7 results
ECONOMIC ACTIVITY, CREDIT MARKET CONDITIONS, AND THE HOUSING MARKET
- Luca Agnello, Vitor Castro, Ricardo M. Sousa
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- Journal:
- Macroeconomic Dynamics / Volume 22 / Issue 7 / October 2018
- Published online by Cambridge University Press:
- 03 July 2017, pp. 1769-1789
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In this paper, we assess the characteristics of the housing market and its main determinants. Using data for 20 industrial countries over the period 1970Q1–2012Q2 and a discrete-time Weibull duration model, we find that the likelihood of the end of a housing boom or a housing bust increases over time. Additionally, we show that the different phases of the housing market cycle are strongly dependent on the economic activity, but credit market conditions are particularly important in the case of housing booms. The empirical findings also indicate that although housing booms have similar lengths in European and non-European countries, housing busts are typically shorter in European countries. The use of a more flexible specification for the hazard function that is based on cubic splines suggests that it evolves in a nonlinear way. From a policy perspective, our study can be useful for predicting the timing and the length of housing boom–bust cycles. Moreover, it highlights the importance of monetary policy by influencing lending rates and affecting the likelihood of occurrence of housing booms.
Evaluation of longevity modeling censored records in Nellore
- D. A. Garcia, G. J. M. Rosa, B. D. Valente, R. Carvalheiro, G. A. Fernandes Júnior, L. G. Albuquerque
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The aim of the present study was to evaluate the prediction ability of models that cope with longevity phenotypic expression as uncensored and censored in Nellore cattle. Longevity was defined as the difference between the dates of last weaned calf and cow birth. There were information of 77 353 females, being 61 097 cows with uncensored phenotypic information and 16 256 cows with censored records. These data were analyzed considering three different models: (1) Gaussian linear model (LM), in which only uncensored records were considered; and two models that consider both uncensored and censored records: (2) Censored Gaussian linear model (CLM); and (3) Weibull frailty hazard model (WM). For the model prediction ability comparisons, the data set was randomly divided into training and validation sets, containing 80% and 20% of the records, respectively. There were considered 10 repetitions applying the following restrictions: (a) at least three animals per contemporary group in the training set; and (b) sires with more than 10 progenies with uncensored records (352 sires) should have daughters in the training and validation sets. The variance components estimated using the whole data set in each model were used as true values in the prediction of breeding values of the animals in the training set. The WM model showed the best prediction ability, providing the lowest χ2 average and the highest number of sets in which a model had the smallest value of χ2 statistics. The CLM and LM models showed prediction abilities 2.6% and 3.7% less efficient than WM, respectively. In addition, the accuracies of sire breeding values for LM and CLM were lower than those obtained for WM. The percentages of bulls in common, considering only 10% of sires with the highest breeding values, were around 75% and 54%, respectively, between LM–CLM and LM–WM models, considering all sires, and 75% between LM–CLM and LM–WM, when only sires with more than 10 progenies with uncensored records were taken into account. These results are indicative of reranking of animals in terms of genetic merit between LM, CLM and WM. The model in which censored records of longevity were excluded from the analysis showed the lowest prediction ability. The WM provides the best predictive performance, therefore this model would be recommended to perform genetic evaluation of longevity in this population.
Seedbank Size and Emergence Pattern of Barnyardgrass (Echinochloa crus-galli) in Arkansas
- Muthukumar V. Bagavathiannan, Jason K. Norsworthy, Kenneth L. Smith, Nilda Burgos
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- Journal:
- Weed Science / Volume 59 / Issue 3 / September 2011
- Published online by Cambridge University Press:
- 20 January 2017, pp. 359-365
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Barnyardgrass is one of the most problematic weeds in Arkansas, and with the documentation of herbicide-resistant biotypes, there is a need to gain a detailed understanding of its ecology. In particular, knowledge on barnyardgrass seedbank size and emergence pattern is vital. An extensive seedbank survey was carried out in 2008 in 12 counties in eastern Arkansas to determine barnyardgrass seedbank size across the region. There was a great variability in seedbank size with a maximum of 215,000 seeds m−2. Among the fields surveyed, barnyardgrass seedbank was found only in 7% of the cotton fields, while it was 22 and 20%, respectively, for rice and soybean. To examine the emergence pattern of barnyardgrass, experiments were conducted in Rohwer (two sites), Stuttgart (one site), and Fayetteville (one site), Arkansas in 2008 and 2009. In each site, barnyardgrass emergence was quantified from naturally occurring seedbanks. Barnyardgrass exhibited an extended period of emergence with days to 100% emergence ranging from 99 to 165 across sites and years. Nevertheless, effective management may be achieved by targeting the peak emergence periods, which range from mid-April to mid-June in Arkansas. The four-parameter Weibull model provided a better fit to the cumulative emergence data. However, the thermal time (growing degree days, GDDs) or hydrothermal time (HTT) models did not predict barnyardgrass emergence any better than calendar days, perhaps because of the inherent variations associated with natural seedbanks. This study establishes seedbank size and general emergence pattern for barnyardgrass in Arkansas. Additionally, these results will be useful for parameterizing herbicide-resistance simulation models for barnyardgrass.
