Count time series models
Poisson qmle of count time series models
... general count time series models. This allows for obtaining optimal predictions without having to specify entirely the conditional distribution. Second, the asymptotic distribution of the ...
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Likelihood Inference for Generalized Integer Autoregressive Time Series Models
... of count time series models is based on thinning operators, which are replacements of the multiplication operator to maintain support on the non-negative ...for count time ...
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Mixtures of Nonlinear Poisson Autoregressions
... nonlinear models. Indeed, the main model we consider is a mixture of Poisson models but the mean is allowed to depend nonlinearly on lagged values of the observed time ...INGARCH models. We ...
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Non Gaussian structural time series models
... such series is the monthly totals of drivers of light goods vehicles (LGV) ...the series contains no zero observations, a Gaussian model can be fitted to the logarithms of the ob s e r v a t i o n s ...a ...
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Count and duration time series with equal conditional stochastic and mean orders
... ACD-like models, even if the probabilistic structure of the model is then complicated by the absence of an explicit iid innovation ...non-positive series ǫ t ...
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Two stage weighted least squares estimator of the conditional mean of observation driven time series models
... a time series, which do not require the whole knowledge of the cdf of the ...INARCH models. We applied our general results to standard count and duration ...
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Modelling Time Series Count Data: An Autoregressive Conditional Poisson Model
... three models, two of which assume that repeated observations are independent and follow a negative binomial distribution in one case or have a constant coefficient of variation in the other ...The time ...
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TIME SERIES ANALYSIS OF ROAD TRAFFIC ACCIDENTS IN NIGERIA
... econometric models within the accident modelling literature to model time series accident count ...these models are more suitable for cross-sectional count ...Modelling ...
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Bidimensional discrete time risk models based on bivariate claim count time series
... risk models, which are based on the assumptions that the claim counts obey some specific bivariate integer-valued time series such as bivariate Poisson MA (BPMA) and the bivariate Poisson AR (BPAR) ...
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Model selection for time series of count data
... of models for integer valued time series a key question is, what is the most appro- priate model for a given data set? This leads onto a secondary question of the appropriate order p for an INAR(p) ...
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Bayesian inference for nonlinear structural time series models
... As expected, the estimated parameter values from all filters are very similar. However, there are notable differences in efficiency and computing time. Table 8 reports the Metropolis- Hastings acceptance rates for ...
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Bayesian graphical forecasting models for business time series
... In chapter 5, a new class of Bayesian forecasting model is developed which defines a conditional independence structure across the brand sales in a market and utilises any heuristic caus[r] ...
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Long memory and structural breaks in time series models
... of time-domain bootstrap procedures for time series has been proposed, ranging from parametric procedures such as those of Freedman (1984) or Efron and Tibshirani (1986) to nonparametric methods, ...
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Machine Learning Models for Sales Time Series Forecasting
... with a long time period (2 years) for a specific store, Figure 11 shows the forecast in the case of historical 79. data with a short time period (3 days) for the same specific store[r] ...
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A study of estimation procedures for time series models in economics
... that Models A and E may be regarded as representative of unfiltered yearly data or deseasonalized quarterly data while Model D with its peak at tt / 2 in the squared gain of the ...a series with spectral ...
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Selection of Heteroscedastic Models: A Time Series Forecasting Approach
... competing models that can be used for forecasting a particular time ...in time series; the in-sample model selection and the out-of-sample model ...candidate models leads to a model ...
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Some aspects of estimation for vector time series models
... for series that are relatively ...ARMA models have been studied by Osborn (1977) for the pure moving average case, and by Phadke and Kedem (1978), Nicholls and Hall (1979), Hillmer and Tiao (1979), and in a ...
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Estimation and identification for vector linear time series models
... where we allow identities to be present in (1.10). As far as Econometric applications are concerned, the most important ARMAX identification results have been given by Hannan in a series of papers, Hannan [1969a, ...
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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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Training Energy-Based Models for Time-Series Imputation
... To compare our approach with generative methods, we also trained a Convolutional RBM with the same energy function as the Convolutional Energy-Based Model all these data sets. Between these two models, the ...
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