Linear time series models and induced ACFs
Estimation for vector linear time series models
... The estimation of these parameters will be achieved by maximising the Gaussian likelihood (or spectral equivalents to this likelihood) although Gaussianity is not required for any of the results to follow. Chapter 1 ...
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11. Time series and dynamic linear models
... These models are linear state space models, where x t = F T t θ t represents the signal, θ t is the state vector, F t is a regression vector and G t is a state ...a time series such as ...
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Goodness-of-fit Tests for Linear and Non-linear Time Series Models
... In order to compare the pairwise approach with the case of considering a fixed number of lags in the conditioning set, we examine the finite sample properties of the CvM test of Bierens [r] ...
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Estimation and identification for vector linear time series models
... Jennrich [1969]. (ii) Subject to the assumptions in Theorem 4 and Corollary l,the iterates 0 ^ ^ are consistent and asymptotically as efficient as the Gaussian Estimate 0^. Thus, to simplify the computation, we may ...
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Inference problems for vector linear time series models
... Finally in this chapter Hannan’s estimation procedure is compared with two other methods due to Durbin [14]. Chapter 4 is concerned with the derivation of a method for the estimation of the coefficients of mixed ...
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Recursive estimation of non-linear time series models
... A r.ecursive scheme for simultaneous optimal estimation of conditional mean and variance in a nonlinear ARCH (autoregressive con- ditional heteroscedastic) model is also proposed.. Keywo[r] ...
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Efficient Regression in Time Series Partial Linear Models
... partial linear regression in time series mod- ...partial linear regression, and proposes an efficient frequency domain estimator for partial linear models with serially corre- ...
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Modeling count time series following generalized linear models
... such models to time ...space models for count time ...the linear predictor which cannot be accommodated yet by tscount but could possibly be included in future ...with time ...
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Time Series Forecasting of Hourly PM10 Using Localized Linear Models
... localized linear models for forecasting hourly PM10 concentration values using data from the monitoring networks of the cities of Athens, Helsinki and ...as linear regression and neural networks, by ...
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Generalized linear mixed models in time series studies of air pollution
... modelisresultedand,denotedbyGLMM+NS.Weinvestigatethevalidityofthismixedmodelingapproachthrough a simulation study. Our simulation results show that for small true pollution effects, fitting ...
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Identification, Estimation and Specification in a Class of Semi Linear Time Series Models
... We have so far discussed some issues for the case where { x t } is stationary. In order to es- tablish an asymptotic theory in each individual case, various conditions may be imposed on the probabilistic structure { e t ...
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Forecasting linear time series models with heteroskedastic errors in a Bayesian approach
... four models, namely Stochastic Volatility (SV), Generalized Au- toregressive Conditional Heteroskedasticity (GARCH), Autoregressive with GARCH errors (AR-GARCH) and Autoregressive with SV errors ...the ...
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Determining optimal architecture for dynamic linear models in time series applications
... Seasonal Models Seasonality is a term used to describe the cyclical or periodic fluctuations of a time ...certain time series data due to the fact that they may have important ...polynomial ...
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Multivariate linear parametric models applied to daily rainfall time series
... these models create negative val- ues in synthetic series, despite their capability to reproduce the main statistics of the observed ...simulated series is equal to the no-rain frequency, (ii) ...
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Which Methodology is Better for Combining Linear and Nonlinear Models for Time Series Forecasting?
... a time series into its linear and nonlinear form are one of the most popular hybrid models, which have recently been shown to be successful for single ...The linear ARIMA and the ...
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Some Convergence Theorems On Linear Models Generating A Pair Of Related Time Series
... Time Series Analysis: Time series analysis can be useful to see how a given asset, security or economic variable changes over ...same time period. Time series data often ...
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Estimation of semiparametric econometric time series models with non linear or heteroscedastic disturbances
... The remaining of the chapter is organized as follows: In §2 we will describe the kernel estimators which we are going to use. In §3 we will introduce the semiparametric estimator of the parameters 0. Also, we will ...
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Point and Interval Forecasting of Spot Electricity Prices: Linear vs. Non-Linear Time Series Models
... monthly time horizons are generally preferred for balance sheet calculations, risk management and derivatives pricing and, in many cases, concentrate not on the actual point forecasts but on the distributions of ...
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Forecasting Models. Time Series Models
... Error Forecasts are always wrong, but often still useful. We have already looked at catastrophic error, which invalidates the model when it happens. Error can also come from selecting a wrong model to apply. This amounts ...
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Generalized linear time series regression
... Generalized linear time series model; Latent time series; Linear ...Often time series data do not directly lend themselves to a classical analysis, because the ...
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