Performance Evaluation of the New Connecticut Leading Employment Index Using Lead Profiles and BVAR Models

Dua and Miller (1996) created leading and coincident employment indexes for the state of Connecticut, following Moore's (1981) work at the national level. The performance of the Dua-Miller indexes following the recession of the early 1990s fell short of expectations. This paper performs two tasks. First, it describes the process of revising the Connecticut Coincident and Leading Employment Indexes. Second, it analyzes the statistical properties and performance of the new indexes by comparing the lead profiles of the new and old indexes as well as their out-of-sample forecasting performance, using the Bayesian Vector Autoregressive (BVAR) method. The new indexes show improved performance in dating employment cycle chronologies. The lead profile test demonstrates that superiority in a rigorous, non-parametric statistic fashion. The mixed evidence on the BVAR forecasting experiments illustrates the truth in the Granger and Newbold (1986) caution that leading indexes properly predict cycle turning points and do not necessarily provide accurate forecasts except at turning points, a view that our results support.

The Time-Series Properties on Housing Prices: A Case Study of the Southern California Market

We examine the time-series relationship between housing prices in eight Southern California metropolitan statistical areas (MSAs). First, we perform cointegration tests of the housing price indexes for the MSAs, finding seven cointegrating vectors. Thus, the evidence suggests that one common trend links the housing prices in these eight MSAs, a purchasing power parity finding for the housing prices in Southern California. Second, we perform temporal Granger causality tests revealing intertwined temporal relationships. The Santa Anna MSA leads the pack in temporally causing housing prices in six of the other seven MSAs, excluding only the San Luis Obispo MSA. The Oxnard MSA experienced the largest number of temporal effects from other MSAs, six of the seven, excluding only Los Angeles. The Santa Barbara MSA proved the most isolated in that it temporally caused housing prices in only two other MSAs (Los Angels and Oxnard) and housing prices in the Santa Anna MSA temporally caused prices in Santa Barbara. Third, we calculate out-of-sample forecasts in each MSA, using various vector autoregressive (VAR) and vector error-correction (VEC) models, as well as Bayesian, spatial, and causality versions of these models with various priors. Different specifications provide superior forecasts in the different MSAs. Finally, we consider the ability of theses time-series models to provide accurate out-of-sample predictions of turning points in housing prices that occurred in 2006:Q4. Recursive forecasts, where the sample is updated each quarter, provide reasonably good forecasts of turning points.

Extracting more out of relocation data: building movement models as mixtures of random walks

We present a framework for fitting multiple random walks to animal movement paths consisting of ordered sets of step lengths and turning angles. Each step and turn is assigned to one of a number of random walks, each characteristic of a different behavioral state. Behavioral state assignments may be inferred purely from movement data or may include the habitat type in which the animals are located. Switching between different behavioral states may be modeled explicitly using a state transition matrix estimated directly from data, or switching probabilities may take into account the proximity of animals to landscape features. Model fitting is undertaken within a Bayesian framework using the WinBUGS software. These methods allow for identification of different movement states using several properties of observed paths and lead naturally to the formulation of movement models. Analysis of relocation data from elk released in east-central Ontario, Canada, suggests a biphasic movement behavior: elk are either in an "encamped" state in which step lengths are small and turning angles are high, or in an "exploratory" state, in which daily step lengths are several kilometers and turning angles are small. Animals encamp in open habitat (agricultural fields and opened forest), but the exploratory state is not associated with any particular habitat type.

Locus of control as a predictor of students dropping out of high school

Education is related to health. In cross-sectional data, education level has been associated with physical functioning. Also, lower levels of education have been associated with health behaviors including smoking, alcohol use, and greater body weight. In school, students may benefit from greater exposed to health-related messages, while students who have dropped out may be more susceptible to influences regarding negative health behaviors such as smoking. ^ Improved school retention might improve long-term health outcomes. However, there is limited evidence regarding modifiable factors that predict likelihood of dropping out. Two likely psychosocial measures are locus of control and parent-child academic conversations. In the current study, data from two waves of a population-based longitudinal survey, the National Education Longitudinal Survey, were utilized to evaluate whether these two psychosocial measures could predict likelihood of dropping out, for students (n = 16,749) in tenth grade at 1990, with dropout status determined at 1992, while controlling for recognized sociodemographic predictors including parental income, parental education level, race/ethnicity, and sex. Locus of control was measured with the Pearlin Mastery Scale, and parent-child academic conversations were measured by three questions concerning course selection at school, school activities and events, and things the student studied in class. ^ In a logistic regression model, with the sociodemographic control measures entered in a first step before entry of the psychosocial measures in a second step, this study determined that lower levels of locus of control were associated with greater likelihood of dropping out after two years (odds ratio (OR) = 1.11, 95% confidence interval (CI) 108 to 1.15, p < .001), and two of the three parent-child academic discussion items were associated with greater likelihood of dropping out after two years (OR = 1.69, CI 1.48-1.93, p < .001; OR = 1.22, CI 1.05-1.41, p = .01; OR = 1.01, CI .88-1.15, p = .94). ^ It is possible that interventions aimed at improving locus of control, and aimed at building parent-child academic conversations, could lower the likelihood of students dropping out, and this in turn could yield improved heath behaviors and health status in the child's future. ^

High-risk drug use and sexual behaviors among out-of-treatment drug users: An aging and life course perspective

High-risk injection drug use and the sexual behaviors that accompany it have large social and financial costs. Tailored treatments have been shown to successfully reduce high-risk behaviors. However, little is known about how age and age at first drug use are related to high-risk injection or sex behaviors. The current study draws on life course theory and hypothesizes that age will have a strong relationship with high-risk behaviors of out-of-treatment drug users. Data from the NIDA Cooperative Agreement was used to analyze the relationship between (1) age, and (2) age at first drug use with seven high-risk injection and sexual behavior variables. Negative binomial regression models revealed that high-risk sexual behavior decreases between 15.8 and 20.9% with each decade of age, while high-risk injection behavior increases between 32 and 67% with each decade of age after the addition of demographic controls. Both high-risk injection and high-risk sex behaviors are significantly reduced with a delayed age at first drug use. Previous research promotes interventions to reduce the high-risk sexual behaviors of older drug users. The current study suggests a refocusing of public health efforts on the high-risk injection habits of older drug users.^