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- Helske, Jouni, et al.
(författare)
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Estimating aggregated nutrient fluxes in four Finnish rivers via Gaussian state space models
- 2013
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Ingår i: Environmetrics. - : Wiley. - 1180-4009 .- 1099-095X. ; 24:4, s. 237-247
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Tidskriftsartikel (refereegranskat)abstract
- Reliable estimates of the nutrient fluxes carried by rivers from land-based sources to the sea are needed for efficient abatement of marine eutrophication. Although nutrient concentrations in rivers generally display large temporal variation, sampling and analysis for nutrients, unlike flow measurements, are rarely performed on a daily basis. The infrequent data calls for ways to reliably estimate the nutrient concentrations of the missing days. Here, we use the Gaussian state space models with daily water flow as a predictor variable to predict missing nutrient concentrations for four agriculturally impacted Finnish rivers. Via simulation of Gaussian state space models, we are able to estimate aggregated yearly phosphorus and nitrogen fluxes, and their confidence intervals.The effect of model uncertainty is evaluated through a Monte Carlo experiment, where randomly selected sets of nutrient measurements are removed and then predicted by the remaining values together with re-estimated parameters. Results show that our model performs well for rivers with long-term records of flow. Finally, despite the drastic decreases in nutrient loads on the agricultural catchments of the rivers over the last 25years, we observe no corresponding trends in riverine nutrient fluxes.
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| 2. |
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| 3. |
- Helske, Jouni, et al.
(författare)
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Improved frequentist prediction intervals for autoregressive models by simulation
- 2015
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Ingår i: Unobserved Components and Time Series Econometrics. - Oxford : Oxford University Press. - 9780199683666 - 9780191763298 ; , s. 291-309
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- It is well known that the so-called plug-in prediction intervals for autoregressive processes, with Gaussian disturbances, are too short, i.e. the coverage probabilities fall below the nominal ones. However, simulation experiments show that the formulas borrowed from the ordinary linear regression theory yield one-step prediction intervals, which have coverage probabilities very close to that claimed. From a Bayesian point of view the resulting intervals are posterior predictive intervals when uniform priors are assumed for both autoregressive coefficients and logarithm of the disturbance variance. This finding enables one to see how to treat multi-step prediction intervals that are obtained by simulation either directly from the posterior distribution or using importance sampling. An application of the method to forecasting the annual gross domestic product growth in the United Kingdom and Spain is given for the period 2002 to 2011 using the estimation period 1962 to 2001.
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