The conventional Sargan (1958) Keywords: Dynamic panel data; tests of overidentifying restrictions; exponential tilting; GMM. * Thanks are due to Stephen Bond, Neil Shephard and Manuel Arellano for helpful discussions on the subject matter of this paper, and to Richard Spady for having kindly provided some of the software. The software used to perform the simulations included the DPD for Ox package described in Doornik, Arellano and Bond (1999) and was written using the Ox language Version 2.10 (Doornik (1999)) throughout. Financial support from the ESRC research grant R00023839 entitled 'Econometrics of trade-by-trade price dynamics' is gratefully acknowledged.

A continuous time econometric modelling framework for multivariate market event (or 'transactions') data is developed in which the model is specified via the vector stochastic intensity. This has the advantage that the conditioning σ-field is updated continuously in time as new information arrives. We introduce the class of generalised Hawkes models which allow the estimation of the dependence of the intensity on the events of previous trading days. Analytic likelihoods are available and we show how to construct diagnostic tests based on the transformation of non-Poisson processes into standard Poisson processes using random changes of time. A proof of the validity of the diagnostic testing procedures is given that imposes only a very weak condition on the point process model, thus establishing their widespread applicability. A continuous time bivariate point process model of the timing of trades and mid-quote changes is presented for a NYSE stock and the empirical findings are related to the theoretical and empirical market microstructure literature.

To understand how cells control and exploit biochemical fluctuations, we must identify the sources of stochasticity, quantify their effects, and distinguish informative variation from confounding "noise." We present an analysis that allows fluctuations of biochemical networks to be decomposed into multiple components, gives conditions for the design of experimental reporters to measure all components, and provides a technique to predict the magnitude of these components from models. Further, we identify a particular component of variation that can be used to quantify the efficacy of information flow through a biochemical network. By applying our approach to osmosensing in yeast, we can predict the probability of the different osmotic conditions experienced by wildtype yeast and show that the majority of variation can be informational if we include variation generated in response to the cellular environment. Our results are fundamental to quantifying sources of variation and thus are a means to understand biological "design."analysis of variance | internal history | gene expression | signal transduction | intrinsic and extrinsic noise C ells must make decisions in fluctuating environments using stochastic biochemistry. Such effects create variation between isogenic cells, which despite sometimes being disadvantageous for individuals may be advantageous for populations (1). Although the random occurrence and timing of chemical reactions are the primary intracellular source, we do not know how much different biochemical processes contribute to the observed heterogeneity (2). It is neither clear how fluctuations in one cellular process will affect variation in another nor how an experimental assay could be designed to quantify this effect. Further, we cannot distinguish variation that is extraneous "noise" from that generated by the flow of information within and between biochemical networks. We will show that a general technique to decompose fluctuations into their constituent parts provides a solution to these problems.Previous work divided variation in gene expression in isogenic populations into two components (3, 4): intrinsic and extrinsic variation. Both components necessarily include a variety of biochemical processes yet dissecting the effects of these processes has previously not been possible. Intrinsic variation should be understood as the average "variability" in gene expression between two copies of the same gene under identical intracellular conditions (4); extrinsic variation is the additional variation generated by interaction with other stochastic systems in the cell and the cell's environment. Single-cell experiments established that stochasticity generated during gene expression can be substantial in both bacteria (3, 5) and eukaryotes (6, 7), but did not identify the biochemical processes that generate this variation, regardless of whether the variation is intrinsic or extrinsic. Decomposing Variation in Biochemical SystemsConsider a fluctuating molecular species in a biochemical system and let ...

A continuous time econometric modelling framework for multivariate market event (or 'transactions') data is developed in which the model is specified via the vector stochastic intensity. This has the advantage that the conditioning σ-field is updated continuously in time as new information arrives. We introduce the class of generalised Hawkes models which allow the estimation of the dependence of the intensity on the events of previous trading days. Analytic likelihoods are available and we show how to construct diagnostic tests based on the transformation of non-Poisson processes into standard Poisson processes using random changes of time. A proof of the validity of the diagnostic testing procedures is given that imposes only a very weak condition on the point process model, thus establishing their widespread applicability. A continuous time bivariate point process model of the timing of trades and mid-quote changes is presented for a NYSE stock and the empirical findings are related to the theoretical and empirical market microstructure literature.

Cells must sense extracellular signals and transfer the information contained about their environment reliably to make appropriate decisions. To perform these tasks, cells use signal transduction networks that are subject to various sources of noise. Here, we study the effects on information transfer of two particular types of noise: basal (leaky) network activity and cell-to-cell variability in the componentry of the network. Basal activity is the propensity for activation of the network output in the absence of the signal of interest. We show, using theoretical models of protein kinase signaling, that the combined effect of the two types of noise makes information transfer by such networks highly vulnerable to the loss of negative feedback. In an experimental study of ERK signaling by single cells with heterogeneous ERK expression levels, we verify our theoretical prediction: In the presence of basal network activity, negative feedback substantially increases information transfer to the nucleus by both preventing a near-flat average response curve and reducing sensitivity to variation in substrate expression levels. The interplay between basal network activity, heterogeneity in network componentry, and feedback is thus critical for the effectiveness of protein kinase signaling. Basal activity is widespread in signaling systems under physiological conditions, has phenotypic consequences, and is often raised in disease. Our results reveal an important role for negative feedback mechanisms in protecting the information transfer function of saturable, heterogeneous cell signaling systems from basal activity.cell sensing | MAPK signaling | mutual information | ultrasensitivity | biomolecular networks C ells must sense extracellular concentrations and transfer the information contained about their environment reliably to make appropriate decisions. Understanding the process of information transfer from the biological environment to the nucleus (1) and studying quantitatively how information about the signal is lost along the way are essential in understanding cellular decision-making (2, 3). The signal transduction networks used by cells are subject to various sources of noise, and we are only beginning to explore how these affect the process of information transfer (4, 5). Here we focus, in the context of protein kinase (PK) signaling, on the little-studied effects of two important types of biological noise: cell-to-cell variability in the componentry of the network and basal network activity. The effect on information transfer of cell-to-cell variation (heterogeneity) in the protein componentry of signaling networks remains largely unexplored. Such variation is expected under physiological conditions (6) and underlies the variable responses observed for genetically identical cells exposed to the same stimulus or drug treatment (7-9). By basal activity, we mean the propensity for activation of the signaling system in the absence of the stimulus or signal of interest. Basal activity is widespread in signaling system...