| 1. |
- Brännäs, Kurt, et al.
(författare)
-
A new approach to modelling and forecasting monthly guest nights in hotels
- 2002
-
Ingår i: International Journal of Forecasting. - : Elsevier. - 0169-2070 .- 1872-8200. ; 18:1, s. 19-30
-
Tidskriftsartikel (refereegranskat)abstract
- Starting from a day-to-day model on hotel specific guest nights we obtain an integer-valued moving average model by cross-sectional and temporal aggregation. The two parameters of the aggregate model reflect mean check-in and the check-out probability. Letting the parameters be functions of dummy and economic variables we demonstrate the potential of the approach in terms of interesting interpretations. Empirical results are presented for a series of Norwegian guests in Swedish hotels. The results indicate strong seasonal patterns in both mean check-in and in the check-out probability. Models based on differenced series are preferred in terms of goodness-of-fit. In a forecast comparison the improvements due to economic variables are small. © 2002 International Institute of Forecasters. Published by Elsevier Science B.V.
|
|
| 2. |
|
|
| 3. |
- Brännäs, Kurt
(författare)
-
Prediction and control for a time-series count data model
- 1995
-
Ingår i: International Journal of Forecasting. - 0169-2070 .- 1872-8200. ; 11:2, s. 263-270
-
Tidskriftsartikel (refereegranskat)abstract
- Time series of count data are becoming more widely available. In a recently suggested class of models, the serial correlation between counts can conveniently be accounted for. In this paper, an easily calculated linear predictor is introduced. Control solutions for average count and for probabilities of specified events are given. An illustration based on a road accident frequency model for a Swedish county is included.
|
|
| 4. |
- Edlund, Per-Olov, et al.
(författare)
-
Forecasting the Swedish unemployment rate. VAR vs. transfer function modelling
- 1993
-
Ingår i: International Journal of Forecasting. - : Elsevier. - 0169-2070 .- 1872-8200. ; 9:1, s. 61-76
-
Tidskriftsartikel (refereegranskat)abstract
- The Swedish unemployment rate is forecast using three time series methods: the ARIMA, transfer function and Vector Autoregressive (VAR) models. Within this context, the choice of modelling strategy is discussed. It is found that the forecasting performance of VAR models is improved by explicitly taking account of cointegration between the variables in the model, despite the fact that unemployment is not cointegrated. However, the more parsimonious ARIMA and transfer function models have lower RMSE for all forecasting horizons. It is also found that the additional variables in the VAR models are important for predicting the turning points in the unemployment rate.
|
|
| 5. |
- Öller, Lars-Erik, et al.
(författare)
-
The accuracy of European growth and inflation forecasts
- 2000
-
Ingår i: International journal of forecasting. - : Elsevier B.V. - 1872-8200 .- 0169-2070. ; 16:3, s. 293-315
-
Tidskriftsartikel (refereegranskat)abstract
- One-year-ahead forecasts by the OECD and by national institutes of GDP growth and inflation in 13 European countries are analysed. RMSE was large: 1.9% for growth and 1.6% for inflation. Six (11) OECD and ten (7) institute growth forecast records were significantly better than an average growth forecast (the current year forecast). All full record-length inflation forecasts were significantly better than both naive alternatives. There was no significant difference in accuracy between the forecasts of the OECD and the institutes. Two forecasts were found to be biased and one had autocorrelated errors. Directional forecasts were significantly better than a naive alternative in one-half of the cases. Overall, inflation forecasts were significantly more accurate than growth forecasts, and in contrast to growth forecasts, they generally improved over time. This has implications for economic policy. Positively biased revisions reveal large errors in data.
|
|
| 6. |
- Athanasopoulos, George, et al.
(författare)
-
Editorial : Innovations in hierarchical forecasting
- 2024
-
Ingår i: International Journal of Forecasting. - : Elsevier. - 0169-2070 .- 1872-8200. ; 40:2, s. 427-429
-
Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)
|
|
| 7. |
- Athanasopoulos, George, et al.
