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作者：Penczynski, Stefan P.; Santana, Maria Isabel
作者单位：University of East Anglia; University of East Anglia; University of East Anglia
摘要：We propose a novel way of measuring trust in institutions, which draws on the experimental method used to elicit time preferences. Our measure is provided in the meaningful metric of the subjective probability of trustworthiness of the trustee. In a lab-in-the-field setting in the Philippines, we measure trust in two different financial institutions. Additionally, we exploit exogenous variation in the eligibility for a future payment to examine whether a promise fulfilled by the institution in...
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作者：Armenter, Roc; Mueller-Itten, Michele; Stangebye, Zachary R.
作者单位：Federal Reserve System - USA; Federal Reserve Bank - Philadelphia; University of St Gallen; University of Notre Dame
摘要：We present a geometric approach to the finite Rational Inattention (RI) model, recasting it as a convex optimization problem with reduced dimensionality that is well suited to numerical methods. We provide an algorithm that outperforms existing RI computation techniques in terms of both speed and accuracy in both static and dynamic RI problems. We further introduce methods to quantify the impact of numerical inaccuracy on the model's outcomes and to produce robust predictions regarding the mos...
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作者：D'Haultfoeuille, Xavier; Tuvaandorj, Purevdorj
作者单位：Institut Polytechnique de Paris; ENSAE Paris; York University - Canada
摘要：We develop a new permutation test for inference on a subvector of coefficients in linear models. The test is exact when the regressors and the error terms are independent. Then we show that the test is asymptotically of correct level, consistent, and has power against local alternatives when the independence condition is relaxed, under two main conditions. The first is a slight reinforcement of the usual absence of correlation between the regressors and the error term. The second is that the n...
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作者：Valaitis, Vytautas; Villa, Alessandro T.
作者单位：University of Surrey; Federal Reserve System - USA; Federal Reserve Bank - Chicago
摘要：We use supervised machine learning to approximate the expectations typically contained in the optimality conditions of an economic model in the spirit of the parameterized expectations algorithm (PEA) with stochastic simulation. When the set of state variables is generated by a stochastic simulation, it is likely to suffer from multicollinearity. We show that a neural network-based expectations algorithm can deal efficiently with multicollinearity by extending the optimal debt management probl...
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作者：Kocherlakota, Narayana R.
作者单位：University of Rochester; National Bureau of Economic Research
摘要：This paper reconsiders the question of testing for the presence of Pareto suboptimal capital overaccumulation in overlapping generations economies. The paper allows generation-specific technology shocks to evolve over time according to a stationary Markov chain, and assumes that an econometrician observes a finite sample of aggregate quantities. In this setting, any statistical test of the null hypothesis of capital overaccumulation with size less than one also has zero power against the alter...
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作者：Pakes, Ariel; Porter, Jack
作者单位：Harvard University; National Bureau of Economic Research; University of Wisconsin System; University of Wisconsin Madison
摘要：This paper proposes a new approach to identification of the semiparametric multinomial choice model with fixed effects. The framework employed is the semiparametric version of the traditional multinomial logit with the fixed-effects model (Chamberlain (1980)). This semiparametric multinomial choice model places no restrictions on either the joint distribution of the random utility disturbances across choices or their within group (or across time) correlations. We show that a novel within-group...
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作者：Kendall, Chad; Oprea, Ryan
作者单位：University of Southern California; University of California System; University of California Los Angeles
摘要：We experimentally study how people form predictive models of simple data generating processes (DGPs), by showing subjects data sets and asking them to predict future outputs. We find that subjects: (i) often fail to predict in this task, indicating a failure to form a model, (ii) often cannot explicitly describe the model they have formed even when successful, and (iii) tend to be attracted to the same, simple models when multiple models fit the data. Examining a number of formal complexity me...
