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We extend the literature on female autonomy along two dimensions. We first develop a new, direct female autonomy index using survey responses of spouses to a variety of household decision-making questions. We then examine the effects of a mother's autonomy on her child's secondary enrollment. We find that our measure is consistent with some of the existing autonomy measures, and that higher autonomy of mothers is correlated with higher secondary enrollment for boys, but not girls. Our results are robust to a range of sensitivity tests. [ABSTRACT FROM AUTHOR]

This study examines the practical implications of the fact that structural changes in factor loadings can produce spurious factors (or irrelevant factors) in forecasting exercises. These spurious factors can induce an overfitting problem in factor-augmented forecasting models. To address this concern, we propose a method to estimate nonspurious factors by identifying the set of response variables that have no structural changes in their factor loadings. Our theoretical results show that the obtained set may include a fraction of unstable response variables. However, the fraction is so small that the original factors are able to be identified and estimated consistently. Moreover, using this approach, we find that a significant portion of 132 U.S. macroeconomic time series have structural changes in their factor loadings. Although traditional principal components provide eight or more factors, there are significantly fewer nonspurious factors. The forecasts using the nonspurious factors can significantly improve out-of-sample performance. [ABSTRACT FROM PUBLISHER]

Butterfly pea ( Clitoria ternatea L.) is a legume used as tea, forage, ornamental, salad, and medicinal plant. The flowers range from white to dark purple with little known about the variation for seed and flower color in the United States Department of Agriculture, Agricultural Research Service, Plant Genetic Resources Conservation Unit germplasm collection. Therefore, 26 butterfly pea accessions were analyzed using a principal component analysis (PCA) and average linkage cluster analysis (ALCA). These butterfly pea genotypes ranged from 56% to 99% for viabilities, 2.57 to 5.88   g for 100 seed weight, 34.07 to 226.26   g for total seed weight, and 1,326 to 3,874 for total seed numbers. PCA accounted for 40%, 57%, 70%, 79%, and 86% of the variation using principal components (PCs) 1 through 5, respectively. PC1 was most correlated with 100 and total seed weight, while PC2 correlated with blue, white, and purple flowers. PC3 correlated mostly with germination, purple flowers, and total seed weight. PCs 4 and 5 primarily correlated with blue and purple flowers, respectively. Several significant correlations were also observed. ALCA grouped the 26 butterfly pea genotypes into four distinct seed number-producing clusters. Clusters 1 to 4 represent the lowest to highest seed numbers produced by the butterfly pea genotypes. Several potential health benefits from butterfly pea flowers, leaves, seeds, and roots for human use were identified from the literature.

Principal Component Analysis, Cluster Analysis and Regression Analysis were combined in testing the US crime dataset. The purpose is to elucidate a dependency between crime rate in several US states and socio-economic conditions. The main objective in Cluster Analysis is to discover natural groupings of the variables such that the degree of association between two variables is maximal when they belong to the same group. Cluster analysis grouped data in two clusters. Both methods suggest that there is a relation between high criminality, families with a low income, the environment with high crime rate, a large number of nonwhites and large population. [ABSTRACT FROM AUTHOR]

Desirable qualities of milling oat varieties include low hull content (high groat content), high beta-glucan content, high groat protein, low oil concentration, low kernel breakage, high grain yield, and superior yield stability. The objective of this study was to develop a graphical method for understanding the influence of environment on genetic relationships among traits. Associations among agronomic and quality traits in 67 oat (Avena sativa L.) performance trials conducted during 1996-2003 across Canada and some northern US states were studied using a trait-association by environment biplot, which allows visual study of pair-wise trait associations in multiple environments (year-location combinations). Based on the differential association of yield with days to heading and plant height, the North American spring oat growing regions can be divided into Northern mega-environment (Canadian Prairies plus North Dakota and Idaho) and Southern mega-environment (Minnesota, South Dakota, and Ontario). We also found that the following trait associations were relatively stable across environments: (1) negative association of protein content vs. oat yield, (2) positive association of beta-glucan vs. groat oil, (3) positive association of beta-glucan vs. protein content, and (4) negative association of beta-glucan vs. breakage. All trait-associations were of moderate magnitude and were responsive to the environment. This suggests that breeding for superior oat varieties with desired trait combinations is possible, but it must be achieved through direct selection for multiple traits in representative environments. doi:10.1300/J411v20n01_01 [ABSTRACT FROM PUBLISHER]