(författare)
-
Forecast reconciliation : A review
- 2024
-
Ingår i: International Journal of Forecasting. - : Elsevier. - 0169-2070 .- 1872-8200. ; 40:2, s. 430-456
-
Forskningsöversikt (refereegranskat)abstract
- Collections of time series formed via aggregation are prevalent in many fields. These are commonly referred to as hierarchical time series and may be constructed cross-sectionally across different variables, temporally by aggregating a single series at different frequencies, or even generalised beyond aggregation as time series that respect linear constraints. When forecasting such time series, a desirable condition is for forecasts to be coherent: to respect the constraints. The past decades have seen substantial growth in this field with the development of reconciliation methods that ensure coherent forecasts and improve forecast accuracy. This paper serves as a comprehensive review of forecast reconciliation and an entry point for researchers and practitioners dealing with hierarchical time series. The scope of the article includes perspectives on forecast reconciliation from machine learning, Bayesian statistics and probabilistic forecasting, as well as applications in economics, energy, tourism, retail demand and demography.
|
|
| 8. |
- Athanasopoulos, George, et al.
(författare)
-
On the evaluation of hierarchical forecasts
- 2023
-
Ingår i: International Journal of Forecasting. - : Elsevier. - 0169-2070 .- 1872-8200. ; 39:4, s. 1502-1511
-
Tidskriftsartikel (refereegranskat)abstract
- The aim of this paper is to provide a thinking road-map and a practical guide to researchers and practitioners working on hierarchical forecasting problems. Evaluating the performance of hierarchical forecasts comes with new challenges stemming from both the structure of the hierarchy and the application context. We discuss several relevant dimensions for researchers and analysts: the scale and units of the time series, the issue of intermittency, the forecast horizon, the importance of multiple evaluation windows and the multiple objective decision context. We conclude with a series of practical recommendations.
|
|
| 9. |
- Beechey, Meredith, et al.
(författare)
-
Forecasting inflation in an inflation-targeting regime : A role for informative steady-state priors
- 2010
-
Ingår i: International Journal of Forecasting. - : Elsevier. - 0169-2070 .- 1872-8200. ; 26:2, s. 248-264
-
Tidskriftsartikel (refereegranskat)abstract
- Inflation targeting as a monetary-policy regime is widely associated with an explicit numerical target for the rate of inflation. This paper investigates whether the forecasting performance of Bayesian autoregressive models can be improved by incorporating information about the target. We compare a mean-adjusted specification, which allows an informative prior on the distribution for the steady state of the process, to traditional methodology. We find that the out-of-sample forecasts of the mean-adjusted autoregressive model outperform those of the traditional specification, often by non-trivial amounts, for five early adopters of inflation targeting. It is also noted that as the sample lengthens, the posterior distribution of steady-state inflation narrows more for countries with explicit point targets.
|
|
| 10. |
- Cross, Jamie L., et al.
(författare)
-
Macroeconomic forecasting with large Bayesian VARs : Global-local priors and the illusion of sparsity
- 2020
-
Ingår i: International Journal of Forecasting. - : Elsevier. - 0169-2070 .- 1872-8200. ; 36:3, s. 899-915
-
Tidskriftsartikel (refereegranskat)abstract
- A class of global-local hierarchical shrinkage priors for estimating large Bayesian vector autoregressions (BVARs) has recently been proposed. We question whether three such priors: Dirichlet-Laplace, Horseshoe, and Normal-Gamma, can systematically improve the forecast accuracy of two commonly used benchmarks (the hierarchical Minnesota prior and the stochastic search variable selection (SSVS) prior), when predicting key macroeconomic variables. Using small and large data sets, both point and density forecasts suggest that the answer is no. Instead, our results indicate that a hierarchical Minnesota prior remains a solid practical choice when forecasting macroeconomic variables. In light of existing optimality results, a possible explanation for our finding is that macroeconomic data is not sparse, but instead dense.
|
|
