The paper suggests two encompassing tests for evaluating multi-step system forecasts invariant to linear transformations. An invariant measure for forecast accuracy is necessary as the conclusions otherwise can depend on how the forecasts are reported (e.g., as in level or growth rates). Therefore, a measure based on the prediction likelihood of the forecast for all variables at all horizons is used. Both tests are based on a generalization of the encompassing test for univariate forecasts where potential heteroscedasticity and autocorrelation in the forecasts are considered. The tests are used in evaluating quarterly multi-step system forecasts made by Statistics Norway
We develop tests for the null hypothesis that forecasts become uninformative beyond some maximum for...
Forecasting and forecast evaluation are inherently sequential tasks. Predictions are often issued on...
This paper proposes a method for comparing and combining conditional quantile forecasts based on the...
The paper suggests two encompassing tests for evaluating multi-step system forecasts invariant to li...
The paper derives a test for equal predictability of multi-step-ahead system forecasts that is invar...
Data set and code that replicates the tables in the papers "Encompassing tests for evaluating m...
We consider tests of forecast encompassing for probability forecasts, for both quadratic and logarit...
We consider tests of forecast encompassing for probability forecasts, for both quadratic and logarit...
In this paper we demonstrate that forecast encompassing tests are valuable tools in getting an insig...
We examine the asymptotic and finite-sample properties of tests for equal forecast accuracy and enco...
Multi-horizon forecasts from large scale models play important roles in ongoing policy debates acros...
Tiao and Xu (1993) proposed a test of whether a time series model, estimated by maximum likelihood, ...
Linear models are invariant under non-singular, scale-preserving linear transformations, whereas mea...
A new method of assessing the comparative quality of forecasting models is introduced. This method f...
One popular method for testing the validity of a model's forecasts is to use the probability integra...
We develop tests for the null hypothesis that forecasts become uninformative beyond some maximum for...
Forecasting and forecast evaluation are inherently sequential tasks. Predictions are often issued on...
This paper proposes a method for comparing and combining conditional quantile forecasts based on the...
The paper suggests two encompassing tests for evaluating multi-step system forecasts invariant to li...
The paper derives a test for equal predictability of multi-step-ahead system forecasts that is invar...
Data set and code that replicates the tables in the papers "Encompassing tests for evaluating m...
We consider tests of forecast encompassing for probability forecasts, for both quadratic and logarit...
We consider tests of forecast encompassing for probability forecasts, for both quadratic and logarit...
In this paper we demonstrate that forecast encompassing tests are valuable tools in getting an insig...
We examine the asymptotic and finite-sample properties of tests for equal forecast accuracy and enco...
Multi-horizon forecasts from large scale models play important roles in ongoing policy debates acros...
Tiao and Xu (1993) proposed a test of whether a time series model, estimated by maximum likelihood, ...
Linear models are invariant under non-singular, scale-preserving linear transformations, whereas mea...
A new method of assessing the comparative quality of forecasting models is introduced. This method f...
One popular method for testing the validity of a model's forecasts is to use the probability integra...
We develop tests for the null hypothesis that forecasts become uninformative beyond some maximum for...
Forecasting and forecast evaluation are inherently sequential tasks. Predictions are often issued on...
This paper proposes a method for comparing and combining conditional quantile forecasts based on the...