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摘要翻译：
通过将间接推理估计重新定义为预测而不是最小化，并使用正则化回归，我们可以绕过估计的三个主要问题：选择汇总统计量、定义距离函数和数值最小化。通过用分类代替回归，我们可以将这种方法扩展到模型选择上。我们给出了三个例子：一个统计拟合，一个简单的真实经济周期模型的参数化和一个基于代理的渔业模型中的启发式选择。其结果是一种自动选择汇总统计信息、对其进行加权并使用它们来参数化模型的方法，而无需运行任何直接的最小化。
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英文标题：
《Indirect inference through prediction》
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作者：
Ernesto Carrella, Richard M. Bailey, Jens Koed Madsen
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最新提交年份：
2018
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分类信息：
一级分类：Economics 经济学
二级分类：Econometrics 计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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英文摘要：
By recasting indirect inference estimation as a prediction rather than a minimization and by using regularized regressions, we can bypass the three major problems of estimation: selecting the summary statistics, defining the distance function and minimizing it numerically. By substituting regression with classification we can extend this approach to model selection as well. We present three examples: a statistical fit, the parametrization of a simple real business cycle model and heuristics selection in a fishery agent-based model. The outcome is a method that automatically chooses summary statistics, weighs them and use them to parametrize models without running any direct minimization.
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PDF链接：
https://arxiv.org/pdf/1807.01579
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