Monetary and fiscal policy interactions in a New Keynesian model with capital accumulation and non-Ricardian consumers

This paper develops a small New Keynesian model with capital accumulation and government debt dynamics. The paper discusses the design of simple monetary and fiscal policy rules consistent with determinate equilibrium dynamics in the absence of Ricardian equivalence. Under this assumption, government debt turns into a relevant state variable which needs to be accounted for in the analysis of equilibrium dynamics. The key analytical finding is that without explicit reference to the level of government debt it is not possible to infer how strongly the monetary and fiscal instruments should be used to ensure determinate equilibrium dynamics. Specifically, we identify in our model discontinuities associated with threshold values of steady-state debt, leading to qualitative changes in the local determinacy requirements. These features extend the logic of Leeper (1991) to an environment in which fiscal policy is non-neutral. Naturally, this non-neutrality increases the importance of fiscal aspects for the design of policy rules consistent with determinate dynamics. JEL Classification: E52, E63fiscal regimes, monetary policy

Monetary and fiscal policy interactions in a New Keynesian model with capital accumulation and non-Ricardian consumers

This paper develops a small New Keynesian model with capital accumulation and government debt dynamics. The paper discusses the design of simple monetary and fiscal policy rules consistent with determinate equilibrium dynamics in the absence of Ricardian equivalence. Under this assumption, government debt turns into a relevant state variable which needs to be accounted for in the analysis of equilibrium dynamics. The key analytical finding is that without explicit reference to the level of government debt it is not possible to infer how strongly the monetary and fiscal instruments should be used to ensure determinate equilibrium dynamics. Specifically, we identify in our model discontinuities associated with threshold values of steady-state debt, leading to qualitative changes in the local determinacy requirements. These features extend the logic of Leeper (1991) to an environment in which fiscal policy is non-neutral and requires us to pay equal attention to to monetary and fiscal policy in designing policy rules consistent with determinate dynamics.

Protein logic: a statistical mechanical study of signal integration at the single-molecule level

Information processing and decision making is based upon logic operations, which in cellular networks has been well characterized at the level of transcription. In recent years however, both experimentalists and theorists have begun to appreciate that cellular decision making can also be performed at the level of a single protein, giving rise to the notion of protein logic. Here we systematically explore protein logic using a well known statistical mechanical model. As an example system, we focus on receptors which bind either one or two ligands, and their associated dimers. Notably, we find that a single heterodimer can realize any of the 16 possible logic gates, including the XOR gate, by variation of biochemical parameters. We then introduce the novel idea that a set of receptors with fixed parameters can encode functionally unique logic gates simply by forming different dimeric combinations. An exhaustive search reveals that the simplest set of receptors (two single-ligand receptors and one double-ligand receptor) can realize several different groups of three unique gates, a result for which the parametric analysis of single receptors and dimers provides a clear interpretation. Both results underscore the surprising functional freedom readily available to cells at the single-protein level.Comment: 19 pages, 4 figures and 9 pages S

Qualitative System Identification from Imperfect Data

Experience in the physical sciences suggests that the only realistic means of understanding complex systems is through the use of mathematical models. Typically, this has come to mean the identification of quantitative models expressed as differential equations. Quantitative modelling works best when the structure of the model (i.e., the form of the equations) is known; and the primary concern is one of estimating the values of the parameters in the model. For complex biological systems, the model-structure is rarely known and the modeler has to deal with both model-identification and parameter-estimation. In this paper we are concerned with providing automated assistance to the first of these problems. Specifically, we examine the identification by machine of the structural relationships between experimentally observed variables. These relationship will be expressed in the form of qualitative abstractions of a quantitative model. Such qualitative models may not only provide clues to the precise quantitative model, but also assist in understanding the essence of that model. Our position in this paper is that background knowledge incorporating system modelling principles can be used to constrain effectively the set of good qualitative models. Utilising the model-identification framework provided by Inductive Logic Programming (ILP) we present empirical support for this position using a series of increasingly complex artificial datasets. The results are obtained with qualitative and quantitative data subject to varying amounts of noise and different degrees of sparsity. The results also point to the presence of a set of qualitative states, which we term kernel subsets, that may be necessary for a qualitative model-learner to learn correct models. We demonstrate scalability of the method to biological system modelling by identification of the glycolysis metabolic pathway from data