Doğrusal olmayan yapıdaki iktisadi değişkenler uzun yıllar doğrusal modeller araçları ile analiz edilmiş bu nedenle de gerçek hayatı açıklamada yetersiz kalmıştır. Son dönemde yapılan çalışmalar sonucunda doğrusal olmayan zaman serileri analizlerinin özellikle makro...

Applying a probabilistic causal approach, we define a class of time series causal models (TSCM) based on stationary Bayesian networks. A TSCM can be seen as a structural VAR identified by the causal relations among the variables. We classify TSCMs into observationally equivalent classes by...

This work explores some distributional properties of aggregate output growth-rate time series. We show that, in the majority of OECD countries, output growth-rate distributions are well-approximated by symmetric exponential-power densities with tails much fatter than those of a Gaussian. Fat...

Several economic data series of Liechtenstein are backwardly estimated in order to achieve consistent historic time series. The generated series consist for instance of the national income for the years 1954 to 1992 (by regressive inter- and retropolation with indicators) and 1993 to 1997 (by...

This report examines whether Google search queries can be used to predict the present and the near future house prices in Finland. Compared to a simple benchmark model, Google searches improve the prediction of the present house price index by 7.5 % measured by mean absolute error. In addition,...

We review and construct consistent in-sample specification and out-of-sample model selection tests on conditional distributions and predictive densities associated with continuous multifactor (possibly with jumps) and (non)linear discrete models of the short term interest rate. The results of...

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...

This paper studies simulation-based optimization with multiple outputs. It assumes that the simulation model has one random objective function and must satisfy given constraints on the other random outputs. It presents a statistical procedure for testing whether a specific input combination...

Quantile and least-absolute deviations (LAD) methods are popular robust statistical methods but have not generally been applied to state filtering and sequential parameter learning. This paper introduces robust state space models whose error structure coincides with quantile estimation...

This paper describes how the frequency domain analysis provides an alternative approach to time domain analysis for a given time series. Spectral and periodogram analysis for a given time series are performed to detect trends and seasonalities in the data. A cross-spectral analysis is used to...