Estimation of quasi-rational DSGE monetary models

This paper proposes the estimation of small-scale dynamic stochastic general equilibrium (DSGE) monetary models under the quasi-rational expectations (QRE) hypothesis. The QRE-DSGE model is based on the idea that the determinate reduced form solution associated with the structural model, if it exists, must have the same lag structure as the ‘best fitting’ vector autoregressive (VAR) model for the observed time series. After discussing solution properties and the local identifiability of the model, a likelihood-based iterative algorithm for estimating the structural parameters and testing the data adequacy of the system is proposed. A Monte Carlo experiment shows that, even controlling for the omitted dynamics bias, the over-rejection of the nonlinear cross-equation restrictions when asymptotic critical values are used and variables are highly persistent is a relevant issue in finite samples. An application based on euro area data illustrates the advantages of using error-correcting formulations of the QRE-DSGE model when the inflation rate and the short-term interest rate are approximated as difference stationary processes. A parametric bootstrap version of the likelihood-ratio test for the implied cross-equation restrictions does not reject the estimated QRE-DSGE model.Dynamic stochastic general equilibrium model, Maximum Likelihood estimation, Quasi-Rational Expectations, VAR. Modelli DSGE, Stima di massima verosimiglianza, Aspettative Quasi-Razionali, Modelli VAR.

Determinacy, indeterminacy and dynamic misspecification in linear rational expectations models

This paper proposes a testing strategy for the null hypothesis that a multivariate linear rational expectations (LRE) model has a unique stable solution (determinacy) against the alternative of multiple stable solutions (indeterminacy). Under a proper set of identification restrictions, determinacy is investigated by a misspecification-type approach in which the result of the overidentifying restrictions test obtained from the estimation of the LRE model through a version of generalized method of moments is combined with the result of a likelihood-based test for the cross-equation restrictions that the LRE places on its finite order reduced form under determinacy. This approach (i) circumvents the nonstandard inferential problem that a purely likelihood-based approach implies because of the presence of nuisance parameters that appear under the alternative but not under the null, (ii) does not involve inequality parametric restrictions and nonstandard asymptotic distributions, and (iii) gives rise to a joint test which is consistent against indeterminacy almost everywhere in the space of nuisance parameters, i.e. except for a point of zero measure which gives rise to minimum state variable solutions, and is also consistent against the dynamic misspecification of the LRE model. Monte Carlo simulations show that the testing strategy delivers reasonable size coverage and power in finite samples. An empirical illustration focuses on the determinacy/indeterminacy of a New Keynesian monetary business cycle model for the US.Determinatezza, Indeterminatezza, Massima verosimiglianza, Metodo generalizzato dei momenti, Modello lineare con aspettative, Identificazione, Variabili Strumentali, VAR,VARMA Determinacy, Generalized method of moments, Indeterminacy, LRE model, Identification, Instrumental Variables, Maximum Likelihood, VAR, VARMA

Self-gravitating systems in a three-dimensional expanding Universe

The non-linear evolution of one-dimensional perturbations in a three-dimensional expanding Universe is considered. A general Lagrangian scheme is derived, and compared to two previously introduced approximate models. These models are simulated with heap-based event-driven numerical procedure, that allows for the study of large systems, averaged over many realizations of random initial conditions. One of the models is shown to be qualitatively, and, in some respects, concerning mass aggregation, quantitatively similar to the adhesion model.Comment: 11 figures, simulations of Q model include