Environmental Triggers of Winter Annual Weed Emergence in the Midwestern United States
- Rodrigo Werle, Mark L. Bernards, Timothy J. Arkebauer, John L. Lindquist
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- Journal:
- Weed Science / Volume 62 / Issue 1 / March 2014
- Published online by Cambridge University Press:
- 20 January 2017, pp. 83-96
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Winter annual weeds are becoming prolific in agricultural fields in the midwestern United States. The objectives of this research were to understand the roles of soil temperature (daily average and fluctuation) and moisture on the emergence of nine winter annual weed species and dandelion and to develop predictive models for weed emergence based on the accumulation of modified thermal/hydrothermal time (mHTT). Experiments were established at Lincoln, NE; Mead, NE; and at two sites (irrigated and rainfed) near Clay Center, NE, in 2010 and 2011. In July of each year, 1,000 seeds of each species were planted in 15 by 20 by 6-cm mesh baskets installed between soybean rows. Soil temperature and water content were recorded at the 2-cm depth. Emerged seedlings were counted and removed from the baskets on a weekly basis until no additional emergence was observed in the fall, resumed in late winter, and continued until emergence ceased in late spring. Weather data were used to accumulate mHTT beginning on August 1. A Weibull function was selected to fit cumulative emergence (%) on cumulative mHTT (seven base temperature [Tbase] by six base water potential [Ψbase] by three base temperature fluctuation [Fbase] candidate threshold values = 126 models); it was also fit to days after August 1 (DAA1), for a total of 127 candidate models per species. The search for optimal base thresholds was based on the theoretic-model comparison approach (Akaike information criterion [AIC]). All three components (Tbase, Ψbase, and Fbase) were only important for Virginia pepperweed. For downy brome and purslane speedwell, including Tbase and Ψbase resulted in the best fit, whereas for dandelion including Tbase and Fbase resulted in the best fit. A model including only Tbase resulted in the best fit for most species included in this study (Carolina foxtail, shepherd's-purse, pinnate tansymustard, henbit, and field pansy). For field pennycress, the model based on DAA1 resulted in the best fit. Threshold values were species specific. Soil temperature was the major environmental factor influencing winter annual weed emergence. Even though soil moisture and often temperature fluctuation are essential for seed germination, Ψbase and Fbase were not as critical in the predictive models as initially expected. Most seedlings (> 90%) of downy brome, pinnate tansymustard, Carolina foxtail, henbit, and field pansy emerged during the fall. Virginia pepperweed, purslane speedwell, dandelion, shepherd's-purse, and field pennycress seedlings emerged during both fall and spring. The results of this research provide robust information on the prediction of the time of winter annual weed emergence, which can help growers make better management decisions.
Predicting Emergence of 23 Summer Annual Weed Species
- Rodrigo Werle, Lowell D. Sandell, Douglas D. Buhler, Robert G. Hartzler, John L. Lindquist
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- Journal:
- Weed Science / Volume 62 / Issue 2 / June 2014
- Published online by Cambridge University Press:
- 20 January 2017, pp. 267-279
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First- and second-year seedbank emergence of 23 summer annual weed species common to U.S. corn production systems was studied. Field experiments were conducted between 1996 and 1999 at the Iowa State University Johnson Farm in Story County, Iowa. In the fall of 1996 and again in 1997, 1,000 seeds for most species were planted in plastic crates. Seedling emergence was counted weekly for a 2-yr period following seed burial (starting in early spring). Soil temperature at 2 cm depth was estimated using soil temperature and moisture model software (STM2). The Weibull function was fit to cumulative emergence (%) on cumulative thermal time (TT), hydrothermal time (HTT), and day of year (DOY). To identify optimum base temperature (Tbase) and base matric potential (ψbase) for calculating TT or HTT, Tbase and ψbase values ranging from 2 to 17 C and −33 to −1,500 kPa, respectively, were evaluated for each species. The search for the optimal model for each species was based on the Akaike's Information Criterion (AIC), whereas an extra penalty cost was added to HTT models. In general, fewer seedlings emerged during the first year of the first experimental run (approximately 18% across all species) than during the second experimental run (approximately 30%). However, second-year seedbank emergence was similar for both experimental runs (approximately 6%). Environmental effects may be the cause of differences in total seedling emergence among years. Based on the AIC criterion, for 17 species, the best fit of the model occurred using Tbase ranging from 2 to 15 C with four species also responding to ψbase = −750 kPa. For six species, a simple model using DOY resulted in the best fit. Adding penalty costs to AIC calculation allowed us to compare TT and HTT when both models behaved similarly. Using a constant Tbase, species were plotted and classified as early-, middle-, and late-emerging species, resulting in a practical tool for forecasting time of emergence. The results of this research provide robust information on the prediction of the time of summer annual weed emergence, which can be used to schedule weed and crop management.
