[PDF] Top 20 Non Gaussian structural time series models
Has 10000 "Non Gaussian structural time series models" found on our website. Below are the top 20 most common "Non Gaussian structural time series models".
Non Gaussian structural time series models
... b y fixing its first two moments it is possible to solve numerically for a t and f3t , and therefore, fully specify the prior-posterior analysis together with the predictive moments for y t . It is obvious that for this ... See full document
249
Estimation of semiparametric econometric time series models with non linear or heteroscedastic disturbances
... Most of the theory about the NN-estimators has been developed in an i.i.d. environment. There are some results on k-NN estimators under dependence, i.e. by Collomb (1985) and Yakowitz (1987), but nothing so far as we ... See full document
224
Prediction of Traffic Related Nitrogen Oxides Concentrations using Structural Time Series Models
... slope, ψ t is the autoregressive (AR) component, and ε t is the observation disturbance error or the random error component at the same instant of time. The disturbance errors are assumed to be normally and ... See full document
16
Beyond location and dispersion models: The Generalized Structural Time Series Model with Applications
... of time-varying skewness and kurtosis (as a way of capturing the dynamic behavior of uni- variate time series processes) has become more apparent in recent years, particularly since the aftermath of ... See full document
34
On uniqueness of moving average representations of heavy tailed stationary processes
... of non-Gaussian i.i.d. processes plays an important role in time series, for instance in the analysis of time reversibility (see Hallin, Lefèvre and Puri (1988), Breidt and Davis ... See full document
12
Bayesian Estimation of Non Gaussian Stochastic Volatility Models
... Volatility models have been widely used to model a changing variance of time series Financial data ...These models usually assume Gaussian distribution for asset returns conditional on ... See full document
9
Short Term Forecasting of Bicycle Traffic Using Structural Time Series Models
... In series and 16-19 hours ahead for the Out ...minute series, the peak MAPE actually ...hourly series were lower than those of the fifteen minute series, particularly in the Out ... See full document
6
Time series models of GDP: a reappraisal
... and non-linear time series models of real GDP on the basis of the consideration that business-cycle features themselves should motivate a good metric for judging a macroeconomic ... See full document
30
Multivariate Bayesian Structural Time Series Model
... target series has a different set of latent states and explanatory variables from ...multiple non-stationary compo- nents such as a time-varying local trend, seasonal effect, and potentially dynamic ... See full document
33
Forecasting Liquidity Ratio of Commercial Banks in Nigeria
... modeling time series in the presence of long memory, the Autoregressive fractionally integrated moving average (ARFIMA) model is ...ARFIMA models are time series models that ... See full document
9
Fast sequential parameter inference for dynamic state space models
... where Σ is the variance matrix and C is some constant value, both of which will be provided by the user. In real life applications, Σ is chosen to ensure low variability, especially for the static parameter situation. C ... See full document
193
Branch recombinant Gaussian processes for analysis of perturbations in biological time series
... generative models for inference of branching ...branch time. In Supplementary Figure S2b, we plot the branch time versus inferred branch time for 50 instances and compare to that achieved ... See full document
9
Multi-frequency scale Gaussian regression for noisy time-series data
... J = ⊕ − , where p and q are, respectively, the number of strictly positive and strictly negative eigen-values of ∆R. K=p+q, the total number of non- zero eigen-values, is the displacement rank of R. A key ... See full document
6
Copula selection models for non-Gaussian responses that are missing not at random
... Selection models have been commonly used to handle MNAR data in clinical and epidemio- logical research ...missingness models typically assuming bivariate ...bivariate Gaussian. At that time, ... See full document
31
Structural Time Series Modelling of Capacity Utilisation
... a structural non-linear time series model for joint esti- mation of capacity and its utilisation, thereby providing the statistical underpinnings to a measurement problem that has received ad ... See full document
30
Inflation Analysis: An Overview
... forecasts. Structural models are, however, useful in clarifying the relationships among the key macroeconomic variables which determine the rate of inflation and consequently provide a framework within ... See full document
23
Techniques for short term economic forecasting
... Assuming correct specification and exogenous variable assumptions, structural econometric models should produce more accurate forecasts than multivariate time series methods.. This arise[r] ... See full document
78
Flattening of the Phillips Curve and the Role of Oil Price: An Unobserved Components Model for the USA and Australia
... Previous studies have concentrated only on changes in the coefficient of the output gap (GAP) and neglected changes in the intercept and coefficients of other variables. This paper includes oil prices as an additional ... See full document
10
Mandelbrot's stochastic time series models
... these models by H=d+1/2 and in this Gaussian special case H is additionally equal to ...fGn time series X t , for ...and Gaussian distribution, but which shows slow trend-like fluctua- ... See full document
13
Orbital Priors for Time Series Models
... Although we agree that sometimes the Jeffreys’ prior may be useful in that it can penalize the non–identified parameter subspace in the parameter space, see e.g. Kleibergen and van Dijk (1994), Chao and Phillips ... See full document
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