A Partial Bias Correction Factor for Stock–Recruitment Parameter Estimation in the Presence of Autocorrelated Environmental Effects

1990 ◽  
Vol 47 (3) ◽  
pp. 516-519 ◽  
Author(s):  
Carl J. Walters
Keyword(s):  
Time Series ◽  
Monte Carlo ◽  
Parameter Estimation ◽  
Environmental Effects ◽  
Bias Correction ◽  
Parameter Estimates ◽  
Common Procedure ◽  
Stock Recruitment ◽  
The Common ◽  
Recruitment Time

Stock–recruitment time series often give a distorted picture of average recruitment rates, with high productivities per spawner being overrepresented at low stock sizes. This distortion is exaggerated by autocorrelation among years in environmental effects on productivity. The common procedure of fitting a stock–recruit curve and then analysing residuals from the curve will result in a substantial underestimate of the autocorrelation among environmental effects. Previous studies have recommended using Monte Carlo simulations to estimate the bias in stock–recruit model parameter estimates. These simulations can generally be avoided by using a simple correction equation. However, deviations from the corrected stock–recruit curve will not give better estimates of autocorrelation patterns in environmental effects, and hence will not help to provide better forecasts and stronger tests for factors that may be causing the effects.

scholarly journals Quirky patterns in time-series of estimates of recruitment could be artefacts

2014 ◽  
Vol 72 (1) ◽  
pp. 111-116 ◽  
Author(s):  
M. Dickey-Collas ◽  
N. T. Hintzen ◽  
R. D. M. Nash ◽  
P-J. Schön ◽  
M. R. Payne
Keyword(s):  
Time Series ◽  
Stock Assessment ◽  
Meta Analysis ◽  
Reference Points ◽  
Fish Stocks ◽  
Stock Recruitment ◽  
Modelling Assumptions ◽  
Meta Analyses ◽  
Recruitment Model ◽  
Recruitment Time

Abstract The accessibility of databases of global or regional stock assessment outputs is leading to an increase in meta-analysis of the dynamics of fish stocks. In most of these analyses, each of the time-series is generally assumed to be directly comparable. However, the approach to stock assessment employed, and the associated modelling assumptions, can have an important influence on the characteristics of each time-series. We explore this idea by investigating recruitment time-series with three different recruitment parameterizations: a stock–recruitment model, a random-walk time-series model, and non-parametric “free” estimation of recruitment. We show that the recruitment time-series is sensitive to model assumptions and this can impact reference points in management, the perception of variability in recruitment and thus undermine meta-analyses. The assumption of the direct comparability of recruitment time-series in databases is therefore not consistent across or within species and stocks. Caution is therefore required as perhaps the characteristics of the time-series of stock dynamics may be determined by the model used to generate them, rather than underlying ecological phenomena. This is especially true when information about cohort abundance is noisy or lacking.

State-space likelihoods for nonlinear fisheries time-series

2005 ◽  
Vol 62 (9) ◽  
pp. 1937-1952 ◽  
Author(s):  
Perry de Valpine ◽  
Ray Hilborn
Keyword(s):  
Time Series ◽  
Monte Carlo ◽  
Bayesian Analysis ◽  
State Space ◽  
Nonlinear Models ◽  
Confidence Regions ◽  
State Space Models ◽  
Model Complexity ◽  
Parameter Estimates ◽  
Nonlinear State Space Models

State-space models are commonly used to incorporate process and observation errors in analysis of fisheries time series. A gap in analysis methods has been the lack of classical likelihood methods for nonlinear state-space models. We evaluate a method that uses weighted kernel density estimates of Bayesian posterior samples to estimate likelihoods (Monte Carlo Kernel Likelihoods, MCKL). Classical likelihoods require integration over the state-space, and we compare MCKL to the widely used errors-in-variables (EV) method, which estimates states jointly with parameters by maximizing a nonintegrated likelihood. For a simulated, linear, autoregressive model and a Schaefer model fit to cape hake (Merluccius capensis × M. paradoxus) data, classical likelihoods outperform EV likelihoods, which give asymptotically biased parameter estimates and inaccurate confidence regions. Our results on the importance of integrated state-space likelihoods also support the value of Bayesian analysis with Monte Carlo posterior integration. Both approaches provide valuable insights and can be used complementarily. Previously, Bayesian analysis was the only option for incorporating process and observation errors with complex nonlinear models. The MCKL method provides a classical approach for such models, so that choice of analysis approach need not depend on model complexity.

