Causal Inference in Latent Class Analysis Lanza, Stephanie T.; Coffman, Donna L.; Xu, Shu
Structural equation modeling,
20/7/1/, Letnik:
20, Številka:
3
Journal Article
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The integration of modern methods for causal inference with latent class analysis (LCA) allows social, behavioral, and health researchers to address important questions about the determinants of ...latent class membership. In this article, 2 propensity score techniques, matching and inverse propensity weighting, are demonstrated for conducting causal inference in LCA. The different causal questions that can be addressed with these techniques are carefully delineated. An empirical analysis based on data from the National Longitudinal Survey of Youth 1979 is presented, where college enrollment is examined as the exposure (i.e., treatment) variable and its causal effect on adult substance use latent class membership is estimated. A step-by-step procedure for conducting causal inference in LCA, including multiple imputation of missing data on the confounders, exposure variable, and multivariate outcome, is included. Sample syntax for carrying out the analysis using SAS and R is given in an appendix.
This article considers the identification conditions of confirmatory factor analysis (CFA) models for ordered categorical outcomes with invariance of different types of parameters across groups. The ...current practice of invariance testing is to first identify a model with only configural invariance and then test the invariance of parameters based on this identified baseline model. This approach is not optimal because different identification conditions on this baseline model identify the scales of latent continuous responses in different ways. Once an invariance condition is imposed on a parameter, these identification conditions may become restrictions and define statistically non-equivalent models, leading to different conclusions. By analyzing the transformation that leaves the model-implied probabilities of response patterns unchanged, we give identification conditions for models with invariance of different types of parameters without referring to a specific parametrization of the baseline model. Tests based on this approach have the advantage that they do not depend on the specific identification condition chosen for the baseline model.
The pollution haven hypothesis (PHH) posits that production within polluting industries will shift to locations with lax environmental regulation. While straightforward, the existing empirical ...literature is inconclusive owing to two shortcomings. First, unobserved heterogeneity and measurement error are typically ignored due to the lack of a credible, traditional instrumental variable for regulation. Second, geographic spillovers have not been adequately incorporated into tests of the PHH. We overcome these issues utilizing two novel identification strategies within a model incorporating spillovers. Using US state-level data, own environmental regulation negatively impacts inbound foreign direct investment. Moreover, endogeneity is both statistically and economically relevant.
Deep hedging Buehler, H.; Gonon, L.; Teichmann, J. ...
Quantitative finance,
08/2019, Letnik:
19, Številka:
8
Journal Article
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We present a framework for hedging a portfolio of derivatives in the presence of market frictions such as transaction costs, liquidity constraints or risk limits using modern deep reinforcement ...machine learning methods. We discuss how standard reinforcement learning methods can be applied to non-linear reward structures, i.e. in our case convex risk measures. As a general contribution to the use of deep learning for stochastic processes, we also show in Section 4 that the set of constrained trading strategies used by our algorithm is large enough to ε-approximate any optimal solution. Our algorithm can be implemented efficiently even in high-dimensional situations using modern machine learning tools. Its structure does not depend on specific market dynamics, and generalizes across hedging instruments including the use of liquid derivatives. Its computational performance is largely invariant in the size of the portfolio as it depends mainly on the number of hedging instruments available. We illustrate our approach by an experiment on the S&P500 index and by showing the effect on hedging under transaction costs in a synthetic market driven by the Heston model, where we outperform the standard 'complete-market' solution.
Abstract Judgment-aggregation theory has always focused on the attainment of rational collective judgments. But so far, rationality has been understood in static terms: as coherence of judgments at a ...given time, defined as consistency, completeness, and/or deductive closure. This paper asks whether collective judgments can be dynamically rational, so that they change rationally in response to new information. Formally, a judgment aggregation rule is dynamically rational with respect to a given revision operator if, whenever all individuals revise their judgments in light of some information (a learnt proposition), then the new aggregate judgments are the old ones revised in light of this information, i.e., aggregation and revision commute. We prove an impossibility theorem: if the propositions on the agenda are non-trivially connected, no judgment aggregation rule with standard properties is dynamically rational with respect to any revision operator satisfying some basic conditions. Our theorem is the dynamic-rationality counterpart of some well-known impossibility theorems for static rationality. We also explore how dynamic rationality might be achieved by relaxing some of the conditions on the aggregation rule and/or the revision operator. Notably, premise-based aggregation rules are dynamically rational with respect to so-called premise-based revision operators.
A tutorial on Bayesian nonparametric models Gershman, Samuel J.; Blei, David M.
Journal of mathematical psychology,
February 2012, 2012-2-00, 20120201, Letnik:
56, Številka:
1
Journal Article
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A key problem in statistical modeling is model selection, that is, how to choose a model at an appropriate level of complexity. This problem appears in many settings, most prominently in choosing the ...number of clusters in mixture models or the number of factors in factor analysis. In this tutorial, we describe Bayesian nonparametric methods, a class of methods that side-steps this issue by allowing the data to determine the complexity of the model. This tutorial is a high-level introduction to Bayesian nonparametric methods and contains several examples of their application.
► Bayesian nonparametric models provide a way to infer the appropriate complexity of a model from data. ► We review several standard nonparametric models, explaining how they can be used for practical data analysis. ► The mathematical foundations of these methods are briefly summarized.
We develop a new quantile-based panel data framework to study the nature of income persistence and the transmission of income shocks to consumption. Log-earnings are the sum of a general Markovian ...persistent component and a transitory innovation. The persistence of past shocks to earnings is allowed to vary according to the size and sign of the current shock. Consumption is modeled as an age-dependent non-linear function of assets, unobservable tastes, and the two earnings components. We establish the nonparametric identification of the nonlinear earnings process and of the consumption policy rule. Exploiting the enhanced consumption and asset data in recent waves of the Panel Study of Income Dynamics, we find that the earnings process features nonlinear persistence and conditional skewness. We confirm these results using population register data from Norway. We then show that the impact of earnings shocks varies substantially across earnings histories, and that this nonlinearity drives heterogeneous consumption responses. The framework provides new empirical measures of partial insurance in which the transmission of income shocks to consumption varies systematically with assets, the level of the shock, and the history of past shocks.
We investigate how urban railroad and highway configurations have influenced urban form in Chinese cities since 1990. Each radial highway displaces 4% of central city population to surrounding ...regions, and ring roads displace about an additional 20%, with stronger effects in the richer coastal and central regions. Each radial railroad reduces central city industrial GDP by about 20%, with ring roads displacing an additional 50%. We provide evidence that radial highways decentralize service sector activity, radial railroads decentralize industrial activity, and ring roads decentralize both. Historical transportation infrastructure provides identifying variation in more recent measures of infrastructure.
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