Saving Between Cohorts: The Role of Planning

We compare the saving behavior of two cohorts: the Early Baby Boomers (EBB, age 51-56 in 2004) and the HRS cohort (age 51-56 in 1992). We find that the Boomers have accumulated more wealth than the previous cohort but they benefited from a large increase in house prices, which lifted the wealth of many home-owners. In fact, many EBB families, particularly those headed by respondents with low education, low income, and minorities, who have less wealth than the previous cohort. Lack of wealth can be traced to lack of retirement planning. Notwithstanding the many initiatives aimed at fostering planning in the 1990s, a large portion of EBB still do not plan for retirement even though most respondents are close to it. The effect of planning is remarkably similar between the two cohorts; those who do not plan accumulate much lower amounts of wealth, from 20 to 45 percent depending on the location in the wealth distribution, than those who do plan. Thus, for both the EBB and the HRS cohort, lack of planning is tantamount to lack of saving irrespective of the many changes in the economy between 1992 and 2004

The Long-Run Risks Model and Aggregate Asset Prices: An Empirical Assessment

The long-run risks model of asset prices explains stock price variation as a response to persistent fluctuations in the mean and volatility of aggregate consumption growth, by a representative agent with a high elasticity of intertemporal substitution. This paper documents several empirical difficulties for the model, as calibrated by Bansal and Yaron (BY, 2004) and Bansal et al. (BKY, 2011). U.S. data do not show as much univariate persistence in consumption or dividend growth as implied by the model. BY's calibration counterfactually implies that long-run consumption and dividend growth should be highly predictable from stock prices. BKY's calibration does better in this respect by greatly increasing the persistence of volatility fluctuations and their impact on stock prices. This calibration fits the predictive power of stock prices for future consumption volatility, but implies much greater predictive power of stock prices for future stock return volatility than is found in the data. The long-run risks model, particularly as calibrated by BKY, implies extremely low yields and negative term premia on inflation-indexed bonds. Finally, neither calibration can explain why movements in real interest rates do not generate strong predictable movements in consumption growth.Economic

The Long-Run Risks Model and Aggregate Asset Prices: An Empirical Assessment

The long-run risks model of asset prices explains stock price variation as a response to persistent fluctuations in the mean and volatility of aggregate consumption growth, by a representative agent with a high elasticity of intertemporal substitution. This paper documents several empirical difficulties for the model, as calibrated by Bansal and Yaron (BY, 2004) and Bansal et al. (BKY, 2011). U.S. data do not show as much univariate persistence in consumption or dividend growth as implied by the model. BY's calibration counterfactually implies that long-run consumption and dividend growth should be highly predictable from stock prices. BKY's calibration does better in this respect by greatly increasing the persistence of volatility fluctuations and their impact on stock prices. This calibration fits the predictive power of stock prices for future consumption volatility, but implies much greater predictive power of stock prices for future stock return volatility than is found in the data. The long-run risks model, particularly as calibrated by BKY, implies extremely low yields and negative term premia on inflation-indexed bonds. Finally, neither calibration can explain why movements in real interest rates do not generate strong predictable movements in consumption growth.Economic

The Long-Run Risks Model and Aggregate Asset Prices: An Empirical Assessment

The long-run risks model of asset prices explains stock price variation as a response to persistent fluctuations in the mean and volatility of aggregate consumption growth, by a representative agent with a high elasticity of intertemporal substitution. This paper documents several empirical difficulties for the model as calibrated by Bansal and Yaron (BY, 2004) and Bansal, Kiku, and Yaron (BKY, 2007a). BY's calibration counterfactually implies that long-run consumption and dividend growth should be highly persistent and predictable from stock prices. BKY's calibration does better in this respect by greatly increasing the persistence of volatility fluctuations and their impact on stock prices. This calibration fits the predictive power of stock prices for future consumption volatility, but implies much greater predictive power of stock prices for future stock return volatility than is found in the data. Neither calibration can explain why movements in real interest rates do not generate strong predictable movements in consumption growth. Finally, the long-run risks model implies extremely low yields and negative term premia on inflation-indexed bonds.

Savings Between Cohorts: the Role of Planning.

