Phase Space Reconstruction from Economic Time Series Data: Improving Models of Complex Real-World Dynamic Systems

Failure of economic models to anticipate the global financial crisis illustrates the need for modeling to better capture complex real-world dynamics. Conventional models—in which economic variables evolve toward equilibria or fluctuate about equilibria in response to exogenous random shocks—are ill-equipped to portray complex real-world dynamics in which economic variables may cycle aperiodically along low-dimensional ‘strange attractors’. We present a method developed in the physics literature—‘phase space reconstruction’—that reconstructs strange attractors present in real-world dynamical systems using time series data on a single variable. Phase space reconstruction provides pictures of real-world dynamics that can guide model specificationphase space reconstruction, time series data, economic dynamics, Agribusiness, Agricultural and Food Policy, Food Consumption/Nutrition/Food Safety, Food Security and Poverty, Production Economics, Risk and Uncertainty,

Impacts of Biofuels on Water Supply: Proposed Cures May Worsen the Disease

water, conservation, biofuels, irrigation, Resource /Energy Economics and Policy, Q25, Q48,

Phase Space Reconstruction from Time Series Data: Where History Meets Theory

In ‘dissipative’ dynamical systems, variables evolve asymptotically toward lowdimensional ‘attractors’ that define their dynamical properties. Unfortunately, real‐world dynamical systems are generally too complex for us to directly observe these attractors. Fortunately, there is a method—‘phase space reconstruction’—that can be used to indirectly detect attractors in real‐world dynamical systems using time series data on a single variable (Broomhead and King, 1985; Schaffer and Kott, 1985; Kott et al, 1988; Williams,1997). Armed with this knowledge, we can formulate more accurate and informative models of real‐world dynamical systems

Phase Space Reconstruction from Econommic Time Series Data: Improving Models of Complex Real-World Dynamic Systems

 Failure of economic models to anticipate the global financial crisis illustrates the need for modeling to better capture complex real-world dynamics. Conventional models—in which economic variables evolve toward equilibria or fluctuate about equilibria in response to exogenous random shocks—are ill-equipped to portray complex real-world dynamics in which economic variables may cycle aperiodically along low-dimensional ‘strange attractors’. We present a method developed in the physics literature—‘phase space reconstruction’—that reconstructs strange attractors present in real-world dynamical systems using time series data on a single variable. Phase space reconstruction provides pictures of real-world dynamics that can guide model specification

DAIRY DEREGULATION AND LOW-INPUT DAIRY PRODUCTION: A BIOECONOMIC EVALUATION

Deregulation of the Australian dairy industry could affect the utilization of resources by milk producers and the profitability of dairy production. In this study we examine the feed mix that dairy producers use, both pastures and supplements, under partial and total deregulation. We are particularly interested in the interaction of pasture utilization and farm profitability. The results of this research demonstrate that profitable low-input dairy is constrained by the most limiting resource, feed supplied by pasture, and that the interactions between economic and biological processes are critical to farm profitability.Agricultural and Food Policy, Production Economics,

OPTIMAL CONTROL OF PEST RESISTANCE TO TRANSGENIC CROP VARIETIES

Transgenic corn varieties entered the market in 1996. These plant varieties carry a gene from the soil bacterium Bacillus thuringiensis kurstaki, Bt, that makes the plant produce a toxin deadly to the pest insect European Corn Borer (ECB) Ostrinia nubilalis (Hübner). Since ECB may build up genetic resistance to this toxin, the growers of transgenic corn varieties are required to plant a portion of their field (refuge) with regular corn. This requirement is expected to prolong the efficiency of Bt corn in combating the ECB because some non-resistant pests can survive in the refuge, and thereby dilute the build-up of resistance in the overall pest population. A fixed refuge size of 20 percent is the currently recommended "rule-of-thumb" by the Environmental Protection Agency (EPA). Past work has searched for an economically-optimal refuge size utilizing discrete-time simulation approaches in which refuge size is treated as an exogenous parameter whose optimal value is found through numerical iteration. The objective of this work is to fine-tune parametric refuge specifications by formulating a bioeconomic model capable of endogenously determining the optimal trajectory of refuge sizes over time via an analytical optimal-control rule. The model will provide novel comparative statics/dynamics results demonstrating the sensitivity of the optimal trajectory to important economic and biological parameters.Crop Production/Industries,

