this article is a comparative study of estimation power of artificial neural networks and autoregressive time series models in inflation forecasting. using 37 years iran’s inflation data, neural networks performs better on average for short horizons than autoregressive models. this study shows usefulness of early stopping technique in learning stage of neural networks for estimating time series...

The construction of score-driven filters for nonlinear time series models is described, and they are shown to apply over a wide range disciplines. Their theoretical practical advantages other methods highlighted. Topics covered include robust modeling, conditional heteroscedasticity, count data, dynamic correlation association, censoring, circular switching regimes.

Abstract We adapt the classical definition of locally stationary processes in discrete time (see e.g. Dahlhaus, ‘Locally processes’, Time Series Analysis: Methods and Applications (2012)) to continuous-time setting obtain equivalent representations frequency domains. From this, a unique time-varying spectral density is derived using Wigner–Ville spectrum. As an example, we investigate Lévy-driv...

Modeling and analysis of future prices has been hot topic for economic analysts in recent years. Traditionally, the complex movements in the prices are usually taken as random or stochastic process. However, they may be produced by a deterministic nonlinear process. Accuracy and efficiency of economic models in the short period forecasting is strategic and crucial for business world. Nonlinear ...