We compare the saving behavior of two cohorts: the Early Baby Boomers (EBB, age 51-56 in 2004) and the HRS cohort (age 51-56 in 1992). We find that EBB have accumulated more wealth than the previous cohort but they benefited from a large increase in house prices, which lifted the wealth of many home-owners. In fact, there are many families among EBB, particularly those headed by respondents with low education, low income, and minorities, which have less wealth than the previous cohort. Lack of wealth can be traced to lack of retirement planning. Notwithstanding the many initiatives aimed at fostering planning in the 1990s, a large portion of EBB still do not plan for retirement even though most respondents are close to it. The effect of planning is remarkably similar between the two cohorts; those who do not plan accumulate much lower amounts of wealth –from 20 to 45 percent depending on the location in the wealth distribution- than those who do plan. Thus, for both the EBB and the HRS cohort, lack of planning is tantamount to lack of saving irrespective of the many changes in the economy between 1992 and 2004.Social Security Administrationhttp://deepblue.lib.umich.edu/bitstream/2027.42/49409/1/wp122.pd

Cytologic scoring of equine exercise-induced pulmonary hemorrhage: Performance of human experts and a deep learning-based algorithm

Exercise-induced pulmonary hemorrhage (EIPH) is a relevant respiratory disease in sport horses, which can be diagnosed by examination of bronchoalveolar lavage fluid (BALF) cells using the total hemosiderin score (THS). The aim of this study was to evaluate the diagnostic accuracy and reproducibility of annotators and to validate a deep learning-based algorithm for the THS. Digitized cytological specimens stained for iron were prepared from 52 equine BALF samples. Ten annotators produced a THS for each slide according to published methods. The reference methods for comparing annotator’s and algorithmic performance included a ground truth dataset, the mean annotators’ THSs, and chemical iron measurements. Results of the study showed that annotators had marked interobserver variability of the THS, which was mostly due to a systematic error between annotators in grading the intracytoplasmatic hemosiderin content of individual macrophages. Regarding overall measurement error between the annotators, 87.7% of the variance could be reduced by using standardized grades based on the ground truth. The algorithm was highly consistent with the ground truth in assigning hemosiderin grades. Compared with the ground truth THS, annotators had an accuracy of diagnosing EIPH (THS of < or ≥ 75) of 75.7%, whereas, the algorithm had an accuracy of 92.3% with no relevant differences in correlation with chemical iron measurements. The results show that deep learning-based algorithms are useful for improving reproducibility and routine applicability of the THS. For THS by experts, a diagnostic uncertainty interval of 40 to 110 is proposed. THSs within this interval have insufficient reproducibility regarding the EIPH diagnosis

Cytologic scoring of equine exercise-induced pulmonary hemorrhage : performance of human experts and a deep learning-based algorithm

Exercise-induced pulmonary hemorrhage (EIPH) is a relevant respiratory disease in sport horses, which can be diagnosed by examination of bronchoalveolar lavage fluid (BALF) cells using the total hemosiderin score (THS). The aim of this study was to evaluate the diagnostic accuracy and reproducibility of annotators and to validate a deep learning-based algorithm for the THS. Digitized cytological specimens stained for iron were prepared from 52 equine BALF samples. Ten annotators produced a THS for each slide according to published methods. The reference methods for comparing annotator’s and algorithmic performance included a ground truth dataset, the mean annotators’ THSs, and chemical iron measurements. Results of the study showed that annotators had marked interobserver variability of the THS, which was mostly due to a systematic error between annotators in grading the intracytoplasmatic hemosiderin content of individual macrophages. Regarding overall measurement error between the annotators, 87.7% of the variance could be reduced by using standardized grades based on the ground truth. The algorithm was highly consistent with the ground truth in assigning hemosiderin grades. Compared with the ground truth THS, annotators had an accuracy of diagnosing EIPH (THS of < or ≥ 75) of 75.7%, whereas, the algorithm had an accuracy of 92.3% with no relevant differences in correlation with chemical iron measurements. The results show that deep learning-based algorithms are useful for improving reproducibility and routine applicability of the THS. For THS by experts, a diagnostic uncertainty interval of 40 to 110 is proposed. THSs within this interval have insufficient reproducibility regarding the EIPH diagnosis.The Dres. Jutta and Georg Bruns-Stifung für innovative Veterinärmedizin.https://journals.sagepub.com/home/vetCompanion Animal Clinical Studie