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- Hacker, R Scott, 1964-, et al.
(författare)
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Optimal lag-length choice in stable and unstable VAR models under situations of homoscedasticity and ARCH
- 2008
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Ingår i: Journal of Applied Statistics. - : Routledge. - 0266-4763 .- 1360-0532. ; 35:6, s. 601-615
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Tidskriftsartikel (refereegranskat)abstract
- The performance of different information criteria - namely Akaike, corrected Akaike (AICC), Schwarz-Bayesian (SBC), and Hannan-Quinn - is investigated so as to choose the optimal lag length in stable and unstable vector autoregressive (VAR) models both when autoregressive conditional heteroscedasticity (ARCH) is present and when it is not. The investigation covers both large and small sample sizes. The Monte Carlo simulation results show that SBC has relatively better performance in lag-choice accuracy in many situations. It is also generally the least sensitive to ARCH regardless of stability or instability of the VAR model, especially in large sample sizes. These appealing properties of SBC make it the optimal criterion for choosing lag length in many situations, especially in the case of financial data, which are usually characterized by occasional periods of high volatility. SBC also has the best forecasting abilities in the majority of situations in which we vary sample size, stability, variance structure (ARCH or not), and forecast horizon (one period or five). frequently, AICC also has good lag-choosing and forecasting properties. However, when ARCH is present, the five-period forecast performance of all criteria in all situations worsens.
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| 2. |
- Strand, Mattias, et al.
(författare)
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Provision of External Data for DSS, BI, and DW by Syndicate Data Suppliers
- 2008
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Ingår i: Collaborative Decision Making. - Amsterdam : IOS Press. - 9781586038816 - 9781607503460 ; , s. 245-256, s. 245-256
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Konferensbidrag (refereegranskat)abstract
- In order to improve business performance and competitiveness it is important for firms to use data from their external environment. More and more attention is directed towards data originating external to the organization, i.e., external data. A firm can either collect this data or cooperate with an external data provider. We address the latter case and focus syndicate data suppliers (SDSs). They are the most common sources when incorporating external data into business intelligence, DSS, and DW solutions. SDSs are specialized in collecting, compiling, refining, and selling data. We provide a detailed description regarding the business idea of syndicate data suppliers and how they conduct their business, as well as a description of the industry of syndicate data suppliers. As such, the paper increases the understanding for external data incorporation and the possibility for firms to cooperate with syndicat data suppliers.
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