Risultati della ricerca
Creator: Gourieroux, Christian, 1949, Renault, Eric., and Touzi, Nizar. Series: Simulation-based inference in econometrics Abstract:
This paper is interested in the small sample properties of the indirect inference procedure which has been previously studied only from an asymptotic point of view. First, we highlight the fact that the Andrews (1993) median-bias correction procedure for the autoregressive parameter of an AR(1) process is closely related to indirect inference; we prove that the counterpart of the median-bias correction for indirect inference estimator is an exact bias correction in the sense of a generalized mean. Next, assuming that the auxiliary estimator admits an Edgeworth expansion, we prove that indirect inference operates automatically a second order bias correction. The latter is a well known property of the Bootstrap estimator; we therefore provide a precise comparison between these two simulation based estimators.
Parola chiave: Edgeworth correction, Econometrics, Bootstrap, Bias correction, Economic models, Indirect inference, and Simulation Soggetto: C13 - Econometric and statistical methods : General - Estimation, C15 - Econometric and statistical methods : General - Simulation methods, C32 - Multiple or simultaneous equation models - Time-series models ; Dynamic quantile regressions, and C22 - Single equation models ; Single variables - Time-series models ; Dynamic quantile regressions
Creator: Diebold, Francis X., 1959- and Schuermann, Til. Series: Simulation-based inference in econometrics Abstract:
The possibility of exact maximum likelihood estimation of many observation-driven models remains an open question. Often only approximate maximum likelihood estimation is attempted, because the unconditional density needed for exact estimation is not known in closed form. Using simulation and nonparametric density estimation techniques that facilitate empirical likelihood evaluation, we develop an exact maximum likelihood procedure. We provide an illustrative application to the estimation of ARCH models, in which we compare the sampling properties of the exact estimator to those of several competitors. We find that, especially in situations of small samples and high persistence, efficiency gains are obtained.
Parola chiave: Econometrics, Observation-driven models, ARCH models, Estimation, and Exact maximum likelihood estimation Soggetto: C22 - Single equation models ; Single variables - Time-series models ; Dynamic quantile regressions
Creator: Litterman, Robert B. Series: Working paper (Federal Reserve Bank of Minneapolis. Research Dept.) Number: 274 Parola chiave: Bayesian analysis, BVAR, and Vector autoregression Soggetto: C11 - Econometric and statistical methods : General - Bayesian analysis and C53 - Econometric modeling - Forecasting and other model applications
Creator: Geweke, John. Series: Working paper (Federal Reserve Bank of Minneapolis. Research Dept.) Number: 526 Parola chiave: Simulation, Monte Carlo, and Econometrics Soggetto: C15 - Econometric and statistical methods : General - Simulation methods and C63 - Mathematical methods and programming - Computational techniques ; Simulation modeling
Creator: Braun, R. Anton. and Christiano, Lawrence J. Series: Working paper (Federal Reserve Bank of Minneapolis. Research Dept.) Number: 529 Abstract:
The money demand literature presents much conflicting evidence on this question. For example, Lucas (1988) reports unrestricted money demand regressions which seem to imply that long-run money demand elasticities are highly unstable across subsamples. At the same time, he also presents evidence from money demand regressions with the income elasticity restricted to unity which seem to suggest stability. We conduct a formal analysis which weighs these apparently conflicting facts to determine which hypothesis is more plausible; the hypothesis that money demand is stable, or the hypothesis that money demand is unstable. We find that the stability hypothesis is the more plausible one. Thus, according to our data set, the answer to the question in the title is "yes".
Parola chiave: M1, Money demand, Regression analysis, Money demand regressions, and Money supply Soggetto: E41 - Money and interest rates - Demand for money and E51 - Monetary policy, central banking, and the supply of money and credit - Money supply ; Credit ; Money multipliers
Creator: Anderson, Paul A. Series: Working paper (Federal Reserve Bank of Minneapolis. Research Dept.) Number: 61 Abstract:
This paper puts forward a method for simulating an existing macroeconometric model while maintaining the additional assumption that individuals form their expectations rationally. This simulation technique is a first response to Lucas' criticism that standard econometric policy evaluation allows policy rules to change but doesn't allow expectations rules to change as economic theory predicts they will. The technique is applied to a version of the St. Louis Federal Reserve Model with interesting results. The rational expectations version of the St. Louis Model exhibits the same neutrality with respect to certain policy rules as small, analytic rational expectations models considered by Lucas, Sargent, and Wallace.
Parola chiave: Rational expectations theory, Simulation, and Forecasting Soggetto: C53 - Econometric modeling - Forecasting and other model applications
Creator: Todd, Richard M. Series: Business analysis committee meeting Descrizione:
Version without Software Appendix appears on the Federal Reserve Bank of Minneapolis Web site at http://www.minneapolisfed.org/publications_papers/pub_display.cfm?id=571
Parola chiave: BVAR, Vector autoregression, and Bayesian analysis Soggetto: C53 - Econometric modeling - Forecasting and other model applications
Creator: Roberds, William. Series: Business analysis committee meeting Abstract:
One of the more significant developments in econometric modeling over the past decade has been the invention of the forecasting technique known as Bayesian vector autoregression (BVAR). This paper provides a detailed description of the process of specifying a BVAR model of quarterly time series on the U.S. macroeconomy. The postsample forecasting performance of the model is evaluated at an informal level by comparing the model's performance to certain naive forecasting methods, and is evaluated at a formal level by means of efficiency tests. Although the null hypothesis of efficiency is rejected for the model's forecasts, the accuracy of the model exceeds that of naive forecasting methods, and seems comparable to that of commercial forecasting firms for early quarter forecasts.
Parola chiave: BVAR, Vector autoregression, and Bayesian analysis Soggetto: C11 - Econometric and statistical methods : General - Bayesian analysis and C53 - Econometric modeling - Forecasting and other model applications