We propose a new information criterion for impulse response function matching estimators (IRFMEs) of the structural parameters of dynamic stochastic general equilibrium (DSGE) macroeconomic models. An advantage of our procedure is that it allows researchers to select the impulse responses that...

This paper introduces methods for computing impulse response functions that do not require specification and estimation of the unknown dynamic multivariate system itself. The central idea behind these methods is to estimate flexible local projections at each period of interest rather than...

A covariance-stationary vector of variables has a Wold representation whose coefficients can be semiparametrically estimated by local projections (Jordà, 2005). Substituting the Wold representations for variables in model expressions generates restrictions that can be used by the method of...

Poor identification of individual impulse response coefficients does not necessarily mean that an impulse response is imprecisely estimated. This paper introduces a three-pronged approach on how to communicate uncertainty of impulse response estimates: (1) withWald tests of joint significance; (2)...

A long tradition in macro finance studies the joint dynamics of aggregate stock returns and dividends using vector autoregressions (VARs), imposing the cross-equation restrictions implied by the Campbell-Shiller (CS) identity to sharpen inference. We take a Bayesian perspective and develop...

We bring together some recent advances in the literature on vector autoregressive moving-average models creating a relatively simple specification and estimation strategy for the cointegrated case. We show that in the cointegrated case with fixed initial values there exists a so-called final...

Rationality of early release data is typically tested using linear regressions. Thus, failure to reject the null does not rule out the possibility of nonlinear dependence. This paper proposes two tests that have power against generic nonlinear alternatives. A Monte Carlo study shows that the...

In this paper, we empirically assess the extent to which early release inefficiency and definitional change affect prediction precision. In particular, we carry out a series of ex-ante prediction experiments in order to examine: the marginal predictive content of the revision process, the...

Our proposed local vector autoregressive (LVAR) model has time-varying parameters that allow it to be safely used in both stationary and non-stationary situations. The estimation is conducted over an interval of local homogeneity where the parameters are approximately constant. The local...

We propose an Adaptive Dynamic Nelson-Siegel (ADNS) model to adaptively forecast the yield curve. The model has a simple yet flexible structure and can be safely applied to both stationary and nonstationary situations with different sources of change. For the 3- to 12-months ahead out-of-sample...