How Should Monetary Policy Respond to Asset-Price Bubbles?

We present a simple macroeconomic model that includes a role for an asset-price bubble. We then derive optimal monetary policy settings for two policymakers: a skeptic, for whom the best forecast of future asset prices is the current price; and an activist, whose policy recommendations take into account the complete stochastic implications of the bubble. We show that the activist’s recommendations depend sensitively on the detailed stochastic properties of the bubble. In some circumstances the activist clearly recommends tighter policy than the skeptic, but in others the appropriate recommendation is to be looser. Our results highlight the stringent informational requirements inherent in an activist policy approach to handling asset-price bubbles.

Eye Tracking Consumer Purchase Behavior Within Physical and Virtual Environments

Understanding how consumers observe and make purchase decisions within a retail context is now both accessible and efficient through the process of eye tracking. Eye tracking package design aesthetics helps us understand and predict what consumers are looking at, and how likely a package might be selected. Typically, this research is conducted in an immersive retail setting where consumers can shop as they would in a normal store-shopping context. A store is stocked with products where a participant in the study shops throughout while wearing an eye tracker to gather data on what their attention fixates on within a given set of shelves. Although a physical store provides the most realistic context, a virtual store could create a more economical, cost effective, and customizable solution for measuring consumer visual attention from packaging design aesthetics. Beginning with CUshop Consumer Experience Laboratory, a virtual store design and context was established by replicating existing fixtures in CUshopTM. Using the virtual technology available at the Sonoco Institute of Packaging Design and Graphics, a digital replication of CUshopTM was created. This began by 3D modeling the store along with generating the exact content to be displayed using real time rendering software. To investigate the process of measuring consumer attention in each environment, the same study was conducted in both stores looking at shelf performance of eleven different barbecue sauce brands. Gaze data, travel time, purchase decision and presence survey scores from a modified Witmer-Singer survey helped demonstrate the feasibility of gathering valid results from a virtual store context. Results indicated that there was not enough evidence to prove a comparison between the physical and virtual store experiments. Presence scores also did not indicate significant differences between either store environments. Analysis suggests that with a larger participant population and more immersive hardware, such as head mounted displays, eye tracking in virtual stores could be a valid process to complement studies already being conducted in real store contexts

Output Gaps in Real Time: Are They Reliable Enough to Use for Monetary Policy?

The output gap – the difference between actual and potential output – is widely regarded as a useful guide to future inflationary pressures, as well as an important indicator of the state of the economy in its own right. Since the output gap is unobservable, however, its estimation is prone to error, particularly in real time. Errors result both from revisions to the underlying data, as well as from end-point problems that are endemic to econometric procedures used to estimate output gaps. These problems reduce the reliability of output gaps estimated in real time, and lead to questions about their usefulness. We examine 121 vintages of Australian GDP data to assess the seriousness of these problems. Our study, which is the first to address these issues using Australian data, is of interest for the method we use to obtain real-time output-gap estimates. Over the past 28 years, our real-time output-gap estimates show no apparent bias, when compared with final output-gap estimates derived with the benefit of hindsight using the latest available data. Furthermore, the root-mean-square difference between the real-time and final output-gap series is less than 2 percentage points, and the correlation between them is over 0.8. Our general conclusion is that quite good estimates of the output gap can be generated in real time, provided a sufficiently flexible and robust approach is used to obtain them.monetary policy; output gaps; real-time data

How Should Monetary Policy Respond to Asset-price Bubbles?

We present a simple model of the macroeconomy that includes a role for an asset-price bubble, and derive optimal monetary policy settings for two policy-makers. The first policy-maker, a sceptic, does not attempt to forecast the future possible paths for the asset-price bubble when setting policy. The second policy-maker, an activist, takes into account the complete stochastic implications of the bubble when setting policy. We examine the optimal policy recommendations of these two policy-makers across a range of plausible assumptions about the bubble. We show that the optimal monetary policy recommendations of the activist depend on the detailed stochastic properties of the bubble. There are some circumstances in which the activist clearly recommends tighter policy than that of the sceptic, while in other cases, the appropriate recommendation is to be looser than the sceptic. Other things equal, the case for ‘leaning against’ a bubble with monetary policy is stronger the lower the probability of the bubble bursting of its own accord, the larger the efficiency losses associated with big bubbles, and the higher the assumed impact of monetary policy on the bubble process.optimal monetary policy; asset-price bubble

Dynamic Clinical Data Mining: Search Engine-Based Decision Support

The research world is undergoing a transformation into one in which data, on massive levels, is freely shared. In the clinical world, the capture of data on a consistent basis has only recently begun. We propose an operational vision for a digitally based care system that incorporates data-based clinical decision making. The system would aggregate individual patient electronic medical data in the course of care; query a universal, de-identified clinical database using modified search engine technology in real time; identify prior cases of sufficient similarity as to be instructive to the case at hand; and populate the individual patient's electronic medical record with pertinent decision support material such as suggested interventions and prognosis, based on prior outcomes. Every individual's course, including subsequent outcomes, would then further populate the population database to create a feedback loop to benefit the care of future patients

Epistemic Uncertainty-Weighted Loss for Visual Bias Mitigation

Deep neural networks are highly susceptible to learning biases in visual data. While various methods have been proposed to mitigate such bias, the majority require explicit knowledge of the biases present in the training data in order to mitigate. We argue the relevance of exploring methods which are completely ignorant of the presence of any bias, but are capable of identifying and mitigating them. Furthermore, we propose using Bayesian neural networks with an epistemic uncertainty-weighted loss function to dynamically identify potential bias in individual training samples and to weight them during training. We find a positive correlation between samples subject to bias and higher epistemic uncertainties. Finally, we show the method has potential to mitigate visual bias on a bias benchmark dataset and on a real-world face detection problem, and we consider the merits and weaknesses of our approach.Comment: To be published in 2022 IEEE CVPR Workshop on Fair, Data Efficient and Trusted Computer Visio

How Should Monetary Policy Respond to Asset-Price Bubbles?

We present a simple macroeconomic model that includes a role for an asset-price bubble. We then derive optimal monetary policy settings for two policymakers: a skeptic, for whom the best forecast of future asset prices is the current price; and an activist, whose policy recommendations take into account the complete stochastic implications of the bubble. We show that the activist’s recommendations depend sensitively on the detailed stochastic properties of the bubble. In some circumstances the activist clearly recommends tighter policy than the skeptic, but in others the appropriate recommendation is to be looser. Our results highlight the stringent informational requirements inherent in an activist policy approach to handling asset-price bubbles

Bayesian uncertainty-weighted loss for improved generalisability on polyp segmentation task

While several previous studies have devised methods for segmentation of polyps, most of these methods are not rigorously assessed on multi-center datasets. Variability due to appearance of polyps from one center to another, difference in endoscopic instrument grades, and acquisition quality result in methods with good performance on in-distribution test data, and poor performance on out-of-distribution or underrepresented samples. Unfair models have serious implications and pose a critical challenge to clinical applications. We adapt an implicit bias mitigation method which leverages Bayesian epistemic uncertainties during training to encourage the model to focus on underrepresented sample regions. We demonstrate the potential of this approach to improve generalisability without sacrificing state-of-the-art performance on a challenging multi-center polyp segmentation dataset (PolypGen) with different centers and image modalities.Comment: To be presented at the Fairness of AI in Medical Imaging (FAIMI) MICCAI 2023 Workshop and published in volumes of the Springer Lecture Notes Computer Science (LNCS) serie