simultaneously captures unit-level heterogeneity and time-varying volatility clustering, making it the

2 个来源1986
spatial analysis

Panel Geary's C

Panel Geary's C extends the classic Geary contiguity ratio to panel datasets, measuring spatial autocorrelation across georeferenced units (regions, cities, countries) observed over multiple time periods. It detects whether neighboring units tend to have similar values, pooling or averaging evidence across the temporal

2 个来源1954
spatial analysis

Panel Geographically Weighted Regression

Panel Geographically Weighted Regression (Panel GWR) extends the standard GWR framework to panel data, allowing regression coefficients to vary both across geographic locations and over time. It captures spatially non-stationary relationships in longitudinal or repeated-measures spatial datasets, combining local spatia

2 个来源2000
econometrics

Panel GLS

Panel GLS is a regression method for longitudinal data that explicitly models the non-spherical error structure — heteroscedasticity across units and serial correlation within units — to recover efficient coefficient estimates. Unlike OLS, it weights observations by the inverse of the error covariance matrix, yielding

2 个来源1935
econometrics

Panel Granger Causality

The Panel Granger Causality test examines whether past values of one variable help predict another variable across multiple cross-sectional units observed over time. It extends the classical Granger causality framework to panel data, accounting for cross-sectional heterogeneity and enabling more powerful inference by p

2 个来源1988
econometrics

Panel Hausman Test

The Hausman specification test for panel data determines whether individual-specific effects are correlated with the regressors — a correlation that would make the random effects estimator inconsistent. A statistically significant result favours the fixed effects model; a non-significant result supports the more effici

2 个来源1978
spatial analysis

Panel Hot Spot Analysis

Panel Hot Spot Analysis applies hot spot detection — typically via the Getis-Ord Gi* statistic — repeatedly across multiple time periods on the same spatial units, enabling researchers to track where clusters of high or low values persist, emerge, or dissolve over time. It bridges cross-sectional spatial statistics wit

2 个来源1992
econometrics

Panel Johansen Cointegration

The Panel Johansen cointegration test extends Johansen's maximum-likelihood framework to panel data, allowing researchers to test whether multiple non-stationary variables share long-run equilibrium relationships across cross-sectional units. It pools the likelihood-ratio statistics from individual Johansen tests and c

2 个来源2001
spatial analysis

Panel Kernel Density Estimation

Panel Kernel Density Estimation (Panel KDE) extends the standard kernel density estimator to panel (longitudinal) data, estimating smooth density surfaces for spatial or attribute variables observed across multiple units and time periods. It reveals how the distribution of a phenomenon shifts, concentrates, or disperse

2 个来源1962
econometrics

Panel KPSS test

The Panel KPSS test, introduced by Hadri (2000), tests the null hypothesis that all series in a panel are stationary against the alternative that some or all contain a unit root. It extends the univariate KPSS framework to panel data by aggregating individual LM statistics, providing higher power than unit-root tests w

2 个来源2000
spatial analysis

Panel Kriging

Panel Kriging is a geostatistical interpolation method that combines kriging's spatial prediction framework with a panel (longitudinal) data structure. It estimates unknown values at unobserved locations and times by borrowing strength from repeated spatial observations across multiple time periods, accounting for both

2 个来源2011
econometrics

Panel KSS

The Panel KSS test reverses the null hypothesis of unit-root tests: it tests whether variables are stationary (stationarity is the null) versus nonstationary (unit root is the alternative). Introduced by Kwiatkowski et al. (1992) and extended to panels by Hadri (2000), this complementary approach provides robustness wh

2 个来源1992
spatial analysis

Panel Local Indicators of Spatial Association

Panel Local Indicators of Spatial Association extends Anselin's LISA statistics — most commonly Local Moran's I — to panel datasets, identifying spatial clusters and outliers at each location across multiple time periods. By applying local autocorrelation measures repeatedly over time, researchers can detect whether sp