Time series bias in the estimation of density-dependent mortality in stock-recruitment models

1995 ◽  
Vol 52 (1) ◽  
pp. 223-232 ◽  
Author(s):  
Ransom A. Myers ◽  
N. J. Barrowman
Keyword(s):  
Time Series ◽  
Gadus Morhua ◽  
Large Time ◽  
Atlantic Cod ◽  
Model Misspecification ◽  
Parameter Estimates ◽  
Density Dependent Mortality ◽  
Stock Recruitment ◽  
Iteroparous Species ◽  
The Relationship

Large biases can occur in parameter estimates for stock–recruitment models because the stock sizes are not chosen independently, being correlated with variability in recruitment. We examine the importance of this "time series bias" by a comprehensive analysis of available stock–recruitment data and the use of simulations. For semelparous species, i.e., species that reproduce only once, time series bias is important for all populations for which we had data. For iteroparous species, i.e., species that reproduce more than once, large biases occur if the populations are exploited at close to the maximum that is biologically possible. Notably, when there is autocorrelation in natural mortality, for univoltine species, the direction of bias is reversed due to model misspecification. Given moderate sample sizes and moderate levels of exploitation, time series bias is small for species such as Atlantic cod (Gadus morhua), for which α, the slope of the relationship between recruitment and number of spawners as the number of spawners goes to zero, is large. Time series bias will usually be important in species such as hakes (Merluccius) for which α appears to be relatively small.

Bias in the Estimation of Functional Relationships from Time Series Data

1985 ◽  
Vol 42 (1) ◽  
pp. 147-149 ◽  
Author(s):  
Carl J. Walters
Keyword(s):  
Time Series ◽  
Measurement Errors ◽  
Time Series Data ◽  
Series Data ◽  
Parameter Estimates ◽  
Natural Experiments ◽  
Independent Variables ◽  
Functional Relationships ◽  
Stock Recruitment ◽  
The Relationship

Functional relationships, such as stock–recruitment curves, are generally estimated from time series data where natural "random" factors have generated both deviations from the relationship and also informative variation in the independent variables. Even in the absence of measurement errors, such natural experiments can lead to severely biased parameter estimates. For stock–recruitment models, the bias is misleading for management: the stock will appear too productive when it is low, and too unproductive when it is large. The likely magnitude of such biases can and should be determined for any particular case by Monte Carlo simulations.

scholarly journals A note on stochastic volatility model estimation

2019 ◽  
Vol 17 (4) ◽  
pp. 22
Author(s):  
Omar Abbara ◽  
Mauricio Zevallos
Keyword(s):  
Time Series ◽  
Monte Carlo ◽  
Stochastic Volatility ◽  
Stochastic Volatility Model ◽  
Parameter Estimates ◽  
Model Estimation ◽  
Stochastic Volatility Models ◽  
Volatility Models ◽  
Volatility Model ◽  
Monte Carlo Evaluation

<p>The paper assesses the method proposed by Shumway and Stoffer (2006, Chapter 6, Section 10) to estimate the parameters and volatility of stochastic volatility models. First, the paper presents a Monte Carlo evaluation of the parameter estimates considering several distributions for the perturbations in the observation equation. Second, the method is assessed empirically, through backtesting evaluation of VaR forecasts of the S&amp;P 500 time series returns. In both analyses, the paper also evaluates the convenience of using the Fuller transformation.</p>

Empirical and theoretical analyses of correction of time-series bias in stock-recruitment relationships of sockeye salmon (Oncorhynchus nerka)

1995 ◽  
Vol 52 (10) ◽  
pp. 2174-2189 ◽  
Author(s):  
Josh Korman ◽  
Randall M. Peterman ◽  
Carl J. Walters
Keyword(s):  
Time Series ◽  
Least Squares ◽  
Bias Correction ◽  
Sockeye Salmon ◽  
Oncorhynchus Nerka ◽  
Empirical Relationship ◽  
Estimation Method ◽  
Estimation Methods ◽  
Theoretic Approach ◽  
Stock Recruitment

