Looking for Cattle and Hog Cycles through a Bayesian Window

The agricultural economics literature, both academic and trade, has discussed the assumed presence of cycles in livestock markets such as cattle and hogs for a very long time. Since Jarvis (1974), there has been considerable discussion over how these cycles impact optimal economic decision making. Subsequent studies such as Rucker, Burt, and LaFrance (1984), Hayes and Schmitz (1987), Foster and Burt (1992), Rosen, Murphy, and Scheinkman (1994), and Hamilton and Kastens (2000) have all investigated some aspect of how biological factors, economic events, or economic actions could be causes of and/or responses to cycles in hog and cattle inventories. There has also been debate, again both in the academic and trade literature, over the length of the cycle(s) present in hog and cattle stocks. To provide both academics and producers with accurate information on the number and periods of cycles that might be present in hog and cattle inventories, this paper provides a purely statistical view of the matter. Using over 140 years of annual data on cattle and hog inventory levels, we estimate Bayesian autoregressive, trend-stationary models on cattle inventories, hog inventories, and the growth rate of cattle inventories. We then use those models to find the posterior distributions of both the number of cycles present in each series and the period lengths of those cycles. We find multiple cycles present in all three series. Cattle inventory results show clear evidence in favor of 4.5, 6, and 11 year cycles with other cycles present but not as clearly identified. Hog inventory results identify five cycles with periods of approximately 4.5, 5.4, 6.8, 10 and 13 years. The data on the growth rate in cattle stocks has similar cycles to the series on the stock levels.Bayesian econometrics, cattle cycles, hog cycles., Agribusiness, Livestock Production/Industries, Production Economics,

Propagating waves in an extremal black string

We investigate the black string in the context of the string theories. It is shown that the graviton is the only propagating mode in the (2+1)--dimensional extremal black string background. Both the dilation and axion turn out to be non-propagating modes.Comment: Minor corrections, 11 pages in ReVTeX, no figure

Thermodynamic duality between RN black hole and 2D dilaton gravity

All thermodynamic quantities of the Reissner-Nordstr\"om (RN) black hole can be obtained from the dilaton and its potential of two dimensional (2D) dilaton gravity. The dual relations of four thermodynamic laws are also established. Furthermore, the near-horizon thermodynamics of the extremal RN black hole is completely described by the Jackiw-Teitelboim theory which is obtained by perturbing around the AdS$_2$-horizon.Comment: 10 pages, 3 figures, version accepted by MPL

Nonpropagation of massive mode on AdS2 in topologically massive gravity

Making use of Achucarro-Ortiz (AO) type of dimensional reduction, we study the topologically massive gravity with a negative cosmological constant on AdS2 spacetimes. For a constant dilaton, this two-dimensional model also admits three AdS2 vacuum solutions, which are related to two AdS3 and warped AdS3 backgrounds with an identification upon uplifting three dimensions. We carry out the perturbation analysis around these backgrounds to find what is a physically propagating field. However, it turns out that there is no propagating massive mode on AdS2 background, in contrast to the Kaluza-Klein (KK) type of dimensional reduction. We note that two dimensionally reduced actions are different and thus, the non-equivalence of their on-shell amplitudes is obtained.Comment: 19 pages, version to appear in EPJ

Double-Well Potential : The WKB Approximation with Phase Loss and Anharmonicity Effect

We derive a general WKB energy splitting formula in a double-well potential by incorporating both phase loss and anharmonicity effect in the usual WKB approximation. A bare application of the phase loss approach to the usual WKB method gives better results only for large separation between two potential minima. In the range of substantial tunneling, however, the phase loss approach with anharmonicity effect considered leads to a great improvement on the accuracy of the WKB approximation.Comment: 14 pages, revtex, 1 figure, will appear at Phys. Rev.

The absence of the Kerr black hole in the Ho\v{r}ava-Lifshitz gravity

We show that the Kerr metric does not exist as a fully rotating black hole solution to the modified Ho\v{r}ava-Lifshitz (HL) gravity with $\Lambda_W=0$ and $\lambda=1$ case. We perform it by showing that the Kerr metric does not satisfy full equations derived from the modified HL gravity.Comment: 35 pages, no figure

A Bayesian Committee Machine Potential for Organic Nitrogen Compounds

Large-scale computer simulations of chemical atoms are used in a wide range of applications, including batteries, drugs, and more. However, there is a problem with efficiency as it takes a long time due to the large amount of calculation. To solve these problems, machine learning interatomic potential (ML-IAP) technology is attracting attention as an alternative. ML-IAP not only has high accuracy by faithfully expressing the density functional theory (DFT), but also has the advantage of low computational cost. However, there is a problem that the potential energy changes significantly depending on the environment of each atom, and expansion to a wide range of compounds within a single model is still difficult to build in the case of a kernel-based model. To solve this problem, we would like to develop a universal ML-IAP using this active Bayesian Committee Machine (BCM) potential methodology for carbon-nitrogen-hydrogen (CNH) with various compositions. ML models are trained and generated through first-principles calculations and molecular dynamics simulations for molecules with only CNH. Using long amine structures to test an ML model trained only with short chains, the results show excellent consistency with DFT calculations. Consequently, machine learning-based models for organic molecules not only demonstrate the ability to accurately describe various physical properties but also hold promise for investigating a broad spectrum of diverse materials systems