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Forecasting turning points in international output growth rates using Bayesian exponentially weighted autoregression, time-varying parameter, and pooling techniques
3 references·16 citing·51 second hop
Forecasting turning points in international output growth rates using Bayesian exponentially weighted autoregression, time-varying parameter, and pooling techniques (1991)Forecasting turning points in internation…Recent Work on Business Cycles in Historical Perspective: Review of Theories and Evidence (1984)Recent Work on Business Cycle…Business cycles, inflation, and forecasting (1983)Business cycles, inflation, a…Sequential Signals of Recession and Recovery (1982)Sequential Signals of Recessi…Forecasting turning points in tourism growth (2018)Forecasting turning points in…Turning Point Mechanisms in a Dualistic Process Model of Institutional Emergence: The Case of the Diesel Particulate Filter in Germany (2013)Turning Point Mechanisms in a…Common Correlated Effects Estimation of Heterogenous Dynamic Panel Data Models with Weakly Exogenous Regressors (2013)Common Correlated Effects Est…Changes in predictive ability with mixed frequency data (2013)Changes in predictive ability…Evaluating probability forecasts for GDP declines using alternative methodologies (2012)Evaluating probability foreca…Evaluating the forecasting performance of econometric models of air passenger traffic flows using multiple error measures (2009)Evaluating the forecasting pe…A SYSTEM FOR DATING AND DETECTING TURNING POINTS IN THE EURO AREA (2008)A SYSTEM FOR DATING AND DETEC…Can the random walk model be beaten in out-of-sample density forecasts? Evidence from intraday foreign exchange rates (2007)Can the random walk model be …Chapter 16 Leading Indicators (2006)Chapter 16 Leading IndicatorsChapter 1 Bayesian Forecasting (2006)Chapter 1 Bayesian ForecastingOPTIMAL FORECAST COMBINATION UNDER REGIME SWITCHING* (2005)OPTIMAL FORECAST COMBINATION …Dynamic Forecasts of Qualitative Variables (2004)Dynamic Forecasts of Qualitat…Forecasting with nonstationary dynamic factor models (2003)Forecasting with nonstationar…A note on aggregation, disaggregation and forecasting performance (2000)A note on aggregation, disagg…Theory and History Behind Business Cycles: Are the 1990s the Onset of a Golden Age? 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this work references cited by second hop

References

  • 1984Recent Work on Business Cycles in Historical Perspective: Review of Theories and Evidence372 cited
  • 1983Business cycles, inflation, and forecasting199 cited
  • 1982Sequential Signals of Recession and Recovery90 cited

Cited by

  • 2000A note on aggregation, disaggregation and forecasting performance134 cited
  • 2006Chapter 1 Bayesian Forecasting122 cited
  • 2004Dynamic Forecasts of Qualitative Variables120 cited
  • 1994Predicting cyclical turning points with leading index in a markov switching model112 cited
  • 2003Forecasting with nonstationary dynamic factor models94 cited
  • 2009Evaluating the forecasting performance of econometric models of air passenger traffic flows using multiple error measures91 cited
  • 2005OPTIMAL FORECAST COMBINATION UNDER REGIME SWITCHING*91 cited
  • 2013Common Correlated Effects Estimation of Heterogenous Dynamic Panel Data Models with Weakly Exogenous Regressors71 cited
  • 2007Can the random walk model be beaten in out-of-sample density forecasts? Evidence from intraday foreign exchange rates70 cited
  • 2012Evaluating probability forecasts for GDP declines using alternative methodologies63 cited
  • 1999Theory and History Behind Business Cycles: Are the 1990s the Onset of a Golden Age?62 cited

Only edges whose both ends are imported exist here. Widen the year window, or show the whole map. Back to the work

  • 2013Changes in predictive ability with mixed frequency data61 cited
  • 2018Forecasting turning points in tourism growth61 cited
  • 2008A SYSTEM FOR DATING AND DETECTING TURNING POINTS IN THE EURO AREA56 cited
  • 2013Turning Point Mechanisms in a Dualistic Process Model of Institutional Emergence: The Case of the Diesel Particulate Filter in Germany55 cited
  • 2006Chapter 16 Leading Indicators46 cited