nonlinear time series estimation
Estimation and testing of persistence in nonlinear and cyclical time series
... The long-run properties of the short-term nominal and real interest rates have re ceived a great deal of attention. Initially, Fama (1975) employed techniques based on the sample autocorrelation function, and inferred ...
253
A flexible approach to parametric inference in nonlinear and time varying time series models
... are nonlinear or exhibit structural breaks or time variation in ...or time varying parameter models to examine whether monetary policy rules have changed over ...exhibit time variation or ...
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Advances in Statistical Network Modeling and Nonlinear Time Series Modeling
... Contribution of Chapter 2: Exchangeable graph models (ExGM) are a nonparametric ap- proach to modeling network data that subsumes a number of popular models. The key ob- ject that de nes an ExGM is of en referred to as a ...
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Estimation of Fractal Dimension of a Noisy Time Series
... a nonlinear locally projective noise reduction scheme specifically developed for chaotic data as proposed by ...projective nonlinear noise reduction scheme is to identify the manifold and to project the ...
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Autoregressive nonlinear time-series modeling of traffic fatalities in Europe
... safety time-series are often small, such models are suitable for this ...the time-intervals should be such that they provide adequate data for the model estimation and still allow for a ...
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Concepts and tools for nonlinear time series modelling
... how the accuracy of the combined predictor can be improved removing the convexity constraint and adding a constant term to the combination. In this way, even if the candidate predictors are biased their combination can ...
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Search for Additive Nonlinear Time Series Causal Models
... likelihood estimation algorithm that allows the computation of BIC scores for certain types of linear models with correlated error terms, though not for the contempo- raneous causal structure of a additive ...
25
Noise reduction in nonlinear time series analysis
... the estimation process and the noise reduction process ...the estimation o f the dynamics and applying one iterate o f their particular noise reduction ...
152
Dimensionality reduction in nonparametric conditional density estimation with applications to nonlinear time series
... of estimation by using the R statistical package ‘loc…t’ of Clive ...kernel estimation by k = nh d ...computational time, it o¤ered no signi…cant bene…t relatively to the …xed bandwidth in terms of ...
153
Fractional cointegration analysis of nonlinear time series with long memory
... NBLS estimation by regression on a proxy for market returns yields reassuringly close results to the first factor of NBPC, which does not rely on pre-specified risk ...
223
Nonlinear time series: semiparametric and nonparametric methods
... parametric estimation has not been uncomplicated due to the difficulty finding the maximum likelihood (ML) ...1994, estimation procedures have been ...different estimation procedures developed for ...
245
Essays on economic time series forecasting
... higher estimation uncertainty compared to a simple parameterized ...the nonlinear time series more accurately than the type of ANN we use in the ...
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Non-parametric smoothing and prediction for nonlinear circular time series
... circular time series, as done in the present ...trend estimation and prediction within a basic model which adds a stationary random noise process to a deterministic trend which is not restricted in ...
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Software Reliability Modeling in Fuzzy Environment
... reliability nonlinear modeling based on time series Time series analysis theory is a method of describing statistics character of dynamics data, which can set up time ...
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Nonlinear time series analysis in financial applications
... the estimation on data set ranging from January 1970 to January 2003, are sensitive to the changes in sample periods ...each time point from February 2003 to September 2010), the likelihood function may not ...
307
Generalized Estimation of Missing Observations in Nonlinear Time Series Model Using State Space Representation
... Abstract: The aim of the study was to formulate a Time Series Model to be used in obtaining optimal estimates of miss- ing observations. State space models and Kalman filter were used to handle irregularly ...
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Estimation for nonlinear time series models using estimating equations
... In this section we recall Godambe's (1985) theorem on stochastic processes and apply it to obtain optimal estimates for recently proposed nonlinear time series models.. Let {Yt1 td} be a[r] ...
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Semiparametric Estimation in Multivariate Nonstationary Time Series Models
... semiparametric nonlinear time series models is studied with possible dependence structures and nonstationarities in the para- metric and nonparametric ...integrated time series. Semi- ...
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Bayesian inference for nonlinear structural time series models
... We initialised the Metropolis-Hastings chain at the maximum likelihood estimate ob- tained from a first-order approximation of the model via the Kalman filter. We chose this initialisation method because we observed that ...
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Subharmonic oscillation modeling and MISO Volterra series
... In order to derive a Volterra MISO model, a modified input is produced. The modified input is generated from the original input signal, knowing the order of the subharmonic and the time period . Both and ...
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