2 个来源1995
spatial analysis

Panel Multiscale Geographically Weighted Regression

Panel MGWR extends Multiscale Geographically Weighted Regression to repeated-observations (panel) data, allowing each predictor to operate at its own spatial bandwidth while controlling for unit-specific or time-specific fixed effects. It is used when both spatial heterogeneity and temporal structure matter simultaneou

2 个来源2017
econometrics

Panel NARDL

Panel NARDL extends the time-series NARDL framework of Shin, Yu and Greenwood-Nimmo (2014) to a panel data setting, allowing researchers to detect asymmetric long-run and short-run relationships between variables across multiple cross-sections simultaneously. By decomposing the regressor into positive and negative part

2 个来源2014
spatial analysis

Panel Network-Based Spatial Analysis

Panel Network-Based Spatial Analysis extends standard spatial econometric models to repeated-measures (panel) data by representing spatial dependence through network connectivity rather than simple geographic proximity. It captures how units connected in a network influence each other's outcomes over time, while contro

2 个来源2000
econometrics

Panel OLS

Panel OLS — also called Pooled OLS — applies the classical ordinary least squares estimator to panel data by stacking all cross-sectional units and time periods into a single sample. It estimates one common set of slope coefficients under the assumption that the intercept and slopes are homogeneous across units and tim

2 个来源1986
spatial analysis

Panel Ordinary Kriging

Panel Ordinary Kriging extends the classical geostatistical interpolation method — Ordinary Kriging — to panel (longitudinal) datasets where the same set of spatial locations is observed repeatedly over multiple time periods. It produces optimal linear unbiased predictions at unsampled locations for each time slice, ac

2 个来源1963
econometrics

Panel PP unit root test

The Panel PP unit root test extends the nonparametric Phillips-Perron correction for serial correlation to a multi-individual panel setting. It tests the null hypothesis that all cross-sectional units contain a unit root, using a pooled or averaged PP-type statistic that is robust to heteroscedastic and serially correl

2 个来源1988
econometrics

Panel Quantile-on-Quantile Regression

Panel quantile-on-quantile (QQ) regression jointly maps any quantile of the outcome distribution onto any quantile of the predictor distribution across multiple cross-sectional units observed over time. It generalises Sim and Zhou's (2015) cross-sectional QQ framework to a panel setting, revealing a full dependence sur

2 个来源2015
econometrics

Panel Random Effects Model

The panel random effects (RE) model treats individual-specific effects as random draws from a population distribution rather than fixed constants, enabling efficient estimation by generalised least squares and allowing inference about time-invariant regressors that are swept away in fixed effects estimation.

2 个来源1966
econometrics

Panel SARIMA model

The Panel SARIMA model applies the Seasonal Autoregressive Integrated Moving Average (SARIMA) framework to panel data, fitting individual or pooled seasonal time series models across multiple cross-sectional units. It captures both non-seasonal and seasonal autocorrelation, trends, and periodicity, making it suitable f

2 个来源1976
statistics

Panel Simple Linear Regression

Panel simple linear regression models a continuous outcome as a linear function of a single predictor using data that track the same entities (individuals, firms, countries) across multiple time periods. It separates within-entity variation from between-entity variation, enabling control for unobserved time-invariant c

2 个来源1986
econometrics

Panel Smooth Transition Regression

Panel Smooth Transition Regression (PSTR) models nonlinear panel relationships where coefficients transition smoothly (rather than abruptly) between regimes as a transition variable crosses thresholds. Introduced by Gonzalez et al. (2005), it extends univariate smooth-transition autoregression (STAR) models to panels,

2 个来源2005
spatial analysis

Panel Spatial Autocorrelation

Panel Spatial Autocorrelation measures whether observations that are geographically close also tend to have similar values across repeated time periods. It extends classic cross-sectional spatial autocorrelation statistics such as Moran's I to panel data, enabling researchers to detect spatial dependence consistently o

