This paper examines the distributions of (zero frequency) unit root test statistics for I(1) processes in the presence of noninvertible moving average components. The analysis initially considers a noninvertible MA(1), for which the asymptotic distribution of the ADF test statistic under the...

Government statistical agencies are required to seasonally adjust non-stationary time series resulting from an aggregate of a number of cross-sectional time series. Traditionally, this has been achieved using the X-11 or X12-ARIMA process by us- ing either direct or indirect seasonal adjustment....

For benchmarking monthly and quarterly series to annual series and to the Economic Census every five years, the U.S. Census Bureau uses an iterative, nonlinear method known as the Causey-Trager method. However, the Census Bureau's X−12−ARIMA seasonal adjustment program uses a modified Denton...

Benchmarking deals with the problem of combining a series of high-frequency data (e.g., monthly) with a series of low frequency data (e.g., quarterly) into a consistent time series. When discrepancies arise between the two series the latter is usually assumed to provide more reliable...

The paper discusses the main issues arising in the construction of quarterly national accounts estimates, adjusted for seasonality and calendar effects, obtained by disaggregating the original annual actual measurements using related monthly indicators. It proposes and implements an approach...

Usually, seasonal adjustment is based on time series models which decompose an unadjusted series into the sum or the product of four unobservable components (trend-cycle, seasonal, working-day and irregular components). In the case of clearly weather-dependent output in the west German...