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A new index of financial conditions
Koop, Gary; Korobilis, Dimitris - In: European Economic Review 71 (2014) C, pp. 101-116
We use factor augmented vector autoregressive models with time-varying coefficients and stochastic volatility to construct a financial conditions index that can accurately track expectations about growth in key US macroeconomic variables. Time-variation in the models׳ parameters allows for the...
Persistent link: https://www.econbiz.de/10011048625
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Forecasting inflation in Mongolia : a dynamic model averaging approach
Doojav, Gan-Ochir; Luvsannyam, Davaajargal - In: Journal of time series econometrics 15 (2023) 1, pp. 27-48
Persistent link: https://www.econbiz.de/10014288359
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Forecasting Interest Rates in India
Dua, Pami; Raje, Nishita; Sahoo, Satyananda - In: Margin: The Journal of Applied Economic Research 2 (2008) 1, pp. 1-41
forecasting performance of Bayesian VAR models is satisfactory for most interest rates and their superiority in performance is …
Persistent link: https://www.econbiz.de/10011136571
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Determining the MSE-optimal cross section to forecast
Arbués, Ignacio - In: Journal of Econometrics 175 (2013) 2, pp. 61-70
In this paper, we address the question of which subset of time series should be selected among a given set in order to forecast another series. We evaluate the quality of the forecasts in terms of Mean Squared Error. We propose a family of criteria to estimate the optimal subset. Consistency...
Persistent link: https://www.econbiz.de/10010666081
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Employing Machine Learning Algorithms to build Trading Strategies with higher than Risk-Free Returns
Uzunlu, Baris Yalin; Hussain, Syed Muzammil - In: International Econometric Review (IER) 12 (2020) 2, pp. 112-138
This research aims at exploring whether simple trading strategies developed using state-ofthe-art Machine Learning (ML) algorithms can guarantee more than the risk-free rate of return or not. For this purpose, the direction of S&P 500 Index returns on every 6th day (SPYRETDIR6) and magnitude of...
Persistent link: https://www.econbiz.de/10012610982
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Forecasting Spanish unemployment with Google Trends and dimension reduction techniques
Mulero, Rodrigo; Garcia-Hiernaux, Alfredo - In: SERIEs - Journal of the Spanish Economic Association 12 (2021) 3, pp. 329-349
variables or which ones to use. The results of a recursive forecasting exercise reveal a statistically significant increase in …
Persistent link: https://www.econbiz.de/10014496096
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Employing machine learning algorithms to build trading strategies with higher than risk-free returns
Uzunlu, Baris Yalin; Hussain, Syed Muzammil - In: International econometric review 12 (2020) 2, pp. 112-138
This research aims at exploring whether simple trading strategies developed using state-ofthe-art Machine Learning (ML) algorithms can guarantee more than the risk-free rate of return or not. For this purpose, the direction of S&P 500 Index returns on every 6th day (SPYRETDIR6) and magnitude of...
Persistent link: https://www.econbiz.de/10012432999
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Forecasting Spanish unemployment with Google Trends and dimension reduction techniques
Mulero, Rodrigo; Garcia-Hiernaux, Alfredo - In: SERIEs : Journal of the Spanish Economic Association 12 (2021) 3, pp. 329-349
variables or which ones to use. The results of a recursive forecasting exercise reveal a statistically significant increase in …
Persistent link: https://www.econbiz.de/10012612549
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Search for Predictors of Inflation Using VAR and BVAR: The Case of Czech Republic
Stráský, Josef; Baxa, Jaromír - In: Bulletin of the Czech Econometric Society 21 (2014)
Forecasting inflation is generally considered a challenging task as forecasters face fundamental uncertainty about the … proper selection of variables driving inflation dynamics. In this paper, we investigate the forecasting performance of … algorithm to evaluate their forecasting performance based on various criteria such as the mean square error, the mean absolute …
Persistent link: https://www.econbiz.de/10011098942
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Bayesian forecasting with highly correlated predictors
Korobilis, Dimitris - In: Economics Letters 118 (2013) 1, pp. 148-150
This paper considers Bayesian variable selection in regressions with a large number of possibly highly correlated macroeconomic predictors. I show that acknowledging the correlation structure in the predictors can improve forecasts over existing popular Bayesian variable selection algorithms.
Persistent link: https://www.econbiz.de/10010603109