autoregressive process
Efficiency of Some Estimators for a Generalized Poisson Autoregressive Process of Order 1
... Various models have been proposed in the literature to study non-negative integer-valued time series. In this paper, we study estimators for the generalized Poisson autoregressive process of order 1, a ...
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Multivariate elliptically contoured autoregressive process
... First, we assume that the mean vector µ is known. Later, it is shown how this assump- tion can be violated. The rest of parameters of the M E l AR k (µ, Σ , p) process we denote by θ which are divided into two ...
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Analysis of DAR(1)/D/s Queue with Quasi Negative Binomial II as Marginal Distribution
... arrival process of a multiserver queue governed by a discrete autoregressive process of order 1 [DAR(1)] with Quasi-Negative Binomial Distribution-II as the marginal ...with autoregressive ...
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A Bayesian latent process spatiotemporal regression model for areal count data
... Additional analyses were carried out to compare the best-fitting model (i.e. Model 5) with some existing CAR-based models used for analyzing spatiotemporal areal data. The first CAR model examined (CAR-1), a modification ...
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Study & Development of Short Term Load Forecasting Models Using Stochastic Time Series Analysis
... an Autoregressive process, partial autocorrelation function (pacf) is useful in determination of the order of the AR model & autocorrelation function (acf) for Moving Average (MA) process is ...
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Time Series Analysis of Demand for Domestic Air Travel in Nigeria
... The Autoregressive Moving Average (ARMA) process was applied to estimate and forecast the future demand for domestic air travel in ...is Autoregressive process of order p and Moving Average ...
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Fractional Integration of the Price Dividend Ratio in a Present Value Model
... memory process, it is very likely that spurious breaks will be de- ...true process was generated by occasional breaks, the long memory process can successfully reproduce many features of the true ...
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Big Data impacts on stochastic Forecast Models: Evidence from FX time series
... is Autoregressive Neural Network Processes (ARNN), a neural network based nonlinear extension of classical autoregressive process models from time series analysis (see Dietz ...series process ...
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Bayesian Inference in Spatial Sample Selection Models
... McMillen, (1995) extends the EM algorithm suggested in McMillen, (1992) to a sample selection model that has a first order spatial autoregressive process in the disturbance term. The estimation scheme ...
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Hyper spherical and Elliptical Stochastic Cycles
... A univariate first order stochastic cycle can be represented as an element of a bivariate first order vector autoregressive process, or VAR(1), where the transition matrix is associated with a Givens ...
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Time Series Analysis of Production and Price of Cattle and Milkfish in the Philippines
... The Ljung–Box test is rarely used in autoregressive integrated moving average (ARIMA) modeling. It is applied to the residuals of a fitted ARIMA model, not the original series, and in such applications the ...
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Compare Value at Risk and Return of Assets Portfolio Stock, Gold, REIT, U.S. & Iran Market Indices
... Their results represent that these models are in some cases poorly accurate because banks occasionally experienced much losses more than their replicas predicted, which recommends these models are deprived at dealing ...
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Stock Volatility Modelling with Augmented GARCH Model with Jumps
... Empirical studies based on the log return time series data of some stocks showed that serial dependence is present in the data; volatility changes over time; distribution of the data is heavy-tailed, asymmetric and ...
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On the Performance of Garch Family Models in the Presence of Additive Outliers
... The autoregressive conditional heteroskedasticity (ARCH) model introduced by Fredrick Engel in 1982 is the first model that assumed that volatility is not ...
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Two Distinct Seasonally Fractionally Differenced Periodic Processes
... nothing is clear about the models (1:1) and (1:2). More precisely, no thing is clear about the stationarity conditions for the model (1:1), because his in…nite moving average representation is unknown and no thing is ...
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Link Prediction in Graphs with Autoregressive Features
... structure amounts to performing graph inference through the discovery of uncovered edges on the graph. The latter problem is interesting per se and it is known as the problem of link prediction where it is assumed that ...
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The Glosten-Jagannathan-Runkle-Generalized Autoregressive Conditional Heteroscedastic Approach to Investigating the Foreign Exchange Forward Premium Volatility
... This paper empirically investigates the volatility dynamics of the EUR/USD forward premium via generalized autoregressive conditional heteroscedastic (GARCH-M) (1,1) and Glosten-Jagannathan-Runkle (GJR)-GARCH ...
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EMPIRICAL INVESTIGATION OF RELATIONSHIP BETWEEN STOCK MARKET AND FOREIGN EXCHANGE RATE: A STUDY OF INDIA
... The purpose of the co-integration test is to determine whether a group of nonstationary series is co-integrated or not. The presence of cointegrating relation forms the basis of the Vector Error Correction (VEC) model ...
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On categorical time series with covariates
... Likelihood based inference for the models we study can be developed along the lines of previous refer- ences by appealing to the properties of multinomial distribution. The proof of consistency and asymptotic normality ...
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Assessment of data-driven models in downscaling of the daily temperature in Birjand synoptic station
... global temperatures, but also changes in the characteristics of systems that balance the Earth’s atmosphere with the existence of has brought. The most important tool for simulating the future status of climate ...
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