Revealed Preference of the Federal Reserve
Using Inverse-Control Theory to Interpret the Policy Equation of a Vector Autoregression
Bibliographic Data
| ID | 19419262 |
|---|---|
| Authors | Michael K Salemi (University of North Carolina, corresponding author) |
| Year | 1995 |
| Volume | 13 |
| Issue | 4 |
| Pages | 419-433 |
| Publication date | 1995-10-01 |
| Open Access | No |
| Type | ARTICLE |
| Venue | Journal of Business and Economic Statistics (JOURNAL) |
| Journal identifiers | ISSN: 0735-0015 • E-ISSN: 1537-2707 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/07350015.1995.10524616 |
| OpenAlex | W2117472037 |
| Language | EN |
| Citations received | 3 |
| References cited | 26 |
How close is observed Federal Reserve policy to policy that would be optimal in the control-theory sense? The question is addressed by using an inverse-control methodology. Federal Reserve policy is characterized by a feedback equation for either changes in the interest rate or money stock. Optimal policy is characterized by solution of the Ricatti equation. Parameters are estimated that characterize the relative importance to the Federal Reserve of stabilizing output, inflation, interest rates, and money growth. The period of study is 1947 through 1992. Evidence is uncovered that policy is optimal within distinct regimes
Econometrics · Economics · Federal funds · Interest rate · Macroeconomics · Microeconomics · Monetary policy · Preference · Vector autoregression · Economic theories and models · Economic Theory and Policy · Monetary Policy and Economic Impact
Lectures on Macroeconomics
Rules, discretion and reputation in a model of monetary policy
Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation
Central Bank Independence and Macroeconomic Performance
Macroeconomics and Reality
Forecasting with Bayesian Vector Autoregressions
Solving the Stochastic Growth Model by Linear-Quadratic Approximation
Forecasting With Bayesian Vector Autoregressions—Five Years of Experience
Rules Rather than Discretion
| Citations per year | 0.13 |
|---|---|
| Citation span | 2003 - 2007 (5) |
| Highly cited | No |