The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Laine Thomas - One of the best experts on this subject based on the ideXlab platform.
-
propensity score weighting under limited overlap and Model Misspecification
Statistical Methods in Medical Research, 2020Co-Authors: Yunji Zhou, Roland A Matsouaka, Laine ThomasAbstract:Propensity score weighting methods are often used in non-randomized studies to adjust for confounding and assess treatment effects. The most popular among them, the inverse probability weighting, assigns weights that are proportional to the inverse of the conditional probability of a specific treatment assignment, given observed covariates. A key requirement for inverse probability weighting estimation is the positivity assumption, i.e. the propensity score must be bounded away from 0 and 1. In practice, violations of the positivity assumption often manifest by the presence of limited overlap in the propensity score distributions between treatment groups. When these practical violations occur, a small number of highly influential inverse probability weights may lead to unstable inverse probability weighting estimators, with biased estimates and large variances. To mitigate these issues, a number of alternative methods have been proposed, including inverse probability weighting trimming, overlap weights, matching weights, and entropy weights. Because overlap weights, matching weights, and entropy weights target the population for whom there is equipoise (and with adequate overlap) and their estimands depend on the true propensity score, a common criticism is that these estimators may be more sensitive to Misspecifications of the propensity score Model. In this paper, we conduct extensive simulation studies to compare the performances of inverse probability weighting and inverse probability weighting trimming against those of overlap weights, matching weights, and entropy weights under limited overlap and misspecified propensity score Models. Across the wide range of scenarios we considered, overlap weights, matching weights, and entropy weights consistently outperform inverse probability weighting in terms of bias, root mean squared error, and coverage probability.
Thomas J Sargent - One of the best experts on this subject based on the ideXlab platform.
-
structured ambiguity and Model Misspecification
Journal of Economic Theory, 2020Co-Authors: Lars Peter Hansen, Thomas J SargentAbstract:Abstract A decision maker is averse to not knowing a prior over a set of restricted structured Models (ambiguity) and suspects that each structured Model is misspecified. The decision maker evaluates intertemporal plans under all of the structured Models and, to recognize possible Misspecifications, under unstructured alternatives that are statistically close to them. Likelihood ratio processes are used to represent unstructured alternative Models, while relative entropy restricts a set of unstructured Models. A set of structured Models might be finite or indexed by a finite-dimensional vector of unknown parameters that could vary in unknown ways over time. We Model such a decision maker with a dynamic version of variational preferences and revisit topics including dynamic consistency and admissibility.
-
structured uncertainty and Model Misspecification
2019Co-Authors: Lars Peter Hansen, Thomas J SargentAbstract:An ambiguity averse decision maker evaluates plans under a restricted family of what we call structured Models and unstructured alternatives that are statistically close to them. The structured Models can include parametric Models in which parameter values vary over time in ways that the decision maker cannot describe probabilistically. Because he suspects that all parametric Models are misspecified, the decision maker also evaluates plans under alternative probability distributions with much less structure.
-
robust control and Model Misspecification
Journal of Economic Theory, 2006Co-Authors: Lars Peter Hansen, Thomas J Sargent, Gauhar Turmuhambetova, Noah WilliamsAbstract:Abstract A decision maker fears that data are generated by a statistical perturbation of an approximating Model that is either a controlled diffusion or a controlled measure over continuous functions of time. A perturbation is constrained in terms of its relative entropy. Several different two-player zero-sum games that yield robust decision rules are related to one another, to the max–min expected utility theory of Gilboa and Schmeidler [Maxmin expected utility with non-unique prior, J. Math. Econ. 18 (1989) 141–153], and to the recursive risk-sensitivity criterion described in discrete time by Hansen and Sargent [Discounted linear exponential quadratic Gaussian control, IEEE Trans. Automat. Control 40 (5) (1995) 968–971]. To represent perturbed Models, we use martingales on the probability space associated with the approximating Model. Alternative sequential and nonsequential versions of robust control theory imply identical robust decision rules that are dynamically consistent in a useful sense.
