Robust decision-making under risk and ambiguity

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1. Apparenté Pas pertinent

   Robust Synthetic Control

   Muhammad Jehangir Amjad, Devavrat Shah, Dennis Shen · 2017 · Économétrie · Résumé IA disponible

   Résumé scientifique

   We present a robust generalization of the synthetic control method for comparative case studies. Like the classical method, we present an algorithm to estimate the unobservable counterfactual of a treatment unit. A distinguishing feature of our algorithm is that of de-noising the data matrix via singular value thresholding, which renders our approach robust in multiple facets: it automatically identifies a good subset of donors, overcomes the challenges of missing data, and continues to work well in settings where covariate information may not be provided. To begin, we establish the condition under which the fundamental assumption in synthetic control-like approaches holds, i.e. when the lin…

2. Apparenté Pas pertinent

   On Bayesian Filtering for Markov Regime Switching Models

   Nigar Hashimzade, Oleg Kirsanov, Tatiana Kirsanova et al. · 2024 · Économétrie · Résumé IA disponible

   Résumé scientifique

   This paper presents a framework for empirical analysis of dynamic macroeconomic models using Bayesian filtering, with a specific focus on the state-space formulation of Dynamic Stochastic General Equilibrium (DSGE) models with multiple regimes. We outline the theoretical foundations of model estimation, provide the details of two families of powerful multiple-regime filters, IMM and GPB, and construct corresponding multiple-regime smoothers. A simulation exercise, based on a prototypical New Keynesian DSGE model, is used to demonstrate the computational robustness of the proposed filters and smoothers and evaluate their accuracy and speed for a selection of filters from each family. We show …

3. Apparenté Pas pertinent

   Nonparametric Tests of Tail Behavior in Stochastic Frontier Models

   William, C. Horrace, Yulong Wang · 2020 · Économétrie · Résumé IA disponible

   Résumé scientifique

   This article studies tail behavior for the error components in the stochastic frontier model, where one component has bounded support on one side, and the other has unbounded support on both sides. Under weak assumptions on the error components, we derive nonparametric tests that the unbounded component distribution has thin tails and that the component tails are equivalent. The tests are useful diagnostic tools for stochastic frontier analysis. A simulation study and an application to a stochastic cost frontier for 6,100 US banks from 1998 to 2005 are provided. The new tests reject the normal or Laplace distributional assumptions, which are commonly imposed in the existing literature.

4. Apparenté Pas pertinent

   Modeling Macroeconomic Variations After COVID-19

   Serena Ng · 2021 · Économétrie · Résumé IA disponible

   Résumé scientifique

   The coronavirus is a global event of historical proportions and just a few months changed the time series properties of the data in ways that make many pre-covid forecasting models inadequate. It also creates a new problem for estimation of economic factors and dynamic causal effects because the variations around the outbreak can be interpreted as outliers, as shifts to the distribution of existing shocks, or as addition of new shocks. I take the latter view and use covid indicators as controls to 'de-covid' the data prior to estimation. I find that economic uncertainty remains high at the end of 2020 even though real economic activity has recovered and covid uncertainty has receded. Dynamic…

5. Apparenté Pas pertinent

   Adaptive Pricing in Insurance: Generalized Linear Models and Gaussian Process Regression Approaches

   Yuqing Zhang, Neil Walton · 2019 · Économétrie · Résumé IA disponible

   Résumé scientifique

   We study the application of dynamic pricing to insurance. We view this as an online revenue management problem where the insurance company looks to set prices to optimize the long-run revenue from selling a new insurance product. We develop two pricing models: an adaptive Generalized Linear Model (GLM) and an adaptive Gaussian Process (GP) regression model. Both balance between exploration, where we choose prices in order to learn the distribution of demands & claims for the insurance product, and exploitation, where we myopically choose the best price from the information gathered so far. The performance of the pricing policies is measured in terms of regret: the expected revenue loss cause…

6. Apparenté Pas pertinent

   A multi-cell experimental design to recover policy relevant treatment effects, with an application to online advertising

   Caio Waisman, Brett R. Gordon · 2023 · Économétrie · Résumé IA disponible

   Résumé scientifique

   Experiments are an important tool to measure the impacts of interventions. However, in experimental settings with one-sided noncompliance, extant empirical approaches may not produce the estimates a decision-maker needs to solve their problem. For example, these experimental designs are common in digital advertising settings, but they are uninformative of decisions regarding the intensive margin -- how much should be spent or how many consumers should be reached with a campaign. We propose a solution that combines a novel multi-cell experimental design with modern estimation techniques that enables decision-makers to recover enough information to solve problems with an intensive margin. Our …

7. Apparenté Pas pertinent

   Machine Learning Estimation of Heterogeneous Treatment Effects with Instruments

   Vasilis Syrgkanis, Victor Lei, Miruna Oprescu et al. · 2019 · Économétrie · Résumé IA disponible

   Résumé scientifique

   We consider the estimation of heterogeneous treatment effects with arbitrary machine learning methods in the presence of unobserved confounders with the aid of a valid instrument. Such settings arise in A/B tests with an intent-to-treat structure, where the experimenter randomizes over which user will receive a recommendation to take an action, and we are interested in the effect of the downstream action. We develop a statistical learning approach to the estimation of heterogeneous effects, reducing the problem to the minimization of an appropriate loss function that depends on a set of auxiliary models (each corresponding to a separate prediction task). The reduction enables the use of all …

8. Apparenté Pas pertinent

   Momentum Dynamics in Competitive Sports: A Multi-Model Analysis Using TOPSIS and Logistic Regression

   Mingpu Ma · 2024 · Économétrie · Résumé IA disponible

   Résumé scientifique

   This paper explores the concept of "momentum" in sports competitions through the use of the TOPSIS model and 0-1 logistic regression model. First, the TOPSIS model is employed to evaluate the performance of two tennis players, with visualizations used to analyze the situation's evolution at every moment in the match, explaining how "momentum" manifests in sports. Then, the 0-1 logistic regression model is utilized to verify the impact of "momentum" on match outcomes, demonstrating that fluctuations in player performance and the successive occurrence of successes are not random. Additionally, this paper examines the indicators that influence the reversal of game situations by analyzing key ma…