This article presents a robust augmented Kalman filter that extends the data-cleaning filter (Masreliez and Martin, 1977) to the general state space model featuring nonstationary and regression effects. The robust filter shrinks the observations towards their one-step-ahead prediction based on...

This paper examines the relevance of incorporating seasonality in agricultural supply models. Former studies have eliminated the problem of seasonality by using seasonally adjusted data. Recent developments in cointegration techniques allow the comprehensive modelling of error-correcting...

This paper shows through a Monte Carlo analysis the effect of neglecting seasonal deterministics on the seasonal KPSS test. We found that the test is most of the time heavily oversized and not convergent in this case. In addition, Bartlett-type non-parametric correction of error variances did...

The problem of optimal decision between unit roots, trend stationarity and trend stationarity with structural breaks is considered. Each of three classes is represented by a hierarchically random process whose parameters are distributed in a non-informative way based on a simple rule. Given a...

This paper studies the asymptotic behaviour of the HEGY tests for quarterly data, for nonseasonal and seasonal autoregressive unit roots, when the time series being analysed is trend and deterministic seasonal stationary but exhibits a change in the seasonal pattern. Our results show that,...

This paper compares the performance of a four methods of seasonal adjustment for monthly monetary aggregates. The methods compared are 1. GLAS - the method currently employed by the Bank. 2. STL - a recently developed flexible non-parametric adjustment method. 3. X-11 ARIMA (a slightly modified...

The popular 'airline' model for a seasonal time series assumes that a variable needs double differencing, i.e. first and seasonal (or annual) differencing. The resultant time series can usaually be described by a low order moving average model with estimated roots close to the unit circle. This...

This chapter reviews the principal methods used by researchers when forecasting seasonal time series. In addition, the often overlooked implications of forecasting and feedback for seasonal adjustment are discussed. After an introduction in Section 1, Section 2 examines traditional univariate...

Seasonal patterns in economic time series are generally examined from a univariate point of view. Using extensions of the unit root literature, important classes of seasonal processes are deterministic, stationary stochastic or mean reverting, and unit root stochastic. Time series tests have...