This paper summarizes and assesses several of the most popular methods to seasonally adjust weekly data. The industry standard approach, known as X-13ARIMA-SEATS, is suitable only for monthly or quarterly data. Given the increased availability and promise of non-traditional data at higher...

We propose a set of algorithms for testing the ergodicity of empirical time series, without reliance on a specific parametric framework. It is shown that the resulting test asymptotically obtains the correct size for stationary and nonstationary processes, and maximal power against non-ergodic...

Semiparametric spectral methods seem particularly appropriate for the analysis of long financial time series, providing they are robust to a variety of forms of conditional heteroscedasticity, which is generally recognized as a dominant feature of financial returns. This paper analyses the...

This paper presents a procedure for studying industrial performance and related issues such as changes in the wage structure. This procedure combines cluster analysis and discriminant analysis as a package, and applies this package to time series data. This enables us to organize industrial data...

Real GDP and oil prices are decomposed into common stochastic trend and cycle processes using structural time series models. Potential real GDP is represented by the level of the trend component of real GDP. The potential rate of growth of real GDP is represented by the stochastic drift element...

Using structural time series models, Cuevas estimates common stochastic trends of real GDP and imports in Venezuela from 1974-2000. The real imports trend drifts upward at almost twice the rate of growth of GDP. This highlights the powerful structural tendency toward increasing imports in...

Money demand in Venezuela is modeled using structural time series and error correction approaches, for the period 1993.1 to 2001.4. The preferred model features seasonal cointegration and was estimated following a structural time series approach. There are similarities in the long-run behavior...

This paper introduces a general class of combined neural network-GARCH models suitable to financial time series analysis. We put special emphasis on designing a full model-building cycle for this class of models that includes all stages of econometric modelling (specification, estimation and...