2 个来源1988
spatial analysis

Panel Spatial Durbin Model

The Panel Spatial Durbin Model (PSDM) extends the cross-sectional Spatial Durbin Model to panel data, capturing both spatial lag dependence in the outcome and spatial spillovers from neighbouring units' explanatory variables across multiple time periods. It simultaneously accounts for unobserved unit-specific and time-

2 个来源2009
spatial analysis

Panel Spatial Error Model

The Panel Spatial Error Model (panel SEM) extends the classical spatial error model to panel data, allowing spatial dependence to enter through the error term across cross-sectional units over multiple time periods. It accounts for spatially correlated omitted variables without imposing a substantive spatial spillover

2 个来源1988
spatial analysis

Panel Spatial Regression

Panel Spatial Regression extends standard panel data models by explicitly accounting for spatial dependence among cross-sectional units observed over time. It combines the temporal control of panel fixed or random effects with a spatial weights matrix that encodes geographic or network proximity, yielding unbiased and

2 个来源1988
econometrics

Panel SVAR model

The Panel SVAR model extends the Structural VAR framework to panel data, jointly modelling multiple endogenous time-series variables across several cross-sectional units (e.g., countries or firms). Structural restrictions — short-run, long-run, or sign restrictions — are imposed on the contemporaneous relationships amo

2 个来源2004
econometrics

Panel System GMM

Panel System GMM is a two-equation GMM estimator for dynamic panel data that stacks the differenced equation (using lagged levels as instruments) with the levels equation (using lagged differences as instruments). Developed by Blundell and Bond (1998) on the foundation of Arellano and Bover (1995), it is the preferred

2 个来源1998
econometrics

Panel TGARCH

Panel TGARCH extends the Threshold GARCH (GJR-GARCH) model to panel data, allowing each cross-sectional unit to exhibit asymmetric volatility responses — where negative shocks generate larger variance increases than positive shocks of the same magnitude — while exploiting the cross-sectional dimension to obtain more ef

2 个来源1993
econometrics

Panel Toda-Yamamoto Causality

The Panel Toda-Yamamoto (PTY) causality test extends the Toda-Yamamoto modified Wald approach to panel data, allowing researchers to test Granger non-causality across multiple cross-sectional units without requiring pre-testing for cointegration or imposing a common causality direction on all units.

2 个来源1995
spatial analysis

Panel Universal Kriging

Panel Universal Kriging extends Universal Kriging to data structures with repeated spatial observations over time (panel or longitudinal format). It simultaneously estimates a deterministic trend surface — incorporating covariates that vary across both space and time — and a stochastic spatially correlated residual, po

2 个来源1963
econometrics

Panel VAR

Panel VAR extends the vector autoregression model to panel data, modelling the dynamic interactions among several variables while controlling for cross-unit heterogeneity through fixed effects. It was introduced by Holtz-Eakin, Newey and Rosen in 1988 and produces impulse-response functions and variance decompositions

2 个来源1988
econometrics

Panel VARX

Panel VARX extends vector autoregression to heterogeneous panels with exogenous variables, enabling simultaneous modeling of multiple endogenous variables alongside observed external factors across many units. Introduced by Holtz-Eakin et al. (1988) and advanced by Canova and Ciccarelli (2013), it captures dynamic rela

2 个来源2013
econometrics

Panel VECM

Panel VECM combines vector error correction modelling with panel data, simultaneously capturing the long-run cointegrating equilibrium among multiple I(1) variables and their short-run adjustment dynamics across multiple cross-sectional units. It is the standard framework when panel variables share at least one common

2 个来源1987
econometrics

Panel Zivot-Andrews test

The Panel Zivot-Andrews test extends the single-series Zivot-Andrews (1992) structural break unit root test to panel data, allowing each cross-sectional unit to have its own endogenously determined break date. It tests the null of a unit root against the alternative of stationarity with a one-time structural break, acc

2 个来源1992
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