The New Keynesian Monetary Model: Does it Show the Comovement Between Output and Inflation in the U.S.?

Published as article in: Journal of Economic Dynamics and Control (2008), 32(May), pp. 1466-1488.

User’s Guide to ADMB2R: A Set of AD Model Builder Output Routines Compatible with the R Statistics Language

ADMB2R is a collection of AD Model Builder routines for saving complex data structures into a file that can be read in the R statistics environment with a single command.1 ADMB2R provides both the means to transfer data structures significantly more complex than simple tables, and an archive mechanism to store data for future reference. We developed this software because we write and run computationally intensive numerical models in Fortran, C++, and AD Model Builder. We then analyse results with R. We desired to automate data transfer to speed diagnostics during working-group meetings. We thus developed the ADMB2R interface to write an R data object (of type list) to a plain-text file. The master list can contain any number of matrices, values, dataframes, vectors or lists, all of which can be read into R with a single call to the dget function. This allows easy transfer of structured data from compiled models to R. Having the capacity to transfer model data, metadata, and results has sharply reduced the time spent on diagnostics, and at the same time, our diagnostic capabilities have improved tremendously. The simplicity of this interface and the capabilities of R have enabled us to automate graph and table creation for formal reports. Finally, the persistent storage in files makes it easier to treat model results in analyses or meta-analyses devised months—or even years—later. We offer ADMB2R to others in the hope that they will find it useful. (PDF contains 30 pages)

Estimating input demand and output supply elasticities in gillnet and seine fishing in Guimaras Strait and adjacent waters

This contribution is a summary of the results of the study conducted by the University of the Philippines in the Visayas team from November 1990 to June 1991. The purpose of this research is to estimate demand and output supply elasticities in gillnet and seine fishing in Guimaras Strait (Philippines) and adjacent waters.

Integrated conceptual ecosystem model development for the Florida Keys/Dry Tortugas coastal marine ecosystem: MARine Estuarine goal Setting (MARES) for South Florida

The overall goal of the MARine and Estuarine goal Setting (MARES) project for South Florida is “to reach a science-based consensus about the defining characteristics and fundamental regulating processes of a South Florida coastal marine ecosystem that is both sustainable and capable of providing the diverse ecosystem services upon which our society depends.” Through participation in a systematic process of reaching such a consensus, science can contribute more directly and effectively to the critical decisions being made by both policy makers and by natural resource and environmental management agencies. The document that follows briefly describes the MARES project and this systematic process. It then describes in considerable detail the resulting output from the first two steps in the process, the development of conceptual diagrams and an Integrated Conceptual Ecosystem Model (ICEM) for the first subregion to be addressed by MARES, the Florida Keys/Dry Tortugas (FK/DT). What follows with regard to the FK/DT is the input received from more than 60 scientists, agency resource managers, and representatives of environmental organizations beginning with a workshop held December 9-10, 2009 at Florida International University in Miami, Florida.

Integrated conceptual ecosystem model development for the southeast Florida coastal marine ecosystem: MARine Estuarine goal Setting (MARES) for South Florida

The overall goal of the MARES (MARine and Estuarine goal Setting) project for South Florida is “to reach a science-based consensus about the defining characteristics and fundamental regulating processes of a South Florida coastal marine ecosystem that is both sustainable and capable of providing the diverse ecosystem services upon which our society depends.” Through participation in a systematic process of reaching such a consensus, science can contribute more directly and effectively to the critical decisions being made both by policy makers and by natural resource and environmental management agencies. The document that follows briefly describes MARES overall and this systematic process. It then describes in considerable detail the resulting output from the first step in the process, the development of an Integrated Conceptual Ecosystem Model (ICEM) for the third subregion to be addressed by MARES, the Southeast Florida Coast (SEFC). What follows with regard to the SEFC relies upon the input received from more than 60 scientists, agency resource managers, and representatives of environmental organizations during workshops held throughout 2009–2012 in South Florida.

Integrated conceptual ecosystem model development for the southwest Florida shelf coastal marine ecosystem: MARine Estuarine goal Setting (MARES) for South Florida

The overall goal of the MARine and Estuarine goal Setting (MARES) project for South Florida is “to reach a science-based consensus about the defining characteristics and fundamental regulating processes of a South Florida coastal marine ecosystem that is both sustainable and capable of providing the diverse ecosystem services upon which our society depends.” Through participation in a systematic process of reaching such a consensus, science can contribute more directly and effectively to the critical decisions being made by both policy makers and by natural resource and environmental management agencies. The document that follows briefly describes the MARES project and this systematic process. It then describes in considerable detail the resulting output from the first two steps in the process, the development of conceptual diagrams and an Integrated Conceptual Ecosystem Model (ICEM) for the second subregion to be addressed by MARES, the Southwest Florida Shelf (SWFS). What follows with regard to the SWFS is the input received from more than 60 scientists, agency resource managers, and representatives of environmental organizations beginning with a workshop held August 19-20, 2010 at Florida Gulf Coast University in Fort Myers, Florida.

Modified Kanerva model. Results for real time word recognition

This paper describes results obtained using the modified Kanerva model to perform word recognition in continuous speech after being trained on the multi-speaker Alvey 'Hotel' speech corpus. Theoretical discoveries have recently enabled us to increase the speed of execution of part of the model by two orders of magnitude over that previously reported by Prager & Fallside. The memory required for the operation of the model has been similarly reduced. The recognition accuracy reaches 95% without syntactic constraints when tested on different data from seven trained speakers. Real time simulation of a model with 9,734 active units is now possible in both training and recognition modes using the Alvey PARSIFAL transputer array. The modified Kanerva model is a static network consisting of a fixed nonlinear mapping (location matching) followed by a single layer of conventional adaptive links. A section of preprocessed speech is transformed by the non-linear mapping to a high dimensional representation. From this intermediate representation a simple linear mapping is able to perform complex pattern discrimination to form the output, indicating the nature of the speech features present in the input window.

Gaussian Process training with input noise

In standard Gaussian Process regression input locations are assumed to be noise free. We present a simple yet effective GP model for training on input points corrupted by i.i.d. Gaussian noise. To make computations tractable we use a local linear expansion about each input point. This allows the input noise to be recast as output noise proportional to the squared gradient of the GP posterior mean. The input noise variances are inferred from the data as extra hyperparameters. They are trained alongside other hyperparameters by the usual method of maximisation of the marginal likelihood. Training uses an iterative scheme, which alternates between optimising the hyperparameters and calculating the posterior gradient. Analytic predictive moments can then be found for Gaussian distributed test points. We compare our model to others over a range of different regression problems and show that it improves over current methods.

Output feedback H2 model matching for decentralized systems with delays

This paper gives a new solution to the output feedback H2 model matching problem for a large class of delayed information sharing patterns. Existing methods for similar problems typically reduce the decentralized problem to a centralized problem of higher state dimension. In contrast, this paper demonstrates that the decentralized model matching solution can be constructed from the original centralized solution via quadratic programming. © 2013 AACC American Automatic Control Council.