Порівняння методів

	Модель бар'єру для даних підрахунку	Регресія негативним біноміальним розподілом	Регресія звичайно найменших квадратів (ЗНК)
Галузь	Статистика	Економетрика	Економетрика
Родина	Regression model	Regression model	Regression model
Рік появи	1986	2011	2019
Автор методу	Mullahy	Hilbe (textbook treatment); generalized linear model framework	Wooldridge (textbook treatment); classical least squares
Тип	Two-part count model	Generalized linear model for count data	Linear regression
Основоположне джерело	Mullahy, J. (1986). Specification and Testing of Some Modified Count Data Models. Journal of Econometrics, 33(3), 341–365.	Hilbe, J. M. (2011). Negative Binomial Regression (2nd ed.). Cambridge University Press.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Інші назви	hurdle count model, two-part count model, zero-truncated count model, Engel Modeli (Hurdle Model)	NB regression, NB2 regression, negatif binom regresyonu	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Пов'язані	5	4	5
Підсумок	The hurdle model is a two-part count-data model introduced by Mullahy (1986). A first stage models the binary choice of crossing a hurdle (a zero versus a non-zero count), and a second stage models the strictly positive counts with a zero-truncated distribution such as a zero-truncated Poisson or negative binomial.	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 by Hilbe (2011), it adds a dispersion parameter so that inference stays valid when Poisson would understate the spread of the data.	Ordinary 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).