ECONOMETRIC-PROCESS MODELS FOR INTEGRATED ASSESSMENT OF AGRICULTURAL PRODUCTION SYSTEMS

This paper develops the conceptual and empirical basis for a class of empirical economic production models that can be linked to site-specific bio-physical models for use in integrated assessment research. Site-specific data are used to estimate econometric production models, and these data and models are then incorporated into a simulation model that represents the decision making process of the farmer as a sequence of discrete or continuous land use and input use decisions. This discrete/continuous structure of the econometric process model is able to simulate decision making both within and outside the range of observed data in a way that is consistent with economic theory and with site-specific bio-physical constraints and processes. An econometric-process model of the dryland grain production system of the Northern Plains demonstrates the capabilities of this type of model.bio-physical models, integrated assessment, production models, dryland grain production, econometric-process models, Production Economics, C5, Q1, Q2,

Econometrics: A bird's eye view

As a unified discipline, econometrics is still relatively young and has been transforming and expanding very rapidly over the past few decades. Major advances have taken place in the analysis of cross sectional data by means of semi-parametric and non-parametric techniques. Heterogeneity of economic relations across individuals, firms and industries is increasingly acknowledge and attempts have been made to take them into account either by integrating out their effects or by modeling the sources of heterogeneity when suitable panel data exists. The counterfactual considerations that underlie policy analysis and treatment evaluation have been given a more satisfactory foundation. New time series econometric techniques have been developed and employed extensively in the areas of macroeconometrics and finance. Non-linear econometric techniques are used increasingly in the analysis of cross section and time series observations. Applications of Bayesian techniques to econometric problems have been given new impetus largely thanks to advances in computer power and computational techniques. The use of Bayesian techniques have in turn provided the investigators with a unifying framework where the tasks and forecasting, decision making, model evaluation and learning can be considered as parts of the same interactive and iterative process; thus paving the way for establishing the foundation of the "real time econometrics". This paper attempts to provide an overview of some of these developments

Econometric reduction theory and philosophy

Econometric reduction theory provides a comprehensive probabilistic framework for the analysis and classification of the reductions (simplifications) associated with empirical econometric models. However, the available approaches to econometric reduction theory are unable to satisfactory accommodate a commonplace theory of social reality, namely that the course of history is indeterministic, that history does not repeat itself and that the future depends on the past. Using concepts from philosophy this paper proposes a solution to these shortcomings, which in addition permits new reductions, interpretations and definitions

SPATIAL SEARCH IN COMMERCIAL FISHING: A DISCRETE CHOICE DYNAMIC PROGRAMMING APPROACH

We specify a discrete choice dynamic programming model of commercial fishing participation and location choices. This approach allows us to examine how fishermen collect information about resource abundance and whether their behavior is forward-looking.Resource /Energy Economics and Policy,

Substructure and Boundary Modeling for Continuous Action Recognition

This paper introduces a probabilistic graphical model for continuous action recognition with two novel components: substructure transition model and discriminative boundary model. The first component encodes the sparse and global temporal transition prior between action primitives in state-space model to handle the large spatial-temporal variations within an action class. The second component enforces the action duration constraint in a discriminative way to locate the transition boundaries between actions more accurately. The two components are integrated into a unified graphical structure to enable effective training and inference. Our comprehensive experimental results on both public and in-house datasets show that, with the capability to incorporate additional information that had not been explicitly or efficiently modeled by previous methods, our proposed algorithm achieved significantly improved performance for continuous action recognition.Comment: Detailed version of the CVPR 2012 paper. 15 pages, 6 figure