ScholarGate
Asistents

Salīdzināt metodes

Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.

Parastā mazāko kvadrātu (OLS) regresija×Kvantīļu regresija×Mūsdienu pārejas autoregresijas (STAR) modelis×
NozareEkonometrijaEkonometrijaEkonometrija
SaimeRegression modelRegression modelRegression model
Izcelsmes gads201919781994
AutorsWooldridge (textbook treatment); classical least squaresKoenker & BassettTeräsvirta (1994); van Dijk, Teräsvirta & Franses (2002)
TipsLinear regressionConditional quantile regressionNonlinear time-series regime-switching model
PirmavotsWooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50. DOI ↗Teräsvirta, T. (1994). Specification, Estimation, and Evaluation of Smooth Transition Autoregressive Models. Journal of the American Statistical Association, 89(425), 208–218. DOI ↗
Citi nosaukumiordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonuconditional quantile regression, regression quantiles, Kantil Regresyonsmooth transition autoregressive model, LSTAR, ESTAR, logistic STAR
Saistītās554
KopsavilkumsOrdinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).Quantile regression models conditional quantiles of an outcome - the median, the 25th or 75th percentile, and so on - rather than the conditional mean that OLS targets. Introduced by Koenker and Bassett in 1978, it reveals how predictors act across the whole distribution, including its tails.The Smooth Transition Autoregressive (STAR) model is a nonlinear time-series model, developed in Teräsvirta's 1994 framework, that lets the dynamics move smoothly rather than abruptly between two regimes. The logistic variant (LSTAR) captures asymmetric business cycles and the exponential variant (ESTAR) captures purchasing-power-parity deviations.
ScholarGateDatu kopa
  1. v1
  2. 1 Avoti
  3. PUBLISHED
  1. v1
  2. 2 Avoti
  3. PUBLISHED
  1. v1
  2. 2 Avoti
  3. PUBLISHED

Doties uz meklēšanu Lejupielādēt slaidus

ScholarGateSalīdzināt metodes: OLS Regression · Quantile Regression · STAR Model. Izgūts 2026-06-18 no https://scholargate.app/lv/compare