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IAAE 2023, Research Methods and Applications on Macroeconomic Forecasting2024We propose a simple yet robust framework to nowcast recession risk at a monthly frequency in both the United States and the Euro Area. Our nowcast leverages both macroeconomic and financial conditions, and is available the first business day after the reference month closes. In particular, we argue that financial conditions are not only useful to predict future downturns–as emphasized by the existing literature–but
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2023 Conference on Digital Experimentation @ MIT (CODE@MIT)2023In this study, we aim to present an effective methodology tailored for companies interested in implement-ing adaptive experimentation in scenarios characterized by potential selection biases or endogeneity. To illustrate our approach, we begin by delving into the realm of online experimentation. Online platforms routinely conduct thousands of A/B tests annually to gain insights into the impact of user-facing
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2023 Conference on Digital Experimentation @ MIT (CODE@MIT)2023Online A/B tests have become an indispensable tool across all the technology industry: if performed correctly, “online” experiments can inform effective decision making and product development. It should therefore not be surprising that Gupta et al. [2019] estimates that online businesses alone collectively run hundreds of thousands of experiments annually. Modern online experiments are often run in marketplaces
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2023 Conference on Digital Experimentation @ MIT (CODE@MIT)2023Randomized Control Trials (RCTs) are widely used across Amazon to causally estimate impacts of proposed feature changes, in order to make data-driven launch decisions. A key element of experimental design is the level of randomization, and the choice often relies on the cross-unit interaction structure. For instance, in the context of advertiser experiments, a treatment may affect the outcome of control
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2023 Conference on Digital Experimentation @ MIT (CODE@MIT)2023There are many experimental settings that may suffer from cross-unit (customers, seller, advertiser, etc.) spillovers, for instance through network effects. Such effects introduce bias and prevent the experimenter from drawing trustworthy insights on the data. One approach to dealing with such spillovers is to group units into clusters and randomize treatment status at the cluster level. Examples of clusters
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