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 examines empirically the nonlinear business cycle dynamics due to the presence of financial frictions. Using a threshold vector auto regression, the authors estimate the behavior of interest rate shocks in which a regime change occurs if the two respective threshold variables namely...

The standard approach to discretizing VARs uses tensor grids. However, when the VAR components exhibit significant unconditional correlations or when there are more than a few variables, this approach creates large inefficiencies because some discretized states will be visited with only...

This chapter provides an overview of solution and estimation techniques for dynamic stochastic general equilibrium models. We cover the foundations of numerical approximation techniques as well as statistical inference and survey the latest developments in the field.

This paper is concerned with the apparent change in the U.S. oil price-macroeconomy relationship. It is investigated to what extent this change can be accounted for by the large oil price surges witnessed in the 1970s. The innovative approach of rolling impulse responses is applied and both the...

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...