Sims (1977) discusses the challenges of identifying large-scale macroeconomic models. He argues that the identification of these models is often based on inappropriate "a priori" restrictions, leading to unreliable conclusions. He highlights that while these models are used for forecasting and policy analysis, their identification is questionable. He suggests that the usual methods of identification are insufficient and that alternative approaches are needed. He also discusses the role of expectations in identification, noting that rational expectations complicate identification. He argues that the identification of large models is often based on false assumptions, but these models can still be useful for forecasting and policy analysis. He suggests that alternative strategies for empirical macroeconomics, such as using unrestricted reduced forms, could be more effective. He also discusses the implications of these findings for policy-making and the need for more systematic approaches to model specification.Sims (1977) discusses the challenges of identifying large-scale macroeconomic models. He argues that the identification of these models is often based on inappropriate "a priori" restrictions, leading to unreliable conclusions. He highlights that while these models are used for forecasting and policy analysis, their identification is questionable. He suggests that the usual methods of identification are insufficient and that alternative approaches are needed. He also discusses the role of expectations in identification, noting that rational expectations complicate identification. He argues that the identification of large models is often based on false assumptions, but these models can still be useful for forecasting and policy analysis. He suggests that alternative strategies for empirical macroeconomics, such as using unrestricted reduced forms, could be more effective. He also discusses the implications of these findings for policy-making and the need for more systematic approaches to model specification.