4期

最新文章

• A Comment on: Monotone Comparative Statics AI翻译
  作者：Amir, Rabah; Rietzke, David
  作者单位：University of Iowa; Lancaster University
  摘要：Milgrom and Shannon (1994) provide necessary and sufficient conditions on parameterized optimization problems for their solution sets to be globally monotone in the parameter. We establish that their conditions may be significantly relaxed when focusing on discrete, binary comparisons between solution sets. Such binary comparisons are ubiquitous in economics and may involve comparing the same decision maker across two distinct regimes or two distinct decision makers with related objectives (e....
• ROBUST PRIORS IN NONLINEAR PANEL DATA MODELS (vol 77, pg 489, 2009) AI翻译
  作者：Arellano, Manuel; Bonhomme, Stephane; Suarez, Sofia Borodich; Schumann, Martin; Shi, Xiaoxia; Tripathi, Gautam
  作者单位：University of Chicago; University of Luxembourg; Maastricht University; University of Wisconsin System; University of Wisconsin Madison
• A Comment on: Fisher-Schultz Lecture: Generic Machine Learning Inference on Heterogeneous Treatment Effects in Randomized Experiments, With an Application to Immunization in India by Victor Chernozhukov, Mert Demirer, Esther Duflo, and Iván Fernández-Val AI翻译
  作者：Imai, Kosuke; Li, Michael Lingzhi
  作者单位：Harvard University; Harvard University; Harvard University
  摘要：We examine the split-sample robust inference (SSRI) methodology introduced by Chernozhukov, Demirer, Duflo, and Fernandez-Val for quantifying uncertainty in heterogeneous treatment effect estimates produced by machine learning (ML) models. Although SSRI properly accounts for the additional variability due to sample splitting, its computational cost becomes prohibitive with complex ML models. We propose an alternative approach based on randomization inference (RI) that preserves the broad appli...
• SELECTING THE MOST EFFECTIVE NUDGE: EVIDENCE FROM A LARGE-SCALE EXPERIMENT ON IMMUNIZATION AI翻译
  作者：Banerjee, Abhijit; Chandrasekhar, Arun G.; Dalpath, Suresh; Duflo, Esther; Floretta, John; Jackson, Matthew O.; Kannan, Harini; Loza, Francine; Sankar, Anirudh; Schrimpf, Anna; Shrestha, Maheshwor
  作者单位：Massachusetts Institute of Technology (MIT); National Bureau of Economic Research; Stanford University; The Santa Fe Institute; The World Bank
  摘要：Policymakers often choose a policy bundle that is a combination of different interventions in different dosages. We develop a new technique-treatment variant aggregation (TVA)-to select a policy from a large factorial design. TVA pools together policy variants that are not meaningfully different and prunes those deemed ineffective. This allows us to restrict attention to aggregated policy variants, consistently estimate their effects on the outcome, and estimate the best policy effect adjustin...
• You Can Lead a Horse to Water: Spatial Learning and Path Dependence in Consumer Search AI翻译
  作者：Hodgson, Charles; Lewis, Gregory
  作者单位：Yale University; National Bureau of Economic Research
  摘要：We develop and estimate a model of consumer search with spatial learning. Consumers make inferences from previously searched objects to unsearched objects that are nearby in attribute space, generating path dependence in search sequences. The estimated model rationalizes patterns in data on online consumer search paths: search tends to converge to the chosen product in attribute space, and consumers take larger steps away from rarely purchased products. Eliminating spatial learning reduces con...
• REPLY TO: Comments on Fisher-Schultz Lecture: Generic Machine Learning Inference on Heterogeneous Treatment Effects in Randomized Experiments, With an Application to Immunization in India AI翻译
  作者：Chernozhukov, Victor; Demirer, Mert; Duflo, Esther; Fernandez-Val, Ivan
  作者单位：Massachusetts Institute of Technology (MIT); Massachusetts Institute of Technology (MIT); Massachusetts Institute of Technology (MIT); Boston University
• Dynamic Concern for Misspecification AI翻译
  作者：Lanzani, Giacomo
  作者单位：University of California System; University of California Berkeley
  摘要：I consider an agent who posits a set of probabilistic models for the payoff-relevant outcomes. The agent has a prior over this set but fears the actual model is omitted and hedges against this possibility. The concern for misspecification is endogenous: If a model explains the previous observations well, the concern attenuates. I show that different static preferences under uncertainty (subjective expected utility, maxmin, robust control) arise in the long run, depending on how quickly the age...
• A Comment on: Monotone Comparative Statics AI翻译
  作者：Amir, Rabah; Rietzke, David
  作者单位：University of Iowa; Lancaster University
  摘要：Milgrom and Shannon (1994) provide necessary and sufficient conditions on parameterized optimization problems for their solution sets to be globally monotone in the parameter. We establish that their conditions may be significantly relaxed when focusing on discrete, binary comparisons between solution sets. Such binary comparisons are ubiquitous in economics and may involve comparing the same decision maker across two distinct regimes or two distinct decision makers with related objectives (e....
• ROBUST PRIORS IN NONLINEAR PANEL DATA MODELS (vol 77, pg 489, 2009) AI翻译
  作者：Arellano, Manuel; Bonhomme, Stephane; Suarez, Sofia Borodich; Schumann, Martin; Shi, Xiaoxia; Tripathi, Gautam
  作者单位：University of Chicago; University of Luxembourg; Maastricht University; University of Wisconsin System; University of Wisconsin Madison
• Producing Health: Measuring Value Added of Nursing Homes AI翻译
  作者：Einav, Liran; Finkelstein, Amy; Mahoney, Neale
  作者单位：Stanford University; National Bureau of Economic Research; Massachusetts Institute of Technology (MIT)
  摘要：We develop a stylized model that allows us to estimate a value-added measure for nursing homes (SNFs) which accounts for patient selection both into and out of a SNF. We use the model, together with detailed data on the physical and mental health of about 6 million Medicare SNF patients between 2011 and 2016, to estimate the value added for about 14,000 distinct SNFs. We document substantial heterogeneity in value added. Nationwide, compared to a 10th percentile SNF, a 90th percentile SNF is a...