A SETAR model for Canadian GDP: non-linearities and forecast comparisons

A SETAR Model for Canadian GDP: Non-linearities and Forecast Comparisons

In this paper we investigate the forecasting performance of the non-linear time series SETAR model by using Canadian GDP data from 1965 to 2000. Besides the with-insample fit, the forecasting performance of a standard linear ARIMA model for the same sample has also been generated for comparative purposes. Two forecasting methods, 1step-ahead and multi-step-ahead forecasting are compared for eac...

Reality Checks and Nested Forecast Model Comparisons

This paper develops a novel and e¤ective bootstrap method for simulating asymptotic critical values for tests of equal forecast accuracy and encompassing among many nested models. The bootstrap, which combines elements of …xed regressor and wild bootstrap methods, is simple to use. We …rst derive the asymptotic distributions of tests of equal forecast accuracy and encompassing applied to foreca...

Providing A Model for Management Earnings Forecast Bias

Despite The Important Role That Management Profit Forecasting Plays In The Decision Making Of Capital Market Actors, These Predictions Appear To Be Biased. In The Attempt To Measure The Bias Of Predicting Profit Management, Numerous One- Dimensional Measurement Tools Have Been Proposed In The Accounting And Finance Literature. Despite These Efforts, No Comprehensive Composite Index Has Been Dev...

A Comparison Between Time Series, Exponential Smoothing, and Neural Network Methods To Forecast GDP of Iran

On SETAR Non-linearity and Forecasting

We consider the usefulness of the two-regime SETAR model for out-of-sample forecasting, and compare it with a linear AR model. A range of newly-developed forecast evaluation techniques are employed. Our simulation results show that time-series data need to exhibit a substantial degree of non-linearity before the SETAR model is favoured on some of these criteria. We find only weak evidence that ...