Estimating trends in tree-ring data

H. Visser, J. Molenaar

Research output: Contribution to journalArticleAcademicpeer-review

15 Citations (Scopus)

Abstract

Two methods from econometrics are introduced to estimate growth trends in time series of ring widths or basal-area increments. First, a trend model is described with a stochastic level and slope. The second model combines a doubly differenced trend and an ARMA model additively. Both models are put into a state-space form and are estimated using the discrete Kalman filter. Unknown noise variances, which control the flexibility of the trends, can be estimated by maximum-likelihood optimization or chosen by hand. It is concluded that the trend plus AR (1) model in combination with ML estimation performs very well. This model is attractive, because the ML-estimation procedure enables an objective choice for unknown parameters. Examples are given of two special features: the prediction of future growth, and the weighing of missing or unreliable data. Finally, both models are compared with spline interpolation and are validated by means of simulated time series.
Original languageEnglish
Pages (from-to)87-100
JournalForest Science
Volume36
Issue number1
Publication statusPublished - 1990

Fingerprint

growth rings
tree ring
time series analysis
time series
econometrics
Kalman filter
trend
basal area
interpolation
hands
prediction

Cite this

Visser, H., & Molenaar, J. (1990). Estimating trends in tree-ring data. Forest Science, 36(1), 87-100.
Visser, H. ; Molenaar, J. / Estimating trends in tree-ring data. In: Forest Science. 1990 ; Vol. 36, No. 1. pp. 87-100.
@article{c0924e7d729d4de69d35a991789ffeed,
title = "Estimating trends in tree-ring data",
abstract = "Two methods from econometrics are introduced to estimate growth trends in time series of ring widths or basal-area increments. First, a trend model is described with a stochastic level and slope. The second model combines a doubly differenced trend and an ARMA model additively. Both models are put into a state-space form and are estimated using the discrete Kalman filter. Unknown noise variances, which control the flexibility of the trends, can be estimated by maximum-likelihood optimization or chosen by hand. It is concluded that the trend plus AR (1) model in combination with ML estimation performs very well. This model is attractive, because the ML-estimation procedure enables an objective choice for unknown parameters. Examples are given of two special features: the prediction of future growth, and the weighing of missing or unreliable data. Finally, both models are compared with spline interpolation and are validated by means of simulated time series.",
author = "H. Visser and J. Molenaar",
year = "1990",
language = "English",
volume = "36",
pages = "87--100",
journal = "Forest Science",
issn = "0015-749X",
publisher = "Society of American Foresters",
number = "1",

}

Visser, H & Molenaar, J 1990, 'Estimating trends in tree-ring data', Forest Science, vol. 36, no. 1, pp. 87-100.

Estimating trends in tree-ring data. / Visser, H.; Molenaar, J.

In: Forest Science, Vol. 36, No. 1, 1990, p. 87-100.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - Estimating trends in tree-ring data

AU - Visser, H.

AU - Molenaar, J.

PY - 1990

Y1 - 1990

N2 - Two methods from econometrics are introduced to estimate growth trends in time series of ring widths or basal-area increments. First, a trend model is described with a stochastic level and slope. The second model combines a doubly differenced trend and an ARMA model additively. Both models are put into a state-space form and are estimated using the discrete Kalman filter. Unknown noise variances, which control the flexibility of the trends, can be estimated by maximum-likelihood optimization or chosen by hand. It is concluded that the trend plus AR (1) model in combination with ML estimation performs very well. This model is attractive, because the ML-estimation procedure enables an objective choice for unknown parameters. Examples are given of two special features: the prediction of future growth, and the weighing of missing or unreliable data. Finally, both models are compared with spline interpolation and are validated by means of simulated time series.

AB - Two methods from econometrics are introduced to estimate growth trends in time series of ring widths or basal-area increments. First, a trend model is described with a stochastic level and slope. The second model combines a doubly differenced trend and an ARMA model additively. Both models are put into a state-space form and are estimated using the discrete Kalman filter. Unknown noise variances, which control the flexibility of the trends, can be estimated by maximum-likelihood optimization or chosen by hand. It is concluded that the trend plus AR (1) model in combination with ML estimation performs very well. This model is attractive, because the ML-estimation procedure enables an objective choice for unknown parameters. Examples are given of two special features: the prediction of future growth, and the weighing of missing or unreliable data. Finally, both models are compared with spline interpolation and are validated by means of simulated time series.

M3 - Article

VL - 36

SP - 87

EP - 100

JO - Forest Science

JF - Forest Science

SN - 0015-749X

IS - 1

ER -

Visser H, Molenaar J. Estimating trends in tree-ring data. Forest Science. 1990;36(1):87-100.