Business Cycle Index Forecasting of Grey Model Optimized by Genetic Algorithm

2011 ◽  
Author(s):  
Huayun Yu ◽  
Dabin Zhang
Keyword(s):  
Genetic Algorithm ◽  
Business Cycle ◽  
Grey Model ◽  
Cycle Index ◽  
Business Cycle Index

BUSINESS-CYCLE FILTERING OF MACROECONOMIC DATA VIA A LATENT BUSINESS-CYCLE INDEX

2006 ◽  
Vol 10 (5) ◽  
pp. 573-594 ◽  
Author(s):  
MICHAEL DUEKER ◽  
CHARLES R. NELSON
Keyword(s):  
Business Cycle ◽  
Interest Rates ◽  
Data Series ◽  
Cycle Index ◽  
Federal Funds ◽  
Price Inflation ◽  
Federal Funds Rate ◽  
Vector Autoregressive ◽  
The Business Cycle ◽  
Business Cycle Index

We use Markov chain Monte Carlo methods to augment, via a novel multimove sampling scheme, a vector autoregressive (VAR) system with a latent business-cycle index that is negative during recessions and positive during expansions. We then sample counterfactual values of the macroeconomic variables in the case where the latent business-cycle index is held constant. These counterfactual values represent posterior beliefs about how the economy would have evolved absent business-cycle fluctuations. One advantage is that a VAR framework provides model-consistent counterfactual values in the same way that VARs provide model-consistent forecasts, so data series are not filtered in isolation from each other. We apply these methods to estimate the business-cycle components of industrial production, consumer price inflation, the federal funds rate, and the spread between long-term and short-term interest rates. These decompositions provide an explicitly counterfactual approach to isolating the effects of the business cycle and to deriving empirical business-cycle facts.

A Genetic Algorithm-Based Multivariate Grey Model in Housing Demand Forecast in Turkey

2020 ◽  
pp. 476-500
Author(s):  
Miraç Eren ◽  
Ali Kemal Çelik ◽  
İbrahim Huseyni
Keyword(s):  
Genetic Algorithm ◽  
Growth And Development ◽  
Consumer Price Index ◽  
Housing Demand ◽  
Grey Model ◽  
Demand Forecast ◽  
Factors Affecting ◽  
Urbanization Rate ◽  
Housing Investments ◽  
Demand Forecasts

Housing sector is commonly considered as a very strong economic industry in terms of both its contribution to creating employment and its impact on other associated sectors. By means of its featured characteristics, the sector also plays an important role on economic growth and development of emerging countries. In this respect, any evidence that determines factors affecting housing investments and future demand behavior may be remarkably valuable for monitoring possible future excess supply and deficits. This chapter attempts to determine factors affecting housing demand in Turkey during a sample period of 2003-2011 using a genetic algorithm-based multivariate grey model. Housing demand forecasts are also employed until the year 2020. Results reveal that several factors including M2 money supply, consumer price index and urbanization rate have an impact on housing demand. According to housing demand forecasts, a significant housing demand increase is expected in Turkey.

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