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We present a tractable model of the effects of nonfinancial risk on intertemporal choice. Our purpose is to provide a simple framework that can be adopted in fields like representative-agent macroeconomics, corporate finance, or political economy, where most modelers have chosen not to incorporate serious nonfinancial risk because available methods were too complex to yield transparent insights. Our model produces an intuitive analytical formula for target assets, and we show how to analyze transition dynamics using a familiar Ramsey-style phase diagram. Despite its starkness, our model captures most of the key implications of nonfinancial risk for intertemporal choice.
We argue that the U.S. personal saving rate’s long stability (1960s–1980s), subsequent steady decline (1980s–2007), and recent substantial rise (2008–2011) can be interpreted using a parsimonious ‘buffer stock’ model of consumption in the presence of labor income uncertainty and credit constraints. Saving in the model is affected by the gap between ‘target’ and actual wealth, with the target determined by credit conditions and uncertainty. An estimated structural version of the model suggests that increased credit availability accounts for most of the long-term saving decline, while fluctuations in wealth and uncertainty capture the bulk of the business-cycle variation.
Small and medium-sized firms typically obtain capital via bank financing. They often rely on a mixture of relationship and arm’s-length banking. This paper explores the reasons for the dominance of heterogeneous multiple banking systems. We show that the incidence of inefficient credit termination and subsequent firm liquidation is contingent on the borrower’s quality and on the relationship bank’s information precision. Generally, heterogeneous multiple banking leads to fewer inefficient credit decisions than monopoly relationship lending or homogeneous multiple banking, provided that the relationship bank’s fraction of total firm debt is not too large.
Measuring confidence and uncertainty during the financial crisis : evidence from the CFS survey
(2010)
The CFS survey covers individual situations of banks and other companies of the financial sector during the financial crisis. This provides a rare possibility to analyze appraisals, expectations and forecast errors of the core sector of the recent turmoil. Following standard ways of aggregating individual survey data, we first present and introduce the CFS survey by comparing CFS indicators of confidence and predicted confidence to ifo and ZEW indicators. The major contribution is the analysis of several indicators of uncertainty. In addition to well established concepts, we introduce innovative measures based on the skewness of forecast errors and on the share of ‘no response’ replies. Results show that uncertainty indicators fit quite well with pattern of real and financial time series of the time period 2007 to 2010. Business Sentiment , Financial Crisis , Survey Indicator , Uncertainty
Measuring confidence and uncertainty during the financial crisis: evidence from the CFS survey
(2011)
The CFS survey covers individual situations of banks and other companies of the financial sector during the financial crisis. This provides a rare possibility to analyze appraisals, expectations and forecast errors of the core sector of the recent turmoil. Following standard ways of aggregating individual survey data, we first present and introduce the CFS survey by comparing CFS indicators of confidence and predicted confidence to ifo and ZEW indicators. The major contribution is the analysis of several indicators of uncertainty. In addition to well established concepts, we introduce innovative measures based on the skewness of forecast errors and on the share of ‘no response’ replies. Results show that uncertainty indicators fit quite well with pattern of real and financial time series of the time period 2007 to 2010. Business Sentiment , Financial Crisis , Survey Indicator , Uncertainty CFS working paper series, 2010, 18. Revised Version July 2011
Local climate change risk assessments (LCCRAs) are best supported by a quantitative integration of physical hazards, exposures and vulnerabilities that includes the characterization of uncertainties. We propose to use Bayesian Networks (BNs) for this task and show how to integrate freely-available output of multiple global hydrological models (GHMs) into BNs, in order to probabilistically assess risks for water supply. Projected relative changes in hydrological variables computed by three GHMs driven by the output of four global climate models were processed using MATLAB, taking into account local information on water availability and use. A roadmap to set up BNs and apply probability distributions of risk levels under historic and future climate and water use was co-developed with experts from the Maghreb (Tunisia, Algeria, Morocco) who positively evaluated the BN application for LCCRAs. We conclude that the presented approach is suitable for application in the many LCCRAs necessary for successful adaptation to climate change world-wide.
