| 1. |
- Beechey, Meredith, et al.
(författare)
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Forecasting inflation in an inflation-targeting regime : A role for informative steady-state priors
- 2010
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Ingår i: International Journal of Forecasting. - : Elsevier. - 0169-2070 .- 1872-8200. ; 26:2, s. 248-264
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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.
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| 2. |
- Fildes, Robert, et al.
(författare)
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Validation and forecasting accuracy in models of climate change
- 2011
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Ingår i: International Journal of Forecasting. - : Elsevier. - 0169-2070 .- 1872-8200. ; 27:4, s. 968-995
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Tidskriftsartikel (refereegranskat)abstract
- Forecasting researchers, with few exceptions, have ignored the current major forecasting controversy: global warming and the role of climate modelling in resolving this challenging topic. In this paper, we take a forecaster's perspective in reviewing established principles for validating the atmospheric-ocean general circulation models (AOGCMs) used in most climate forecasting, and in particular by the Intergovernmental Panel on Climate Change (IPCC). Such models should reproduce the behaviours characterising key model outputs, such as global and regional temperature changes. We develop various time series models and compare them with forecasts based on one well-established AOGCM from the UK Hadley Centre. Time series models perform strongly, and structural deficiencies in the AOGCM forecasts are identified using encompassing tests. Regional forecasts from various GCMs had even more deficiencies. We conclude that combining standard time series methods with the structure of AOGCMs may result in a higher forecasting accuracy. The methodology described here has implications for improving AOGCMs and for the effectiveness of environmental control policies which are focussed on carbon dioxide emissions alone. Critically, the forecast accuracy in decadal prediction has important consequences for environmental planning, so its improvement through this multiple modelling approach should be a priority.
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| 3. |
- Giordani, Paolo, et al.
(författare)
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Forecasting macroeconomic time series with locally adaptive signal extraction
- 2010
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Ingår i: International Journal of Forecasting. - : Elsevier BV. - 0169-2070 .- 1872-8200. ; 26:2, s. 312-325
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Tidskriftsartikel (refereegranskat)abstract
- We introduce a non-Gaussian dynamic mixture model for macroeconomic forecasting. The locally adaptive signal extraction and regression (LASER) model is designed to capture relatively persistent AR processes (signal) which are contaminated by high frequency noise. The distributions of the innovations in both noise and signal are modeled robustly using mixtures of normals. The mean of the process and the variances of the signal and noise are allowed to shift either suddenly or gradually at unknown locations and unknown numbers of times. The model is then capable of capturing movements in the mean and conditional variance of a series, as well as in the signal-to-noise ratio. Four versions of the model are estimated by Bayesian methods and used to forecast a total of nine quarterly macroeconomic series from the US, Sweden and Australia. We observe that allowing for infrequent and large parameter shifts while imposing normal and homoskedastic errors often leads to erratic forecasts, but that the model typically forecasts well if it is made more robust by allowing for non-normal errors and time varying variances. Our main finding is that, for the nine series we analyze, specifications with infrequent and large shifts in error variances outperform both fixed parameter specifications and smooth, continuous shifts when it comes to interval coverage.
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| 4. |
- Kourentzes, Nikolaos, et al.
(författare)
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Improving forecasting by estimating time series structural components across multiple frequencies
- 2014
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Ingår i: International Journal of Forecasting. - : Elsevier. - 0169-2070 .- 1872-8200. ; 30:2, s. 291-302
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Tidskriftsartikel (refereegranskat)abstract
- Identifying the most appropriate time series model to achieve a good forecasting accuracy is a challenging task. We propose a novel algorithm that aims to mitigate the importance of model selection, while increasing the accuracy. Multiple time series are constructed from the original time series, using temporal aggregation. These derivative series highlight different aspects of the original data, as temporal aggregation helps in strengthening or attenuating the signals of different time series components. In each series, the appropriate exponential smoothing method is fitted and its respective time series components are forecast. Subsequently, the time series components from each aggregation level are combined, then used to construct the final forecast. This approach achieves a better estimation of the different time series components, through temporal aggregation, and reduces the importance of model selection through forecast combination. An empirical evaluation of the proposed framework demonstrates significant improvements in forecasting accuracy, especially for long-term forecasts.
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| 5. |
- Trapero, Juan R., et al.
(författare)
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Analysis of judgmental adjustments in the presence of promotions
- 2013
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Ingår i: International Journal of Forecasting. - : Elsevier. - 0169-2070 .- 1872-8200. ; 29:2, s. 234-243
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Tidskriftsartikel (refereegranskat)abstract
- Sales forecasting is becoming increasingly complex, due to a range of factors, such as the shortening of product life cycles, increasingly competitive markets, and aggressive marketing. Often, forecasts are produced using a Forecasting Support System that integrates univariate statistical forecasts with judgment from experts in the organization. Managers then add information to the forecast, such as future promotions, potentially improving the accuracy. Despite the importance of judgment and promotions, papers devoted to studying their relationship with forecasting performance are scarce. We analyze the accuracy of managerial adjustments in periods of promotions, based on weekly data from a manufacturing company. Intervention analysis is used to establish whether judgmental adjustments can be replaced by multivariate statistical models when responding to promotional information. We show that judgmental adjustments can enhance baseline forecasts during promotions, but not systematically. Transfer function models based on past promotions information achieved lower overall forecasting errors. Finally, a hybrid model illustrates the fact that human experts still added value to the transfer function models.
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