Using data from 30 sockeye salmon (Oncorhynchus nerka) stocks and Monte Carlo simulations, we examined the importance of time-series bias on estimates of optimal harvest rate, optimal escapement, and sustainable yield. We compared the performance of the least-squares procedure for fitting a Ricker curve with an existing bias-correction method. Simulations showed that the effect of time-series bias is greatest for low-productivity stocks that exhibit a high degree of autocorrelation among residuals of the stock-recruitment relationship. A strong inverse empirical relationship between autocorrelation and stock productivity among the 30 stocks suggests that time-series bias is a more important concern for low-productivity northern stocks than for more productive southern stocks. The corrected method reduced bias in optimal escapement estimates under a limited set of conditions but at the price of increased variance in the estimates. For a constant escapement goal policy, using the bias correction thus resulted in sustainable yields slightly lower than or equal to expected values for 28 of the 30 stocks compared with yields obtained using the standard least-squares estimation method. We demonstrate the value of using a decision theoretic approach to evaluate the performance of estimation methods.

scholarly journals Estimating the Parameters of Dynamical Systems from Big Data Using Sequential Monte Carlo Samplers

2018 ◽  
Author(s):  
Peter Green ◽  
Simon Maskell
Keyword(s):  
Monte Carlo ◽  
Big Data ◽  
Parameter Estimation ◽  
Markov Chain ◽  
Dynamical Systems ◽  
Structural Dynamics ◽  
Sequential Monte Carlo ◽  
Measurement Data ◽  
Parameter Estimates ◽  
Computationally Efficient

In this paper the authors present a method which facilitates computationally efficient parameter estimation of dynamical systems from a continuously growing set of measurement data. It is shown that the proposed method, which utilises Sequential Monte Carlo samplers, is guaranteed to be fully parallelisable (in contrast to Markov chain Monte Carlo methods) and can be applied to a wide variety of scenarios within structural dynamics. Its ability to allow convergence of one's parameter estimates, as more data is analysed, sets it apart from other sequential methods (such as the particle filter).

Parameter estimation in linear functional relationships

1983 ◽  
Vol 245 (2) ◽  
pp. R135-R142
Author(s):  
E. M. Haacke ◽  
M. D. Goldman
Keyword(s):  
Monte Carlo ◽  
Parameter Estimation ◽  
Linear Functional ◽  
Linear Models ◽  
Weighted Least Squares ◽  
Parameter Estimates ◽  
Volume Changes ◽  
Independent Variables ◽  
Functional Relationships ◽  
Data Space

We present the application of a weighted least-squares technique to extract parameter estimates in linear models when all variables are subject to error and the goal of the investigation is the value of the parameters themselves. We assume that the relative variances of the variables are known and that the errors between variables are independent. The method of parameter estimation for linear functional relationships is presented, and we describe its differences from linear regression. We discuss how to obtain confidence intervals for the parameter estimates with an emphasis on computer Monte Carlo simulations. An explicit example related to measurements of lung volume changes is presented. An eigenvalue analysis of the data pertaining to the number of independent variables and a physical interpretation of the data space are also discussed.

Parameter Estimation for the Constrained Gravity Model: A Comparison of Six Methods

1976 ◽  
Vol 8 (6) ◽  
pp. 673-683 ◽  
Author(s):  
F Stetzer
Keyword(s):  
Monte Carlo ◽  
Parameter Estimation ◽  
Gravity Model ◽  
Parameter Estimates ◽  
Sample Sizes ◽  
Specification Error ◽  
Nonlinear Methods ◽  
Monte Carlo Experiments ◽  
Sampling Variation ◽  
Source Of Error

Six methods of parameter estimation for the production-constrained gravity model are compared in the context of interurban consumer travel. Monte-Carlo experiments reveal that the nonlinear methods (Batty and Mackie, 1972) are inferior to the linear methods (Nakanishi and Cooper, 1974) when there is specification error present, but that the latter rapidly lose their advantage as sample sizes decrease. Bias in the parameter estimates is a more serious source of error than sampling variation.

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