On fitting distributed lag models subject to polynomial restrictions

DOI10.1016/0304-4076(81)90106-8zbMath0471.62109OpenAlexW2084909427MaRDI QIDQ1157661

Marcello Pagano, Michael J. Hartley

Publication date: 1981

Published in: Journal of Econometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0304-4076(81)90106-8

zbMATH Keywords

orthogonal methodsdegree of polynomialautoregressive residualslength of lagorder of autoregression

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10)

Related Items (7)

Model specification tests for balanced representation state space models ⋮ How do emerging markets respond to macroeconomic shocks? -- Dynamic panel evidence on the effects of disasters ⋮ Testing for the length of a distributed lag with a differencing transformation ⋮ On fitting distributed lag models subject to polynomial restrictions ⋮ Superiority comparisons of heterogeneous linear estimators ⋮ Predicting hospital census using time series regression methods ⋮ Manufacturing investment-performance causality in the UK clothing industry

Cites Work

• On fitting distributed lag models subject to polynomial restrictions
• A General Procedure for Obtaining Maximum Likelihood Estimates in Generalized Regression Models
• Some Time and Frequency Domain Distributed Lag Estimators: A Comparative Monte Carlo Study
• Generalized Least Squares with an Estimated Autocovariance Matrix
• A Distributed Lag Estimator Derived from Smoothness Priors
• Testing the Restrictions of the Almon Lag Technique
• Some recent advances in time series modeling
• On the Linear Convergence of a Covariance Factorization Algorithm
• A Note on Bias in the Almon Distributed Lag Estimator
• Least squares, singular values and matrix approximations
• Testing for Fourth Order Autocorrelation in Quarterly Regression Equations
• Some Comments on C P
• The Choice of the Degree of a Polynomial Regression as a Multiple Decision Problem
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