Modelling time series count data: an Autoregressive Conditional Poisson model

By Andréas Heinen. 2003

212 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Finance

View Paper: https://www.semanticscholar.org/paper/75bc9d0e20bb89bbaf2b2ec931c8720c34c6054a

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Citations in the corpus

Dynamic Factor Models for Multivariate Count Data: An Application to Stock-Market Trading Activity (2011, unknown): “In order to analyze comovements in the number of trades for five individual stocks, Heinen and Rengifo (2007) also adopted an observation-driven approach extending the univariate autoregressive conditional Poisson model of Heinen (2003).”
Multivariate autoregressive modeling of time series count data using copulas (2007, background): “This paper constitutes a multivariate extension to the univariate time series of counts model developed in Heinen (2003).”
Forecasting Liquidity-Adjusted Intraday Value-at-Risk with Vine Copulas (2013, unknown): “We then turn to modeling the marginal behavior of stock returns and bid-ask spreads by the use of GARCH processes and the Autoregressive Conditional Double Poisson model, respectively (Heinen, 2003; Groß-Klußmann and Hautsch, 2011).”
Interventions in INGARCH processes (2010, unknown): “Similar models, in which fkt} is regressed on past values of the observed process and past values of fkt} itself, have been studied before by Rydberg and Shephard (2000), Streett (2000), Heinen (2003) and more recently by Fokianos et al. (2009).”
Some recent progress in count time series (2011, unknown): “Related work about the properties of Equation (6) has been carried out by Rydberg and Shephard [36], Streett [37], Heinen [38] and Ferland et al. [39]–in fact the last reference considers a more general model of order (p, q).”
Count Time Series Models (2012, background): “Several results for model (12) have been reported in the literature, see Rydberg and Shephard (2000), Streett (2000), Heinen (2003) and Ferland et al. (2006) who consider the following general model of order (p, q): Yt | FY,λt−1 ∼ Poisson(λt), λt = d + p ∑ i=1 aiλt−i + q ∑ j=1 bjYt−j, t ≥ max(p,…”
Integer‐Valued GARCH Process (2006, unknown): “These results are also given in Heinen (2001).”
Predicting Bid–Ask Spreads Using Long‐Memory Autoregressive Conditional Poisson Models (2011, unknown): “Furthermore, the proposed LMACP model contributes to the literature on dynamic count data models as in Rydberg and Shephard (1999), Heinen (2003), Fokianos et al. (2009) and Ferland et al. (2006) among others.”
The INARCH(1) Model for Overdispersed Time Series of Counts (2010, background): “, counts of cases of a certain disease), and others; for references, see Heinen (2003) and Weiß (2008). In line with this practical relevance, diverse models for count data time series have been proposed in literature, designed for different types of marginal distribution and autocorrelation structu”
Integer-valued autoregressive models for counts showing underdispersion (2013, methodology): “The DP distribution was introduced by Efron [9] (also see Section 11.1.8 in [15]) and applied to the INGARCH approach by Heinen [13].”
Count Time Series: A Methodological Review (2021, unknown): “…∼ Poisson(λt), λt = d + p∑ i=1 aiλt−i + q∑ j=1 bjXt−j, (16) where ai > 0 for i = 1, 2, . . . , p and bj > 0 for j = 1, 2, . . . , q; see Rydberg and Shephard (2000), Streett (2000), Heinen (2003), Ferland, Latour, and Oraichi (2006), and Fokianos, Rahbek, and Tjøstheim (2009) among others.”