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on Discrete Choice Models |
| By: | Riccardo Di Francesco (DEF, University of Rome "Tor Vergata") |
| Abstract: | Empirical studies in various social sciences often involve categorical outcomes with inherent ordering, such as self-evaluations of subjective well-being and self-assessments in health domains. While ordered choice models, such as the ordered logit and ordered probit, are popular tools for analyzing these outcomes, they may impose restrictive parametric and distributional assumptions. This paper introduces a novel estimator, the ordered correlation forest, that can naturally handle non-linearities in the data and does not assume a specific error term distribution. The proposed estimator modifies a standard random forest splitting criterion to build a collection of forests, each estimating the conditional probability of a single class. Under an “honesty” condition, predictions are consistent and asymptotically normal. The weights induced by each forest are used to obtain standard errors for the predicted probabilities and the covariates’ marginal effects. Evidence from synthetic data shows that the proposed estimator features a superior prediction performance than alternative forest-based estimators and demonstrates its ability to construct valid confidence intervals for the covariates’ marginal effects. |
| Keywords: | Ordered non-numeric outcomes, choice probabilities, machine learning |
| JEL: | C14 C25 C55 |
| Date: | 2024–05–06 |
| URL: | http://d.repec.org/n?u=RePEc:rtv:ceisrp:577&r= |
| By: | Elena Panova (TSE-R - Toulouse School of Economics - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement) |
| Abstract: | We consider the problem of sharing the cost of a fixed tree-network among users with differentiated willingness to pay for the good supplied through the network. We find that the associated value-sharing problem is convex, hence, the core is large and we axiomatize a new, computationally simple core selection based on the idea of proportionality. |
| Keywords: | Sharing network cost, Core, Proportional allocation |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-04556220&r= |
| By: | Ayden Higgins (University of Oxford); Koen Jochmans (TSE-R - Toulouse School of Economics - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement) |
| Abstract: | The maximum-likelihood estimator of nonlinear panel data models with fixed effects is asymptotically biased under rectangular-array asymptotics. The literature has devoted substantial effort to devising methods that correct for this bias as a means to salvage standard inferential procedures. The chief purpose of this paper is to show that the (recursive, parametric) bootstrap replicates the asymptotic distribution of the (uncorrected) maximum-likelihood estimator and of the likelihood-ratio statistic. This justifies the use of confidence sets and decision rules for hypothesis testing constructed via conventional bootstrap methods. No modification for the presence of bias needs to be made. |
| Keywords: | Bootstrap, Fixed effects, Incidental-parameter problem, Inference, Panel data |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-04557288&r= |
| By: | Sudhir A. Shah (Department of Economics, Delhi School of Economics) |
| Abstract: | We propose an asset’s money-metric value as the appropriate representation of its subjective value to an investor. This value is expressed in monetary terms and is invariant across equivalent utility representations of the investor’s preference. The ordering of money-metric values across assets matches the investor’s preference ordering over the assets.The money-metric value of a risky asset is inversely related to the investor’s risk aversion, while the money-metric value of a risk-free asset is uniform across preferences with comparable risk-aversion. Finally, an asset’s arbitrage-free market price is the sum of its money-metric value and the investor’s willingness-to-pay for fully de-risking the asset. JEL Code: G11, G12 |
| Keywords: | money-metric asset valuation, arbitrage-free prices, risk aversion |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:cde:cdewps:347&r= |
| By: | Xin Liu |
| Abstract: | I propose a quantile-based nonadditive fixed effects panel model to study heterogeneous causal effects. Similar to standard fixed effects (FE) model, my model allows arbitrary dependence between regressors and unobserved heterogeneity, but it generalizes the additive separability of standard FE to allow the unobserved heterogeneity to enter nonseparably. Similar to structural quantile models, my model's random coefficient vector depends on an unobserved, scalar ''rank'' variable, in which outcomes (excluding an additive noise term) are monotonic at a particular value of the regressor vector, which is much weaker than the conventional monotonicity assumption that must hold at all possible values. This rank is assumed to be stable over time, which is often more economically plausible than the panel quantile studies that assume individual rank is iid over time. It uncovers the heterogeneous causal effects as functions of the rank variable. I provide identification and estimation results, establishing uniform consistency and uniform asymptotic normality of the heterogeneous causal effect function estimator. Simulations show reasonable finite-sample performance and show my model complements fixed effects quantile regression. Finally, I illustrate the proposed methods by examining the causal effect of a country's oil wealth on its military defense spending. |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2405.03826&r= |