DYNAMICS OF OPTIMAL INTERACTIONS BETWEEN PASTURE PRODUCTION AND MILK YIELDS OF AUSTRALIAN DAIRY FARMS

Deregulation of the Australian dairy industry could effect the utilization of resources by milk producers. In this study we examine the feed input mix dairy producers use, both pastures and supplements, prior to and after deregulation. We are particularly interested in the interaction of pasture utilization and farm profitability.dairy production, pasture utilization, deregulation, Land Economics/Use, Livestock Production/Industries,

Reconstructing systematic persistent impacts of promotional marketing with empirical nonlinear dynamics

An empirical question of long-standing interest is how price promotions affect a brand’s sale shares in the fast-moving consumer-goods market. We investigated this question with concurrent promotions and sales records of specialty beer brands pooled over Tesco stores in the UK. Most brands were continuously promoted, rendering infeasible a conventional approach of establishing impact against an off-promotion sales baseline, and arguing in favor of a dynamics approach. Moreover, promotion/sales records were volatile without easily-discernable regularity. Past work conventionally attributed volatility to the impact of exogenous random shocks on stable markets, and reasoned that promotions have only an ephemeral impact on sales shares in stationary mean-reverting stochastic markets, or a persistent freely-wandering impact in nonstationary markets. We applied new empirical methods from the applied sciences to uncover an overlooked alternative: ‘systematic persistence’ in which promotional impacts evolve systematically in an endogenously-unstable market governed by deterministic-nonlinear dynamics. We reconstructed real-world market dynamics from the Tesco dataset, and detected deterministic-nonlinear market dynamics. We used reconstructed market dynamics to identify a complex network of systematic interactions between promotions and sales shares among competing brands, and quantified/ characterized the dynamics of these interactions. For the majority of weeks in the study, we found that: (1) A brand’s promotions drove down own sales shares (a possibility recognized in the literature), but ‘cannibalized’ sales shares of competing brands (perhaps explaining why brands were promoted despite a negative marginal impact on own sales shares); and (2) Competitive interactions between brands owned by the same multinational brewery differed from those with outside brands. In particular, brands owned by the same brewery enjoyed a ‘mutually-beneficial’ relationship in which an incremental increase in the sales share of one marginally increased the sales share of the other. Alternatively, the sales shares of brands owned by different breweries preyed on each other’s market shares

Economic Dynamics of the German Hog-Price Cycle

We investigated the economic dynamics of the German hog-price cycle with an innovative ‘diagnostic’ modeling approach. Hog-price cycles are conventionally modeled stochastically—most recently as randomly-shifting sinusoidal oscillations. Alternatively, we applied Nonlinear Time Series analysis to empirically reconstruct a deterministic, low-dimensional, and nonlinear attractor from observed hog prices. We next formulated a structural (explanatory) model of the pork industry to synthesize the empirical hog-price attractor. Model simulations demonstrate that low price-elasticity of demand contributes to aperiodic price cycling – a well know result – and further reveal two other important driving factors: investment irreversibility (caused by high specificity of technology), and liquidity-driven investment behavior of German farmers

Explaining the German hog price cycle: A nonlinear dynamics approach

We investigated German hog-price dynamics with an innovative ‘diagnostic’ modeling approach. Hog-price cycles are conventionally modeled stochastically—most recently as randomly-shifting sinusoidal oscillations. Alternatively, we applied nonlinear time series analysis to empirically reconstruct a deterministic, low-dimensional, and nonlinear attractor from observed hog prices. We next formulated a structural (explanatory) model of the pork industry to synthesize the empirical hog-price attractor. Model simulations demonstrate that low price-elasticity of demand contributes to aperiodic price cycling – a well know result – and further reveal two other important driving factors: investment irreversibility (caused by high specificity of technology), and liquidity-driven investment behavior of German farmers