Frankfurt airport's impact on regional and national employment and income : some new results using an improved version of the extended model for interregional input-output-analysis

We develop an interregional version of the standard textbook input-output model, that is extended with respect to the inclusion of the consumption expenditures and income generation process into the endogenous part of the input-output table. We also introduce a new method for deriving a two-region version of an interregional input-output table from original input-output tables for an overall economy and one of its regions. In an empirical assessment of the economic effects of the Frankfurt Airport, the interregional model is successfully employed. It is shown, that the model is capable of reducing the degree of overestimation of economic effects that results from inappropriate use of national input-output tables in the assessment of regional impact effects

Modeling the trading process on financial markets using the MSACD model

We propose a new framework for modeling time dependence in duration processes. The ACD approach introduced by Engle and Russell (1998) will be extended so that the conditional expectation of the durations depends on an unobservable stochastic process which is modeled via a Markov chain. The Markov switching ACD model (MSACD) is a flexible tool for description of financial duration processes. The introduction of a latent information regime variable can be justified in the light of recent market microstructure theories. In an empirical application we show that the MSACD approach is able to capture specific characteristics of inter trade durations while alternative ACD models fail. JEL classification: C41, C22, C25, C51, G1

Comparison of MSACD models

We propose a new framework for modelling time dependence in duration processes on financial markets. The well known autoregressive conditional duration (ACD) approach introduced by Engle and Russell (1998) will be extended in a way that allows the conditional expectation of the duration process to depend on an unobservable stochastic process which is modelled via a Markov chain. The Markov switching ACD model (MSACD) is a very flexible tool for description and forecasting of financial duration processes. In addition, the introduction of an unobservable, discrete valued regime variable can be justified in the light of recent market microstructure theories. In an empirical application we show that the MSACD approach is able to capture several specific characteristics of inter trade durations while alternative ACD models fail. JEL classification: C22, C25, C41, G1

The Markov switching ACD model

We propose a new framework for modelling time dependence in duration processes on financial markets. The well known autoregressive conditional duration (ACD) approach introduced by Engle and Russell (1998) will be extended in a way that allows the conditional expectation of the duration process to depend on an unobservable stochastic process, which is modelled via a Markov chain. The Markov switching ACD model (MSACD) is a very flexible tool for description and forecasting of financial duration processes. In addition the introduction of an unobservable, discrete valued regime variable can be justified in the light of recent market microstructure theories. In an empirical application we show, that the MSACD approach is able to capture several specific characteristics of inter trade durations while alternative ACD models fail. Furthermore, we use the MSACD to test implications of a sequential trade model

Polycyclic Aromatic Hydrocarbons in House Dust Samples : Source Identification and Apportionment

House dust is a heterogeneous matrix, consisting of a variety of inorganic, organic and biological materials. Once pollutants are adsorbed onto house dust particles, they either do not degrade at all or degrade at rates that are relatively slower than their ambient counterparts. Thus house dusts are useful reservoirs for chronic exposure to indoor pollutants. In this study, house dust samples from suburban houses in Brisbane, Australia were collected in summer 2004 and winter 2005. Given the growing need to understand the potential risks of indoor pollutants and to develop appropriate control strategies, the objective of the study was to use receptor-oriented models to estimate the number of sources, their compositions and the contribution of each source to the samples. Thus the polycyclic aromatic hydrocarbon (PAH) composition data were analyzed with advanced factor analysis models. Four factors were required to reproduce the summer data well and each factor had distinctive compositions that suggested that natural gas utilities, cooking, vehicle emissions and miscellaneous combustion processes are the main sources of PAHs in the samples. The implications of the results and of the observed correlation between the building characteristics and the PAH profiles on the quality of these indoor microenvironments and the development of control strategies are discussed