Transformed Polynomials for Nonlinear Autoregressive Models of the Conditional Mean

Arbeitspapier

This paper proposes a new set of transformed polynomial functions that provide a flexible setting for nonlinear autoregressive modeling of the conditional mean while at the same time ensuring the strict stationarity, ergodicity, fading memory and existence of moments of the implied stochastic sequence. The great flexibility of the transformed polynomial functions makes them interesting for both parametric and semi-nonparametric autoregressive modeling. This flexibility is established by showing that transformed polynomial sieves are sup-norm-dense on the space of continuous functions and offer appropriate convergence speeds on Holder function spaces.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Tinbergen Institute Discussion Paper ; No. 12-133/III

Classification and Topics

• Classification: Wirtschaft ; Econometrics ; Estimation: General ; Semiparametric and Nonparametric Methods: General ; Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
• Subject: time-series ; nonlinear autoregressive models ; semi-nonparametric models ; method of sieves. ; Nichtlineares Verfahren ; Stochastischer Prozess ; Theorie

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Blasques, Francisco
• Event: Veröffentlichung
• (who): Tinbergen Institute
• (where): Amsterdam and Rotterdam
• (when): 2012

Further information

• Handle: http://hdl.handle.net/10419/87550
• Last update: 10.03.2025, 11:43 AM CET