This is the first paper in a series of two that synthesizes, compares, and extends methods for causal inference with longitudinal panel data in a structural equation modeling (SEM) framework. Starting with a cross-lagged approach, this paper builds a general cross-lagged panel model (GCLM) with...

This chapter extends the univariate forecasting method proposed by Wang, Luc, and Hsiao (2013) to forecast the multivariate long memory model subject to structural breaks. The approach does not need to estimate the parameters of this multivariate system nor need to detect the structural breaks....

The research used a long memory or Autoregressive Fractionally Integrated Moving Average model to study and forecast crude oil prices using weekly West Texas Intermediate and Brent series for the period 15/5/1987 to 20/12/2013. Fractional differencing Methods such as Local Whittle Estimator and...

Purpose – The purpose of this study is to establish a seasonal autoregressive integrated moving average model able to capture and explain the patterns and the determinants of German tourism demand in Croatia.Design – The present study is based on the Box-Jenkins approach in building a...

In this paper we entertain a method of finding the most robust moving average weighting scheme to use for the purpose of timing the market. Robustness of a weighting scheme is defined its ability to generate sustainable performance under all possible market scenarios regardless of the size of...

Forecasting oil prices has been of great interests for macroeconomists in the recent years. Our article contributes to this strand of the literature by using a dynamic model averaging (DMA) method to improve forecasting accuracy of real oil prices. The advantage of DMA is that the method...