Crude Oil and Stock Markets: Stability, Instability, and Bubbles

We analyze the long-run relationship between the world price of crude oil and international stock markets over 1971:1-2008:3 using a cointegrated vector error correction model with additional regressors. Allowing for endogenously identified breaks in the cointegrating and error correction matrices, we find evidence for breaks after 1980:5, 1988:1, and 1999:9. We find a clear long-run relationship between these series for six OECD countries for 1971:1-1980.5 and 1988:2-1999.9, suggesting that stock market indices respond negatively to increases in the oil price in the long run. During 1980.6-1988.1, we find relationships that are not statistically significantly different from either zero or from the relationships of the previous period. The expected negative long-run relationship appears to disintegrate after 1999.9. This finding supports a conjecture of change in the relationship between real oil price and real stock prices in the last decade compared to earlier years, which may suggest the presence of several stock market bubbles and/or oil price bubbles since the turn of the century.crude oil, stock market prices, cointegrated VECM, structural stability, stock market bubble, oil price bubble

Nearctic \u3ci\u3eAcleris\u3c/i\u3e: Resurrection of \u3ci\u3eA. Stadiana\u3c/i\u3e and a Revised Identity for \u3ci\u3eA. Semiannula\u3c/i\u3e (Lepidoptera: Tortricidae)

Type study showed that Acleris stadiana (Barnes & Busck), currently considered a junior synonym of A. semiannula (Robinson), is in fact a distinct taxon. Although superficially similar, these taxa differ markedly in genital structure. In males of A. semiannula, the aedeagus is short, broad, and virtually straight, whereas in those of A. stadiana, it is long, thin, and sharply bent. What was known in literature as A. semiannula proved to be A. stadi­ana. We redefine both A. semiannula and the resurrected A. stadiana

ENTROPY-BASED ESTIMATION AND INFERENCE IN BINARY RESPONSE MODELS UNDER ENDOGENEITY

This paper considers estimation and inference for the binary response model in the case where endogenous variables are included as arguments of the unknown link function. Semiparametric estimators are proposed that avoid the parametric assumptions underlying the likelihood approach as well as the loss of precision when using nonparametric estimation. Suggestions are made for how the utility maximization decision model can be altered to permit attributes to vary across alternatives.Research Methods/ Statistical Methods,

RISK-RETURN ANALYSIS OF INCORPORATING ANNUAL LEGUMES AND LAMB GRAZING WITH DRYLAND CROP ROTATIONS

Profitability and risk, 1988-2001, are examined for lamb-grazed field pea as a fallow alternative with wheat, or an extended wheat-sunflower-millet rotation. Switching from conventional wheat-fallow to an extended rotation with grazed-peas increases profitability (2.3% to 7.3%), and reduces risk (below 0% target in only 2 versus 7 of 14 years).Crop Production/Industries,

Disaster Planning and Management

Recent events such as hurricanes, tsunamis, earthquakes, power outages, and the threat of pandemics have highlighted our vulnerability to natural disasters. This vulnerability is exacerbated by many organizations\u27 increasing dependence on computer, telecommunications, and other technologies, and trends toward integrating suppliers and business partners into everyday business operations. In response many organizations are implementing disaster recovery planning processes. In this paper we discuss how to identify threats and scenarios; how to articulate the disaster recovery strategies; and four elements of the generic disaster recovery plan: Mitigation, preparedness, response, and recovery. We then provide examples of software that can help disaster recovery professionals in the planning and implementation process. Finally we present some trends that will reinforce the criticality of the issue

Book Reviews

Book reviews by Joseph J. Miller, William J. Syring, Richard F. Swisher, Ronald P. Rejent, James H. Graham, Jr., Daniel C. O\u27Grady, Theodore Stensland, James H. Neu, and Lawrence J. Petroshius

Biobehavioral Indices of Emotion Regulation Relate to School Attitudes, Motivation, and Behavior Problems in a Low-Income Preschool Sample

Effective emotion regulation may promote resilience and preschool classroom adjustment by supporting adaptive peer interactions and engagement in learning activities. We investigated how hypothalamus-pituitary-adrenal axis (HPA) regulation, cardiac reactivity, and classroom emotion displays related to adjustment among low-income preschoolers attending Head Start. A total of 62 four-year-olds completed a laboratory session including a baseline soothing video; emotion-eliciting slides/video clips, and recovery. Salivary cortisol, heart rate, and vagal tone were measured throughout. Independent coders used handheld computers to observe classroom emotion expression/regulation. Teachers rated child motivation, persistence/attention, learning attitudes, and internalizing/externalizing symptoms. Results reveal associations between biobehavioral markers of regulatory capacity and early school adjustment.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/73847/1/annals.1376.043.pd

Homogeneous Nucleation Rate Measurements for Water Over a Wide Range of Temperature and Nucleation Rate

An expansion cloud chamber was used to measure the homogeneous nucleation rate for water over a wide range of temperature from 230-290 K and nucleation rates of 1-106 drops cm-3 s-1. The comprehensive and extensive nature of this data allows a much more detailed comparison between theory and experiment than has previously been possible. The expansion chamber technique employs continuous pressure measurement and an adiabatic pulse of supersaturation to give the time history of supersaturation and temperature during the nucleation. The resulting drop concentration is determined using photographic techniques. The experimental observations are presented in tabular form and from them an empirical nucleation rate formula is determined: J=S2 exp[328.124-5.582 43T+0.030 365T2-5. 0319E-5T3-(999.814-4.100 87T+3.010 84E-3 T2)ln -2S], where J is the nucleation rate in units of drops cm -1 s-1 is the supersaturation ratio and T is the temperature in K