We take a model selection approach to the question of whether a class of adaptive prediction models ("artificial neural networks") are useful for predicting future values of 9 macroeconomic variables. We use a variety of out-of-sample forecast-based model selection criteria including forecast error measures and forecast direction accuracy. Ex ante or real-time forecasting results based on rolli...

This paper focusses on survey expectations and discusses their uses for testing and modeling of expectations. Alternative models of expectations formation are reviewed and the importance of allowing for heterogeneity of expectations is emphasized. A weak form of the rational expectations hypothesis which focusses on average expectations rather than individual expectations is advanced. Other mod...

As extensions to the Black-Scholes model with constant volatility, option pricing models with time-varying volatility have been suggested within the framework of generalized autoregressive conditional heteroskedasticity (GARCH). However, application of the GARCH option pricing model has been hampered by the lack of simulation techniques able to incorporate early exercise features. In the presen...

in this paper using catastrophe theory, we investigate non-smooth changes in tehran stock exchange. stock market crashes bring not only panic among investors, but also in deeper market lead to recession and decrease in consumer's confidence. as catastrophe theory is strong tool in explaining nonlinear phenomena, by applying stochastic cusp catastrophe model we examine sudden change in tehr...

exchange rate prediction, as one of the main variables in macroeconomics, has been one of the aims of the economic research for a long time. for modeling and predicting exchange rate we apply stochastic differential equation, specifically we use geometric brownian motion (gbm) and jump-diffusion process (mjdp) attributed to merton. we show that the result of simulation based on gbm and mjdp out...

Evaluation of forecast optimality in economics and finance has almost exclusively been conducted under the assumption of mean squared error loss. Under this loss function optimal forecasts should be unbiased and forecast errors serially uncorrelated at the single period horizon with increasing variance as the forecast horizon grows. Using analytical results we show that standard properties of o...

We consider a set of linear regression models that differ in their choice of regressors, and derive a method for inference that controls for the set of models under investigation. The method is based around an estimate of the distribution for a class of statistics, which can depend on two or more models. An example is the largest R2 over a set of regression models. The distribution will typical...

Factor models are widely used in summarizing large datasets with few underlying latent factors and in building time series forecasting models for economic variables. In these models, the reduction of the predictors and the modeling and forecasting of the response y are carried out in two separate and independent phases. We introduce a potentially more attractive alternative, Sufficient Dimensio...