Lars Peter Hansen - One of the best experts on this subject based on the ideXlab platform.
-
structured ambiguity and Model Misspecification
Journal of Economic Theory, 2020Co-Authors: Lars Peter Hansen, Thomas J SargentAbstract:Abstract A decision maker is averse to not knowing a prior over a set of restricted structured Models (ambiguity) and suspects that each structured Model is misspecified. The decision maker evaluates intertemporal plans under all of the structured Models and, to recognize possible Misspecifications, under unstructured alternatives that are statistically close to them. Likelihood ratio processes are used to represent unstructured alternative Models, while relative entropy restricts a set of unstructured Models. A set of structured Models might be finite or indexed by a finite-dimensional vector of unknown parameters that could vary in unknown ways over time. We Model such a decision maker with a dynamic version of variational preferences and revisit topics including dynamic consistency and admissibility.
-
structured uncertainty and Model Misspecification
2019Co-Authors: Lars Peter Hansen, Thomas J SargentAbstract:An ambiguity averse decision maker evaluates plans under a restricted family of what we call structured Models and unstructured alternatives that are statistically close to them. The structured Models can include parametric Models in which parameter values vary over time in ways that the decision maker cannot describe probabilistically. Because he suspects that all parametric Models are misspecified, the decision maker also evaluates plans under alternative probability distributions with much less structure.
-
robust control and Model Misspecification
Journal of Economic Theory, 2006Co-Authors: Lars Peter Hansen, Thomas J Sargent, Gauhar Turmuhambetova, Noah WilliamsAbstract:Abstract A decision maker fears that data are generated by a statistical perturbation of an approximating Model that is either a controlled diffusion or a controlled measure over continuous functions of time. A perturbation is constrained in terms of its relative entropy. Several different two-player zero-sum games that yield robust decision rules are related to one another, to the max–min expected utility theory of Gilboa and Schmeidler [Maxmin expected utility with non-unique prior, J. Math. Econ. 18 (1989) 141–153], and to the recursive risk-sensitivity criterion described in discrete time by Hansen and Sargent [Discounted linear exponential quadratic Gaussian control, IEEE Trans. Automat. Control 40 (5) (1995) 968–971]. To represent perturbed Models, we use martingales on the probability space associated with the approximating Model. Alternative sequential and nonsequential versions of robust control theory imply identical robust decision rules that are dynamically consistent in a useful sense.
Raman Uppal - One of the best experts on this subject based on the ideXlab platform.
-
portfolio choice with Model Misspecification a foundation for alpha and beta portfolios
Social Science Research Network, 2016Co-Authors: Raman Uppal, Paolo ZaffaroniAbstract:Our objective is to investigate the effect of Model Misspecification on mean-variance portfolios and to show how asset-pricing theory and asymptotic analysis (for large number of assets) can be used to provide powerful solutions to mitigate Misspecification. The starting point of our analysis is the Arbitrage Pricing Theory (APT). We extend the APT to show that it can capture not just small pricing errors that are independent of factors, but also large pricing errors from mismeasured or missing factors. Our key insight is that, instead of treating Misspecification directly in the mean-variance portfolio, it is better to first decompose the portfolio into components that correspond to the two components of returns in the APT: a ``beta'' portfolio that depends on factor risk premia and an ``alpha'' portfolio that depends only on pricing errors. For the beta portfolio, we treat Misspecification using asymptotic analysis: as the number of assets increases, we show that the weights of the alpha portfolio dominate those of the beta portfolio, leading to an expression for mean-variance portfolio weights that is immune to beta Misspecification. For the alpha portfolio, we treat Misspecification by imposing the APT restriction on alphas, which serves both as an identification condition and a shrinkage constraint. Finally, we demonstrate that our approach achieves an out-of-sample Sharpe ratio that is more than double that of the equally weighted portfolio.
