This paper proposes a moment-matching method for approximating vector autoregressions by finite-state Markov chains. The Markov chain is constructed by targeting the conditional moments of the underlying continuous process. The proposed method is more robust to the number of discrete values and...

This paper provides some test cases, called circuits, for the evaluation of Gaussian likelihood maximization algorithms of the cointegrated vector autoregressive model. Both I(1) and I(2) models are considered. The performance of algorithms is compared first in terms of effectiveness, defined as...

This note examines the accuracy of methods that are commonly used to approximate AR(1)-processes with discrete Markov chains. The quadrature-based method suggested by Tauchen and Hussey (1991) generates excellent approximations with a small number of nodes when the autocorrelation is low or...

This paper investigates the power properties of the Sargan test in the presence of measurement errors in dynamic panel data models. The general conclusion from the Monte Carlo simulations is that the Sargan test, in many cases, leads the econometrician to accept misspecified models with...

The dynamics of the US economy are modelled using a time-varying structural vector autoregression that incorporates information from the yield curve. We find important changes in the dynamics of macroeconomic variables such as inflation and the federal funds rate. In addition our results suggest...

Vector autoregressions (VARs) are economically interpretable only when identified by being transformed into a structural form (the SVAR) in which the contemporaneous variables stand in a well-defined causal order. These identifying transformations are not unique. It is widely believed that...

The superiority of full information approaches when estimating a system of equation is well known for large samples. However, less is known about the small sample properties of these estimators relative to limited information approachs. This is especially true for the context of Panel data...