We propose a method to estimate time invariant cyclical dynamic stochastic general equilibrium models using the information provided by a variety of filters. We treat data filtered with alternative procedures as contaminated proxies of the relevant model-based quantities and estimate structural...

We propose a method to estimate time invariant cyclical DSGE models using the information provided by a variety of filtering approaches. We treat data filtered with alternative procedures as contaminated proxy of the relevant model-based quantities and estimate structural and nonstructural...

This paper studies how the HP-Filter should be adjusted, when changing the frequency of observations. It complements the results of Baxter and King (1999) with an analytical analysis, demonstrating that the filter parameter should be adjusted by multiplying it with the fourth power of the...

We extend the frequency-specific Granger-causality test of Breitung et al. (2006) to a more general null hypothesis that allows causality testing at unknown frequencies within a prespecified range of frequencies. This setup corresponds better to empirical situations encountered in applied...

Consider the problem of smoothing a time series for extracting its low frequency characteristics, collectively called its trend. This paper proposes a competitive, to existing methods, solution in choosing the optimal degree of smoothing based on the distribution of the residuals from the smooth...

This paper investigates how the ordering of variables affects properties of the time-varying covariance matrix in the Cholesky multivariate stochastic volatility model. It establishes that systematically different dynamic restrictions are imposed when the ratio of volatilities is time-varying....

This article suggests and compares the properties of some nonlinear Markov-switching filters. Two of them are sigma point filters: the Markov switching central difference Kalman filter (MSCDKF) and MSCDKFA. Two of them are Gaussian assumed filters: Markov switching quadratic Kalman filter...

This paper develops a novel method to correct small-sample bias in autoregressive roots of AR(p) models. We evaluate median-bias properties and variability of the bias-adjusted parameters by examining the accuracy of bias-adjusted impulse responses. Our simulation results show that bias...

This paper considers approximating the nite sample null-distribution of a test statistic as its asymptotic distribution under a local alternative. We focus on the Likelihood Ratio test for the rank of cointegration and use nonlinearities that represent some nite sample distributional features....