Σύγκριση μεθόδων

	Time-varying parameter OLS	Παλινδρόμηση Ελαχίστων Τετραγώνων (OLS)
Πεδίο	Οικονομετρία	Οικονομετρία
Οικογένεια	Regression model	Regression model
Έτος προέλευσης	1976	2019
Δημιουργός	Cooley & Prescott (1976); further developed by Harvey (1990)	Wooldridge (textbook treatment); classical least squares
Τύπος	Time-series regression with evolving coefficients	Linear regression
Θεμελιώδης πηγή	Cooley, T. F., & Prescott, E. C. (1976). Estimation in the Presence of Stochastic Parameter Variation. Econometrica, 44(1), 167–184.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Εναλλακτικές ονομασίες	TVP-OLS, time-varying coefficient regression, rolling OLS, locally weighted OLS	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Συναφείς	4	5
Σύνοψη	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 structural change in time-series 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).