Applying a probabilistic causal approach, we define a class of time series causal models (TSCM) based on stationary Bayesian networks. A TSCM can be seen as a structural VAR identified by the causal relations among the variables. We classify TSCMs into observationally equivalent classes by...

This paper uses a structurally estimated macroeconometric model, denoted the MC model, to evaluate inflation targeting in the United States. Various interest rate rules are tried with differing weights on inflation and output, and various optimal control problems are solved using differing...

A solution method and an estimation method for nonlinear rational expectations models are presented in this paper. The solution method can be used in forecasting and policy applications and can handle models with serial correlation and multiple viewpoint dates. When applied to linear models, the...

A forecast produced by an econometric model is a weighted aggregate of predetermined variables in the model. In many models the number of predetermined variables used is very large, often exceeding the number of observations. A method is proposed in this paper for testing an econometric model as...

I have been doing research in macroeconomics since the late 1960s, almost 50 years. In this paper I pause and take stock. The paper is part personal reflections on macroeconometric modeling, part a road map of the techniques of macroeconometric modeling, and part comments on what I think I have...

Frontmatter -- Contents -- Tables -- Figures -- Preface -- Abbreviations -- 1 Introduction -- 2 The MC Model -- 3 Interest Rate Effects -- 4 Testing the NAIRU Model -- 5 U.S. Wealth Effects -- 6 Testing for a New Economy in the 1990s -- 7 A "Modern" View of Macroeconomics -- 8 Estimated European...