Volume 22, Issue 4

In this paper, we develop a new class of parametric nonlinear time series models by combining two important classes of models, namely smooth transition models and hidden Markov regime-switching models. The class of models is general and flexible enough to incorporate two types of switching behavior: smooth state transitions and abrupt changes in hidden states. The estimation of the hidden states and model parameters is performed by applying filtering theory and a filter-based expectation-maximization (EM) algorithm. Applications of the model are illustrated using simulated data and real financial data. Other potential applications are mentioned.

This paper introduces the rescaled representation of VAR models (R-VARs) and demonstrates its application in forecasting mixed-frequency macroeconomic data. We develop the model, illustrate how to implement it, and derive the asymptotic properties of the estimates. We show that R-VARs provide reliable estimates of the prediction error bands while maintaining the precision of the point forecasts. We illustrate these features by comparing it to a mixed-frequency Bayesian VAR model, the leading alternative in the existing literature.

Because Hodrick–Prescott (HP) filtering and ℓ 1 trend filtering are expressed as penalized least squares problem, both of them require the specification of their tuning parameter. For HP filtering, we have accumulated knowledge for selecting the value of its tuning parameter. However, we do not have similar knowledge for ℓ 1 trend filtering. This paper presents a new method for specifying the tuning parameter of ℓ 1 trend filtering so that the sum of squared residuals of HP filtering and that of ℓ 1 trend filtering may be equivalent.

In this paper, we propose and study an effective Bayesian subset selection method for two-threshold variable autoregressive (TTV-AR) models. The usual complexity of model selection is increased by capturing the uncertainty of the two unknown threshold levels and the two unknown delay lags. By using Markov chain Monte Carlo (MCMC) techniques with driven by a stochastic search, we can identify the best subset model from a large number of possible choices. Simulation experiments show that the proposed method works very well. As applied to the application to the Hang Seng index, we successfully distinguish the best subset TTV-AR model.

Based on a panel wavelet efficiency analysis, we conclude that there is a systematic pattern of positive asymmetric price transmission inefficiencies in the interest rates of the largest Swedish mortgage lenders. Thus, there seems to be a higher propensity for mortgage lenders to swiftly increase their customers’ mortgage interest rates subsequent to an increase in its borrowing costs, than to decrease their customers’ mortgage rates subsequent to a corresponding decrease in the cost of borrowing. A unique contribution is our proposed wavelet method which enables a robust detection of positive asymmetric price transmission effects at various time-frequency scales, while simultaneously controlling for non-stationary trends, autocorrelation, and structural breaks. Since traditional time-series analysis methods essentially implies that several wavelet time scales are aggregated into one single time series, the blunt traditional error correction analysis totally failed to discover APT effects for this data set. In summary, using the wavelet method we show that even though the customers in the end finally will benefit from decreases in the mortgage lenders’ financing costs, the lenders wait disproportionally long before the customers’ mortgage rates are decreased.