Minimizing attrition bias: a longitudinal study of depressive symptoms in an elderly cohort
- Chung-Chou H. Chang, Hsiao-Ching Yang, Gong Tang, Mary Ganguli
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- Journal:
- International Psychogeriatrics / Volume 21 / Issue 5 / October 2009
- Published online by Cambridge University Press:
- 17 March 2009, pp. 869-878
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Background: Attrition from mortality is common in longitudinal studies of the elderly. Ignoring the resulting non-response or missing data can bias study results.
Methods: 1260 elderly participants underwent biennial follow-up assessments over 10 years. Many missed one or more assessments over this period. We compared three statistical models to evaluate the impact of missing data on an analysis of depressive symptoms over time. The first analytic model (generalized mixed model) treated non-response as data missing at random. The other two models used shared parameter methods; each had different specifications for dropout but both jointly modeled both outcome and dropout through a common random effect.
Results: The presence of depressive symptoms was associated with being female, having less education, functional impairment, using more prescription drugs, and taking antidepressant drugs. In all three models, the same variables were significantly associated with depression and in the same direction. However, the strength of the associations differed widely between the generalized mixed model and the shared parameter models. Although the two shared parameter models had different assumptions about the dropout process, they yielded similar estimates for the outcome. One model fitted the data better, and the other was computationally faster.
Conclusions: Dropout does not occur randomly in longitudinal studies of the elderly. Thus, simply ignoring it can yield biased results. Shared parameter models are a powerful, flexible, and easily implemented tool for analyzing longitudinal data while minimizing bias due to nonrandom attrition.
Vector survival and parasite infection: the effect of Wuchereria bancrofti on its vector Culex quinquefasciatus
- K. KRISHNAMOORTHY, S. SUBRAMANIAN, G. J. VAN OORTMARSSEN, J. D. F. HABBEMA, P. K. DAS
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- Journal:
- Parasitology / Volume 129 / Issue 1 / July 2004
- Published online by Cambridge University Press:
- 10 June 2004, pp. 43-50
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This paper investigates a cohort of 2187 laboratory reared Culex quinquefasciatus fed on 69 human volunteers, including 59 persons with different levels of Wuchereria bancrofti microfilariae and 10 without microfilaria. Mosquitoes were followed until death. Mosquito survival was analysed in relation to the level of microfilaria in the human and larval count in the dead mosquito. Vector mortality during the extrinsic incubation period (12 days post-engorgement) was significantly higher in mosquitoes fed on microfilaraemic volunteers (50%) than in those fed on amicrofilaraemics (29%). Both the percentage infected and the geometric mean parasite density was significantly higher among mosquitoes which died before 13 days (45% infected and 10 larvae per infected mosquito) than those surviving beyond 13 days (39% and 2·2), suggesting a parasite loss of more than 80% during the extrinsic incubation period. A large proportion (62%) of the mosquitoes that died during the early of phase of parasite development were infected (36% in low, 26% in medium and 90% in high human Mf-density). Survival analysis showed that the parasite load in mosquitoes and the human Mf-density for a given parasite load are independent risk factors of vector survival. Overall, the hazard of dying was found to be 11–15 times higher among mosquitoes fed on microfilaraemic volunteers than those fed on amicrofilaraemics. The hazard doubles for every increase of about 60–70 parasites in the vector. As a consequence of the parasite-induced reduction in vector survival, the transmission success of the parasite is reduced. The implication of the results on control/elimination of lymphatic filariasis using mass-drug administration is discussed.