-
Model Misspecification and underdiversification
Journal of Finance, 2003Co-Authors: Raman Uppal, Tan WangAbstract:In this paper, we study intertemporal portfolio choice when an investor accounts explicitly for Model Misspecification. We develop a framework that allows for ambiguity about not just the joint distribution of returns for all stocks in the portfolio, but also for different levels of ambiguity for the marginal distribution of returns for any subset of these stocks. We find that when the overall ambiguity about the joint distribution of returns is high, then small differences in ambiguity for the marginal return distribution will result in a portfolio that is significantly underdiversified relative to the standard mean-variance portfolio
-
Model Misspecification and under diversification
Social Science Research Network, 2002Co-Authors: Tan Wang, Raman UppalAbstract:In this paper we develop a Model of intertemporal portfolio choice where an investor accounts explicitly for the possibility of Model Misspecification. This work is motivated by the difficulty in estimating precisely the probability law for asset returns. Our contribution is to develop a framework that allows for ambiguity about the joint distribution of returns for all stocks being considered for the portfolio, and also for different levels of ambiguity for the marginal distribution of returns for any subset of these stocks. We then use this framework to derive in closed-form the optimal portfolio weights of an investor who accounts for Model Misspecification. We illustrate the Model by calibrating it to data on international equity returns. The calibration shows that when the overall ambiguity about the joint distribution of returns is high, then small differences in ambiguity for the marginal return distribution will result in a portfolio that is significantly under-diversified relative to the standard mean-variance portfolio. Keywords :Portfolio choice,uncertainty,ambiguity,robust control
Sung Kyun Park - One of the best experts on this subject based on the ideXlab platform.
-
graphical diagnostics to check Model Misspecification for the proportional odds regression Model
Statistics in Medicine, 2009Co-Authors: Bhramar Mukherjee, Thomas F Suesse, David Sparrow, Sung Kyun ParkAbstract:The cumulative logit or the proportional odds regression Model is commonly used to study covariate effects on ordinal responses. This paper provides some graphical and numerical methods for checking the adequacy of the proportional odds regression Model. The methods focus on evaluating functional Misspecification for specific covariate effects, but Misspecification of the link function can also be dealt with under the same framework. For the logistic regression Model with binary responses, Arbogast and Lin (Statist. Med. 2005; 24:229–247) developed similar graphical and numerical methods for assessing the adequacy of the Model using the cumulative sums of residuals. The paper generalizes their methods to ordinal responses and illustrates them using an example from the VA Normative Aging Study. Simulation studies comparing the performance of the different diagnostic methods indicate that some of the graphical methods are more powerful in detecting Model Misspecification than the Hosmer–Lemeshow-type goodness-of-fit statistics for the class of Models studied. Copyright © 2008 John Wiley & Sons, Ltd.
-
graphical diagnostics to check Model Misspecification for the proportional odds regression Model
Statistics in Medicine, 2009Co-Authors: Ivy Liu, Bhramar Mukherjee, Thomas F Suesse, David Sparrow, Sung Kyun ParkAbstract:The cumulative logit or the proportional odds regression Model is commonly used to study covariate effects on ordinal responses. This paper provides some graphical and numerical methods for checking the adequacy of the proportional odds regression Model. The methods focus on evaluating functional Misspecification for specific covariate effects, but Misspecification of the link function can also be dealt with under the same framework. For the logistic regression Model with binary responses, Arbogast and Lin (Statist. Med. 2005; 24:229-247) developed similar graphical and numerical methods for assessing the adequacy of the Model using the cumulative sums of residuals. The paper generalizes their methods to ordinal responses and illustrates them using an example from the VA Normative Aging Study. Simulation studies comparing the performance of the different diagnostic methods indicate that some of the graphical methods are more powerful in detecting Model Misspecification than the Hosmer-Lemeshow-type goodness-of-fit statistics for the class of Models studied.