The budget constraint requires that, eventually, consumption must adjust fully to any permanent shock to income. Intuition suggests that, knowing this, optimizing agents will fully adjust their spending immediately upon experiencing a permanent shock. However, this paper shows that if consumers are impatient and are subject to transitory as well as permanent shocks, the optimal marginal propensity to consume out of permanent shocks (the MPCP) is strictly less than 1, because buffer stock savers have a target wealth-to-permanent-income ratio; a positive shock to permanent income moves the ratio below its target, temporarily boosting saving. Keywords: Risk, Uncertainty, Consumption, Precautionary Saving, Buffer Stock Saving, Permanent Income Hypothesis.
Background: Experienced and anticipated regret influence physicians’ decision-making. In medicine, diagnostic decisions and diagnostic errors can have a severe impact on both patients and physicians. Little empirical research exists on regret experienced by physicians when they make diagnostic decisions in primary care that later prove inappropriate or incorrect. The aim of this study was to explore the experience of regret following diagnostic decisions in primary care.
Methods: In this qualitative study, we used an online questionnaire on a sample of German primary care physicians. We asked participants to report on cases in which the final diagnosis differed from their original opinion, and in which treatment was at the very least delayed, possibly resulting in harm to the patient. We asked about original and final diagnoses, illness trajectories, and the reactions of other physicians, patients and relatives. We used thematic analysis to assess the data, supported by MAXQDA 11 and Microsoft Excel 2016.
Results: 29 GPs described one case each (14 female/15 male patients, aged 1.5–80 years, response rate < 1%). In 26 of 29 cases, the final diagnosis was more serious than the original diagnosis. In two cases, the diagnoses were equally serious, and in one case less serious. Clinical trajectories and the reactions of patients and relatives differed widely. Although only one third of cases involved preventable harm to patients, the vast majority (27 of 29) of physicians expressed deep feelings of regret.
Conclusion: Even if harm to patients is unavoidable, regret following diagnostic decisions can be devastating for clinicians, making them ‘second victims’. Procedures and tools are needed to analyse cases involving undesirable diagnostic events, so that ‘true’ diagnostic errors, in which harm could have been prevented, can be distinguished from others. Further studies should also explore how physicians can be supported in dealing with such events in order to prevent them from practicing defensive medicine.
Quantitative models have several advantages compared to qualitative methods for pest risk assessments (PRA). Quantitative models do not require the definition of categorical ratings and can be used to compute numerical probabilities of entry and establishment, and to quantify spread and impact. These models are powerful tools, but they include several sources of uncertainty that need to be taken into account by risk assessors and communicated to decision makers. Uncertainty analysis (UA) and sensitivity analysis (SA) are useful for analyzing uncertainty in models used in PRA, and are becoming more popular. However, these techniques should be applied with caution because several factors may influence their results. In this paper, a brief overview of methods of UA and SA are given. As well, a series of practical rules are defined that can be followed by risk assessors to improve the reliability of UA and SA results. These rules are illustrated in a case study based on the infection model of Magarey et al. (2005) where the results of UA and SA are shown to be highly dependent on the assumptions made on the probability distribution of the model inputs.
We find that high macroeconomic uncertainty is associated with greater accumulation of physical capital, despite a reduction in investment and valuations. To reconcile this puzzling evidence, we show that uncertainty predicts lower depreciation and utilization of existing capital, which dominates the investment slowdown. Motivated by these dynamics, we develop a quantitative production-based model in which firms implement precautionary savings through reducing utilization rather than raising invest-ment. Through this novel intensive-margin mechanism, uncertainty shocks command a quarter of the equity premium in general equilibrium, while flexibility in utilization adjustments helps explain uncertainty risk exposures in the cross-section of industry returns.