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  1. 2 Operations Research and Decisions

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  1. 2 2013

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  1. 2 GADOMSKI J.

  2. 1 NOWAK P.

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On some properties of composite distributed lag models

100%
GADOMSKI J.
Operations Research and Decisions
|
2013
|
vol. 23
|
issue 3
5-22
EN
The analysis presented in this paper is focused on basic properties of discrete distributed lag models. Such models are commonly used to model dynamic systems in various applications. In the presented considerations, time-varying distributed lags have been analyzed. Composite distributed lag models analyzed in this paper result from the summation or superposition of component distributed lag models. The analysis is restricted to models with a lag distribution, whose mean lag and variance exist. The paper presents relationships between the mean values and variances of the lag distributions of composite distributed lag models and of the component distributed lag models, as well as the relationships between the variance of the random term of composite distributed lag models and the variance of the random term of the component distributed lag models.
2
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Deterministic properties of serially connected distributed lag models

100%
NOWAK P., GADOMSKI J.
Operations Research and Decisions
|
2013
|
vol. 23
|
issue 3
43-55
EN
Distributed lag models are an important tool in modeling dynamic systems in economics. In the analysis of composite forms of such models, the component models are ordered in parallel (with the same independent variable) and/or in series (where the independent variable is also the dependent variable in the preceding model). This paper presents an analysis of certain deterministic properties of composite distributed lag models composed of component distributed lag models arranged in sequence, and their asymptotic properties in particular. The models considered are in discrete form. Even though the paper focuses on deterministic properties of distributed lag models, the derivations are based on analytical tools commonly used in probability theory such as probability distributions and the central limit theorem.
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