To this end we employ three automatic modelling devices. These models are often difficult to estimate, and we follow the idea of White (2006) to transform the speci?fication and non- linear estimation problem into a linear model selection and estimation problem. What makes these models interesting in the present context is that they form a class of universal approximators and may be expected to work well during exceptional periods such as major economic crises. The focus is on a specific class of models, the so-called single hidden-layer feedforward autoregressive neural net- work models. In this work we consider forecasting macroeconomic variables dur- ing an economic crisis. We provide necessary and sufficient conditions for identification of covariate-conditioned average causal effects, parametric and nonparametric estimation results, and new tests for unconfoundedness. For example, the settable system approach permits identification and estimation of causal effects without requiring exogenous instruments, generalizing the classical structural equations approach it relaxes the stable unit treatment value assumption of the treatment effect approach and provides significant insight into the selection of covariates and it accommodates mutual causality, generalizing the DAG approach. The settable system framework nests these prior approaches, while affording significant improvements to each. This paper unifies three complementary approaches to defining, identifying, and estimating causal effects: the classical structural equations approach of the Cowles Commision the treatment effects framework of Rubin (1974) and Rosenbaum and Rubin (1983) and the Directed Acyclic Graph (DAG) approach of Pearl.
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