Model Averaging in Markov-Switching Models: Predicting National Recessions with Regional Data

Predicting Recessions with Leading Indicators: Model Averaging and Selection Over the Business Cycle

Four model selection methods are applied to the problem of predicting business cycle turning points: equally-weighted forecasts, Bayesian model averaged forecasts, and two models produced by the machine learning algorithm boosting. The model selection algorithms condition on different economic indicators at different forecast horizons. Models produced by BMA and boosting outperform equally-weig...

Predicting waste generation using Bayesian model averaging

A prognosis model has been developed for solid waste generation from households in Hoi An City, a famous tourist city in Viet Nam. Waste sampling, followed by a questionnaire survey, was carried out to gather data. The Bayesian model average method was used to identify factors significantly associated with waste generation. Multivariate linear regression analysis was then applied to evaluate th...

Predicting Bankruptcy of Companies using Data Mining Models and Comparing the Results with Z Altman Model

One of the issues helping make investment decisions is appropriate tools and models to evaluate financial situation 0f the organization.  By means of these tools, investors can analyze financial situation of the organization and identify financial distress or an ideal condition, they become aware of making decisions to invest in appropriate conditions.  The main objective of this study is to ev...

Fads Models with Markov Switching Hetroskedasticity: decomposing Tehran Stock Exchange return into Permanent and Transitory Components

Stochastic behavior of stock returns is very important for investors and policy makers in the stock market. In this paper, the stochastic behavior of the return index of Tehran Stock Exchange (TEDPIX) is examined using unobserved component Markov switching model (UC-MS) for the 3/27/2010 until 8/3/2015 period. In this model, stock returns are decomposed into two components; a permanent componen...

Markov-Switching Models