1,721,036 research outputs found
A Nonlinear Dynamics Approach to Evaluating the 'Realism' of Food Systems Models
No full textHow can theoretical market models—which necessarily abstract from reality—satisfy demands for realism
when used to support high-stakes food policy? Past work concludes that modelers can be reasonably required
to demonstrate the ‘degree of correspondence’ between a model and reality, but leaves open the question of
how to demonstrate correspondence. We suggest that correspondence be demonstrated by requiring modelers
to produce persuasive empirical evidence of real-world market dynamics that their models skillfully
reproduce. Real-world market dynamics are masked in volatile observed prices. Agricultural economists
conventionally attribute price volatility to exogenous random shocks that can be modeled with linear
stochastic approaches, but there is increasing recognition that price volatility also may be generated
endogenously by nonlinear market dynamics. Selecting between these competing explanations for market
instability matters in food policy because they present policymakers very different surrogate realities with
divergent policy implications. We propose pre-modeling application of Nonlinear Time Series analysis to
distinguish between linear and nonlinear dynamic structure in observed price data, and provide a framework
guiding its sound application. Price data testing positive for nonlinear dynamic structure provides evidence
that observed market volatility may be explained with parsimonious nonlinear specifications. Alternatively,
price data testing negative for nonlinear dynamics provides evidence that linear stochastic approaches may
better model observed volatility
Distinguishing between Endogenous and Exogenous Price Volatility in Food Security Assessment: An Empirical Nonlinear Dynamics Approach
No full textWe propose an empirical scheme—based on nonlinear dynamics—for diagnosing real-world market dynamics from observed price series data.
The scheme distinguishes between endogenous and exogenous volatility in observed price series, tests whether endogenous volatility is generated by low-dimensional deterministic market dynamics, simulates these dynamics with a phenomenological market model, and models extreme volatility probabilistically. These diagnostics allow policymakers to make an empirically-informed determination of whether laissez-faire or interventionist policies are most promising in reducing price volatility in particular cases. We apply the diagnostic scheme to provide compelling empirical evidence that observed volatility in organic apple, pear, orange, and lemon prices at the Milano (Italy) Ipercoop is due to endogenous market dynamics governed by low-dimensional nonlinear behavior. The implication for food policy is that this inherently unstable market cannot be relied upon to systematically stabilize observed price volatility from random exogenous shocks. There may be scope for public interventions targeted to increasing the flexibility of organic fruit producers in responding to changing market conditions
A systematic review on price volatility in agriculture
Full text linkThe recent extreme volatility in agriculture prices determines serious repercussions to various stakeholders and levels in the food value chain, that is, producers, intermediaries, and customers, at micro-, meso- and macro-economic governance levels, respectively. Persistent high/low degree of agriculture prices leads to unsustainable production/consumption patterns, thus representing an impediment to reaching the goal of responsible consumption and production (UN-SDGs 12). The lack of comprehensive real-time information on price volatility's internal and external factors often resulted in an inconclusive and counterintuitive outcome while performing empirical estimation. The present review was conducted using the PRISMA framework to systematically identify and analyze literature from two important databases. Papers have been grouped by volatility drivers, governance levels, theoretical approaches, and background data types. The present review is a valuable starting point for understanding the links between multi-dimensional factors affecting the persistent price volatility and the theoretical and empirical analytics trends to provide the computational advancement needed to cope with model estimation issues. It also highlights the importance of a paradigm shift in researching agriculture price volatility to addressing food crises, considering principal objectives and perspectives such as food security, poverty alleviation, sustainability in food value chains, and resilience of food systems across the globe
Reconstructing deterministic economic dynamics from volatile time series data
No full textEconomists conventionally attribute observed volatility in economic time series data to exogenous random shocks that agitate otherwise stable real-world markets; and consequently, model volatility with a variety of linear-stochastic and probabilistic methods. However, some economists have recognized another possible explanation for volatility: Markets may be intrinsically unstable, and we might be able to model attending volatility parsimoniously with low-dimensional, nonlinear, deterministic dynamic models without resorting to stochastic inputs. Whether observed volatility is generated by inherently stable or unstable markets has serious policy implications. Will laissez-faire policies suffice to dampen volatility because markets are self-correcting, or are interventionist policies required? This chapter introduces nonlinear time series analysis (NLTS)—a collection of methods developed in mathematical physics to diagnose the source of real-world volatility from observed time series data. Depending on data quality, economists can potentially use NLTS to reconstruct phase-space market dynamics and extract equations of motion from a single price series
