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Artificial Prediction Markets, Forecast Combinations and Classical Time Series
Lipán, Marek ; Baruník, Jozef (advisor) ; Kukačka, Jiří (referee)
Economic agents often face situations, where there are multiple competing fore- casts available. Despite five decades of research on forecast combinations, most of the methods introduced so far fail to outperform the equal weights forecast combination in empirical applications. In this study, we gather a wide spectrum of forecast combination methods and reexamine these findings in two different classical economic times series forecasting applications. These include out-of- sample combining forecasts from the ECB Survey of Professional Forecasters and forecasts of the realized volatility of the U.S. Treasury futures log-returns. We asses the performance of artificial predictions markets, a class of machine learning methods, which has not yet been applied to the problem of combin- ing economic times series forecasts. Furthermore, we propose a new simple method called Market for Kernels, which is designed specifically for combining time series forecasts. We found that equal weights can be significantly out- performed by several forecast combinations, including Bates-Granger methods and artificial prediction markets in the ECB Survey of Professional Forecasters application and by almost all examined forecast combinations in the financial application. We also found that the Market for Kernels forecast...
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Spatial approaches to hedonic modelling of housing market: Prague case
Lipán, Marek ; Křehlík, Tomáš (advisor) ; Troch, Tomáš (referee)
Having at hands instruments capable of effective housing appraisal can be essential not only for the real property evaluator in a bank, policy maker or real estate agent, but also for single individual seeking for an objective way to assess the tenure choice decisions. The housing market data are of a spatial nature. We address the spatial issues by implementing spatial modelling techniques into a hedonic price model. The main focus of the thesis is put on building a kriging model, which shows to be a powerful tool in explaining and predicting the prices of housing in the Prague at market. The kriging model comes out the best from the comparison of performance with the traditional spaceless hedonic pricing model as well as the common econometric spatial models. The usefulness of our kriging model is demonstrated in a possible application as the extension of the net present value model of the optimal tenure choice for a prospective first home owner. In a simplified economic scenario we found that the optimality of the tenure choice depends on the inflation, expected holding period as well as the precise location of the flat in the Prague.
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Artificial Prediction Markets, Forecast Combinations and Classical Time Series
Lipán, Marek ; Baruník, Jozef (advisor) ; Kukačka, Jiří (referee)
Economic agents often face situations, where there are multiple competing fore- casts available. Despite five decades of research on forecast combinations, most of the methods introduced so far fail to outperform the equal weights forecast combination in empirical applications. In this study, we gather a wide spectrum of forecast combination methods and reexamine these findings in two different classical economic times series forecasting applications. These include out-of- sample combining forecasts from the ECB Survey of Professional Forecasters and forecasts of the realized volatility of the U.S. Treasury futures log-returns. We asses the performance of artificial predictions markets, a class of machine learning methods, which has not yet been applied to the problem of combin- ing economic times series forecasts. Furthermore, we propose a new simple method called Market for Kernels, which is designed specifically for combining time series forecasts. We found that equal weights can be significantly out- performed by several forecast combinations, including Bates-Granger methods and artificial prediction markets in the ECB Survey of Professional Forecasters application and by almost all examined forecast combinations in the financial application. We also found that the Market for Kernels forecast...
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Spatial approaches to hedonic modelling of housing market: Prague case
Lipán, Marek ; Křehlík, Tomáš (advisor) ; Troch, Tomáš (referee)
Having at hands instruments capable of effective housing appraisal can be essential not only for the real property evaluator in a bank, policy maker or real estate agent, but also for single individual seeking for an objective way to assess the tenure choice decisions. The housing market data are of a spatial nature. We address the spatial issues by implementing spatial modelling techniques into a hedonic price model. The main focus of the thesis is put on building a kriging model, which shows to be a powerful tool in explaining and predicting the prices of housing in the Prague at market. The kriging model comes out the best from the comparison of performance with the traditional spaceless hedonic pricing model as well as the common econometric spatial models. The usefulness of our kriging model is demonstrated in a possible application as the extension of the net present value model of the optimal tenure choice for a prospective first home owner. In a simplified economic scenario we found that the optimality of the tenure choice depends on the inflation, expected holding period as well as the precise location of the flat in the Prague.
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