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Model-based forecast adjustment : with an illustration to inflation
Franses, Philip Hans - 2018 - This version: March 2018
Persistent link: https://www.econbiz.de/10011823289
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Some tools for robustifying econometric analyses
Hoornweg, Victor; Franses, Philip Hans - 2013
Persistent link: https://www.econbiz.de/10010354387
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How informative are the unpredictable components of earnings forecasts?
Bruijn, Bert de; Franses, Philip Hans - 2015
earnings forecasters, we see that small adjustments to the model forecasts lead to more forecast accuracy. Based on past track …
Persistent link: https://www.econbiz.de/10010490078
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How Informative are the Unpredictable Components of Earnings Forecasts?
de Bruijn, Bert - 2015
earnings forecasters, we see that small adjustments to the model forecasts lead to more forecast accuracy. Based on past track …
Persistent link: https://www.econbiz.de/10013026946
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How informative are earnings forecasts?
Bruijn, Bert de; Franses, Philip Hans - In: Journal of risk and financial management : JRFM 11 (2018) 3, pp. 1-20
available forecast for each forecaster and the difference between the average and the forecast that this forecaster previously … made. We extended the knowledge base by analyzing the unpredictable component of the earnings forecast. We found that for … some forecasters the unpredictable component can be used to improve upon the predictable forecast, but we also found that …
Persistent link: https://www.econbiz.de/10011895745
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Short patches of outliers, ARCH and volatility modeling
Franses, Philip Hans; Dijk, Dick van; Lucas, André - 1998
In this paper we test for (Generalized) AutoRegressive Conditional Heteroskedasticity [(G)ARCH] in daily data on 22 exchange rates and 13 stock market indices using the standard Lagrange Multiplier [LM] test for GARCH and a LM test that is resistant to patches of additive outliers. The data span...
Persistent link: https://www.econbiz.de/10011284080
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Inflation, forecast intervals and long memory regression models
Bos, Charles S.; Franses, Philip Hans; Ooms, Marius - 2001
) model with those for ARIMA(1,d,1) models withfixed order of d=0 and d=1 for inflation. Comparing meansquared forecast errors …,1,1) model provides the best forecasts, but itsmulti-step forecast intervals are too large. …
Persistent link: https://www.econbiz.de/10011316885
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Random-coefficient periodic autoregression
Franses, Philip Hans (contributor);  … - 2005
Persistent link: https://www.econbiz.de/10003116189
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Intertemporal similarity of economic time series : an application of dynamic time warping
Franses, Philip Hans; Wiemann, Thomas - 2018
Persistent link: https://www.econbiz.de/10011893650
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Constructing Seasonally Adjusted Data with Time-Varying Confidence Intervals
Koopman, Siem Jan; Franses, Philip Hans - 2003
Seasonal adjustment methods transform observed time series data into estimated data, where these estimated data are constructed such that they show no or almost no seasonal variation. An advantage of model-based methods is that these can provide confidence intervals around the seasonally...
Persistent link: https://www.econbiz.de/10014091918