Using simulation and analysis we show that agent-based auction-cleared automated markets can be stabilized using only completely myopic agents (without value traders), if these naı̈ve agents are provided with a price signal that reflects order book information. This demonstrates that information in the order book is extremely valuable, that prediction can be replaced by better instantaneous info...

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...

We propose new forecast combination schemes for predicting turning points of business cycles. The combination schemes deal with the forecasting performance of a given set of models and possibly providing better turning point predictions. We consider turning point predictions generated by autoregressive (AR) and Markov-Switching AR models, which are commonly used for business cycle analysis. In ...

The optimal weights on indicators in models with partial information about the state of the economy and forward-looking variables are derived and interpreted, both for equilibria under discretion and under commitment. An example of optimal monetary policy with a partially observable potential output and a forward-looking indicator is examined. The optimal response to the optimal estimate of pot...

This paper applies the hybrid dynamic general-equilibrium, vector autoregressive (DGE-VAR) model developed by Ireland (1999) to Canadian time series. It presents the first Canadian evidence that a hybrid DGE-VAR model may have better out-of-sample forecasting accuracy than a simple, structure-free VAR model. The evidence suggests that estimated DGE models have the potential to add good forecast...

Abstract The aim of the paper is to compare forecasting performance a class statedependent autoregressive (SDAR) models for univariate time series with two alternative families nonlinear models, such as SETAR and GARCH models. study conducted on US GDP growth rate using quarterly data. Two methods forecast comparison are employed. first method consists in evaluation average by measures root mea...

This paper develops a Bayesian quantile regression model with time-varying parameters (TVPs) for forecasting inflation risks. The proposed parametric methodology bridges the empirically established benefits of TVP regressions ability to flexibly whole distribution inflation. In order make our approach accessible and relevant forecasting, we derive an efficient Gibbs sampler by transforming stat...