An n-variable structural vector auto-regression (SVAR) can be identified (up to shock order) from the evolution of the residual covariance across time if the structural shocks exhibit heteroskedasticity (Rigobon (2003), Sentana and Fiorentini (2001)). However, the path of residual covariances is...

We propose a new non-recursive identification scheme for uncertainty shocks, which exploits breaks in the unconditional volatility of macroeconomic variables. Such identification approach allows us to simultaneously address two major questions in the empirical literature on uncertainty: (i) Does...

In this paper, an Unobserved Components Model is employed to decompose German real GDP into the trend, cycle and seasonal components and the working day effect. The most important findings are: 1) The growth rate of potential output declined from 4.2 per cent in the sixties to 1.4 per cent at...

The house price in Hong Kong is well-known to be "unaffordable." This paper argues that the commonly used house price-to-income ratio may be misleading in an economy with almost half of the population living in either public rental housing or subsidized ownership. Moreover, we re-focus on the...

In this paper an Unobserved Components Model is employed to decompose U.S. real GDP into trend and cycle components. The main findings are that there exist three cycles with a period of about two, five and 13 years, respectively, and that the long-run development during the last 50 years can be...

We propose a modified version of the augmented Kalman filter (AKF) to evaluate the likelihood of linear and time-invariant state-space models (SSMs). Unlike the regular AKF, this augmented steady-state Kalman filter (ASKF), as we call it, is based on a steady-state Kalman filter (SKF). We show...

We introduce a new estimation framework which extends the Generalized Method of Moments (GMM) to settings where a subset of the parameters vary over time with unknown dynamics. To filter out the dynamic path of the time-varying parameter, we approximate the dynamics by an autoregressive process...

Markov models introduce persistence in the mixture distribution. In time series analysis, the mixture components relate to different persistent states characterizing the state-specific time series process. Model specification is discussed in a general form. Emphasis is put on the functional form...

Path forecasts, defined as sequences of individual forecasts, generated by vector autoregressions are widely used in applied work. It has been recognized that a profound econometric analysis often requires, besides the path forecast, a joint prediction region that contains the whole future path...

Chen and Zadrozny (1998) developed the linear extended Yule-Walker (XYW) method for determining the parameters of a vector autoregressive (VAR) model with available covariances of mixed-frequency observations on the variables of the model. If the parameters are determined uniquely for available...