Statistics — Page 29
Explore statistical methods, their assumptions, interpretation and limitations.
Research field
Method family
NBD-Dirichlet Model
The NBD-Dirichlet model is the canonical stochastic model of repeat buying and brand choice in stationary, competitive consumer-goods markets. Introduced by Gerald Goodhardt, Andrew Ehrenberg and Christopher Chatfield in their 1984 Journal...
NOMINATE
NOMINATE — Nominal Three-step Estimation — is the family of spatial scaling procedures developed by Keith Poole and Howard Rosenthal to recover legislators' ideological positions from roll-call votes. Each legislator and the yea and nay out...
Negative Binomial Regression
Negative Binomial Regression is a generalized linear model for count outcomes that extends Poisson regression to handle overdispersion, where the variance of the counts exceeds their mean. Developed in the GLM tradition and treated in depth...
Nelson-Aalen Estimator
The Nelson-Aalen estimator is a non-parametric estimator of the cumulative hazard function from right-censored time-to-event data. Developed by Wayne Nelson for reliability hazard plotting in 1972 and placed on a rigorous counting-process f...
Nemenyi Test
The Nemenyi test is a nonparametric post-hoc multiple comparison procedure introduced by Peter Nemenyi in his 1963 Princeton doctoral thesis. It is applied after a significant Friedman test to identify which specific pairs of conditions dif...
Nested Logit
The Nested Logit model is a discrete choice framework that groups mutually exclusive alternatives into hierarchical nests, allowing correlated unobserved utilities within each nest while maintaining independence across nests. Introduced for...
Nested Logit Brand Choice
The nested logit model of brand choice relaxes the restrictive independence-of-irrelevant-alternatives (IIA) assumption of the standard multinomial logit by grouping similar alternatives into nests. Developed by Daniel McFadden as a member...
Network Econometrics
Network econometrics estimates how individuals' outcomes are causally shaped by the behaviour and characteristics of their social-network neighbours. Formalised by Bramoullé, Djebbari, and Fortin (2009), the framework embeds a row-normalise...
Network-Based Spatial Analysis
Network-based spatial analysis (NBSA) analyzes the distribution and interaction of spatial phenomena constrained to a network structure — such as roads, railways, or rivers — using network distance rather than straight-line (Euclidean) dist...
New Economics of Labor Migration Test
The new economics of labor migration (NELM), launched by Oded Stark and David Bloom in 1985, recasts migration as a decision made by households rather than isolated individuals and as a strategy aimed at managing risk and relative standing...
Newey-West HAC
Newey-West HAC standard errors, introduced by Whitney Newey and Kenneth West in 1987, provide a covariance matrix estimator for OLS regression that remains valid under both heteroskedasticity and serial autocorrelation of unknown form. They...
No-U-Turn Sampler
The No-U-Turn Sampler (NUTS) is a self-tuning Markov chain Monte Carlo algorithm introduced by Hoffman and Gelman (2014) that extends Hamiltonian Monte Carlo (HMC) by automatically determining the optimal number of leapfrog steps, eliminati...
Nonlinear ADF Unit Root Test
The Nonlinear ADF unit root test, most prominently operationalized by Kapetanios, Shin, and Snell (2003), extends the classical Augmented Dickey-Fuller test to detect mean reversion that occurs via an Exponential Smooth Transition Autoregre...
Nonlinear AR Model
The Nonlinear AR model extends the classical autoregressive framework by allowing the mapping from past values to the current value to follow an arbitrary or regime-switching nonlinear function. Major families include the Self-Exciting Thre...
Nonlinear ARCH model
The Nonlinear ARCH (NARCH) model, introduced by Higgins and Bera (1992), extends Engle's original ARCH framework by allowing the power transformation of volatility to be estimated from the data rather than fixed at two. This flexibility cap...
Nonlinear ARDL
The Nonlinear ARDL (NARDL) model extends the linear ARDL bounds-testing framework to allow asymmetric long-run and short-run relationships. By decomposing the regressor into cumulative positive and negative partial sums, it tests whether in...
Nonlinear ARDL bounds test
The Nonlinear ARDL bounds test, developed by Shin, Yu, and Greenwood-Nimmo (2014), extends the linear ARDL framework to detect asymmetric long-run relationships in time series. By decomposing a regressor into positive and negative partial s...
Nonlinear ARIMA model
The Nonlinear ARIMA model extends the classical Box-Jenkins ARIMA framework by allowing the conditional mean of a time series to depend on past values and past errors through a nonlinear function. It encompasses families such as Threshold A...
Nonlinear ARMA model
The Nonlinear ARMA (NARMA) model extends the classical linear ARMA framework by allowing the conditional mean to depend on past observations and past errors through an arbitrary nonlinear function. It captures complex dynamics — such as reg...
Nonlinear Arellano-Bond GMM
Nonlinear Arellano-Bond GMM extends the classic Arellano-Bond difference-GMM framework to panel models where the conditional mean function is nonlinear in parameters or variables. It uses lagged levels of the dependent variable as instrumen...
Nonlinear DCC-GARCH model
The Nonlinear DCC-GARCH model extends Engle's (2002) Dynamic Conditional Correlation framework by allowing correlations to respond asymmetrically to negative versus positive return shocks. Proposed by Cappiello, Engle, and Sheppard (2006),...
Nonlinear Dynamic Panel Data Model
The nonlinear dynamic panel data model extends standard panel methods to settings where the outcome is binary, count-valued, or censored and where past realizations of the outcome directly affect current ones. It handles unobserved individu...
Nonlinear EGARCH model
The Nonlinear EGARCH model extends Nelson's (1991) Exponential GARCH by allowing the news impact function to take a flexible nonlinear form, capturing asymmetric and nonlinear responses of conditional volatility to past shocks. It is widely...
Nonlinear Engle-Granger Cointegration
Nonlinear Engle-Granger cointegration extends the classical two-step Engle-Granger procedure to detect long-run equilibria where adjustment toward the equilibrium is nonlinear — for example, faster above than below a threshold, or governed...