Nonlinear time series analysis with R
No full textIn the process of data analysis, the investigator is often facing highly-volatile and random-appearing observed data. A vast body of literature shows that the assumption of underlying stochastic processes was not necessarily representing the nature of the processes under investigation and, when other tools were used, deterministic features emerged. Non Linear Time Series Analysis (NLTS) allows researchers to test whether observed volatility conceals systematic non linear behavior, and to rigorously characterize governing dynamics. Behavioral patterns detected by non linear time series analysis, along with scientific principles and other expert information, guide the specification of mechanistic models that serve to explain real-world behavior rather than merely reproducing it. Often there is a misconception regarding the complexity of the level of mathematics needed to understand and utilize the tools of NLTS (for instance Chaos theory). However, mathematics used in NLTS is much simpler than many other subjects of science, such as mathematical topology, relativity or particle physics. For this reason, the tools of NLTS have been confined and utilized mostly in the fields of mathematics and physics. However, many natural phenomena investigated I many fields have been revealing deterministic non linear structures. In this book we aim at presenting the theory and the empirical of NLTS to a broader audience, to make this very powerful area of science available to many scientific areas. This book targets students and professionals in physics, engineering, biology, agriculture, economy and social sciences as a textbook in Nonlinear Time Series Analysis (NLTS) using the R computer language
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Judicial resolution of resource-use conflicts arising from sedimentation management in dam/reservoir projects A law and economics approach
Full text linkThe loss of the world’s reservoir capacity to sedimentation is catalyzing a paradigm shift toward managing dam/reservoir projects as renewable resources. This requires altering dam operations to stabilize storage capacity by releasing sediment downstream. Legal uncertainty regarding whether dam owners are liable for damages to surrounding property interests due to altered dam operations provides a significant disincentive for dam owners to engage in sustainable sedimentation management. We formulate an analytical framework that considers who courts should entitle, or give a right to prevail, to generate the greatest social-economic benefits from sedimentation management
Judicial resolution of resource-use conflicts arising from sedimentation management in dam
No full textThesis (M.A.), Agricultural Economics, Washington State UniversityDepartment of Agricultural Economics, Washington State Universit
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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Phase space reconstruction methods in applied economics and econometrics
Full text linkMarket responses to unpredictable events such as preference change, food contamination, or changes in technology and information are not always known. Phase space reconstruction, a tool designed to analyze nonlinear time series, is investigated for use as an econometric tool to detect nonlinear dynamics economic time series. It is applied to examine consumer responses to unpredictable events, changes in dynamic livestock cycles, and nonlinear structure in regression residuals. The empirical application of phase space reconstruction analyzing economic behavior demonstrates an intuitive, appealing, and straightforward demonstration as to the use of this diagnostic tool. The first essay investigates how to reconstruct dynamic consumer reactions from market events using phase space reconstruction. This approach can provide important and unique empirical insights into consumer reactions to product recall or contaminant events. We apply phase space reconstruction analysis to U.S. meat demand, demonstrating distinct differences between intertemporal shorter run impacts from food safety incidents (e.g., E. Coli and BSE) relative to longer run health effects (e.g., cholesterol). Moreover, we show that consumers have reacted to food safety events differently depending on the particular food contaminate associated with that event. In the second essay, phase space reconstruction is investigated as a diagnostic tool for determining the structure of detected nonlinear processes in regression residuals. Empirical evidence supporting this approach is provided using simulations from an Ikeda mapping and the S&P 500. Results in the form of phase portraits (e.g., scatter plots of reconstructed dynamical systems) provide qualitative information to discern structural components from apparent randomness and provide insights categorizing structural components into functional classes to enhance econometric/time series modeling efforts. The third essay applies the technique of phase space reconstruction to investigate U.S. livestock cycles. Results are presented for both pork and cattle cycles, providing empirical evidence that the cycles themselves have slowly diminished. By comparing the two livestock cycles important insights can be made. The phase space analysis suggests that the biological constraint has become a less significant factor in livestock cycles while technology and information are relatively more significant
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