共查询到9条相似文献，搜索用时 0 毫秒
1.
We investigate the impact of corrections for dynamic selection bias on forecasting accuracy in a multi‐period stay/leave model. While corrections for selection bias are needed for consistent coefficient estimates, they do not necessarily produce more accurate forecasts than uncorrected techniques. Theorem 1 shows that, apart from estimation errors, a shrinkage principle applies: the heterogeneity restriction imposed by uncorrected and combination techniques improves accuracy for forecasting individuals that leave, and hurts accuracy for forecasting individuals that stay. This has important implications for decision making because of the potential for asymmetric losses. We also present an illustrative empirical application and results from Monte Carlo experiments. We find that differences in relative accuracy vary directly with the degree of selection bias and inversely with the percentage of the initial population that stays. Copyright © 2007 John Wiley & Sons, Ltd. 相似文献
2.
Wojciech W. Charemza 《Journal of forecasting》2002,21(6):417-433
Macroeconomic model builders attempting to construct forecasting models frequently face constraints of data scarcity in terms of short time series of data, and also of parameter non‐constancy and underspecification. Hence, a realistic alternative is often to guess rather than to estimate parameters of such models. This paper concentrates on repetitive guessing (drawing) parameters from iteratively changing distributions, with the straightforward objective function being that of minimization of squares of ex‐post prediction errors, weighted by penalty weights and subject to a learning process. The examples are those of a Monte Carlo analysis of a regression problem and of a dynamic disequilibrium model. It is also an example of an empirical econometric model of the Polish economy. Copyright © 2002 John Wiley & Sons, Ltd. 相似文献
3.
Jan Prüser 《Journal of forecasting》2019,38(7):621-631
Forecasting with many predictors provides the opportunity to exploit a much richer base of information. However, macroeconomic time series are typically rather short, raising problems for conventional econometric models. This paper explores the use of Bayesian additive regression trees (Bart) from the machine learning literature to forecast macroeconomic time series in a predictor‐rich environment. The interest lies in forecasting nine key macroeconomic variables of interest for government budget planning, central bank policy making and business decisions. It turns out that Bart is a valuable addition to existing methods for handling high dimensional data sets in a macroeconomic context. 相似文献
4.
Jan Prüser 《Journal of forecasting》2019,38(1):29-38
Dynamic model averaging (DMA) is used extensively for the purpose of economic forecasting. This study extends the framework of DMA by introducing adaptive learning from model space. In the conventional DMA framework all models are estimated independently and hence the information of the other models is left unexploited. In order to exploit the information in the estimation of the individual time‐varying parameter models, this paper proposes not only to average over the forecasts but, in addition, also to dynamically average over the time‐varying parameters. This is done by approximating the mixture of individual posteriors with a single posterior, which is then used in the upcoming period as the prior for each of the individual models. The relevance of this extension is illustrated in three empirical examples involving forecasting US inflation, US consumption expenditures, and forecasting of five major US exchange rate returns. In all applications adaptive learning from model space delivers improvements in out‐of‐sample forecasting performance. 相似文献
5.
Hui Feng 《Journal of forecasting》2009,28(3):183-193
In this paper we investigate the impact of data revisions on forecasting and model selection procedures. A linear ARMA model and nonlinear SETAR model are considered in this study. Two Canadian macroeconomic time series have been analyzed: the real‐time monetary aggregate M3 (1977–2000) and residential mortgage credit (1975–1998). The forecasting method we use is multi‐step‐ahead non‐adaptive forecasting. Copyright © 2008 John Wiley & Sons, Ltd. 相似文献
6.
Ming Yin;Feiya Lu;Xingxuan Zhuo;Wangzi Yao;Jialong Liu;Jijiao Jiang; 《Journal of forecasting》2024,43(2):344-365
Historical tourism volume, search engine data, and weather calendar data have close causal relationship with daily tourism volume. However, when used in the prediction of daily tourism volume, the feature variables of the huge and complex search engine data do not have strong independence. These repetitive and highly relevant data must be analyzed and selected; otherwise, they will increase the training burden of neural network and reduce the prediction effect. This study proposes a daily tourism volume prediction model, maximum correlation minimum redundancy feature selection and long short-term memory, on the basis of feature selection and deep learning. Firstly, the multivariate high-dimensional features, including search engine data and weather factors, are selected to identify the key influencing factors. Secondly, the deep neural network is used to make a multistep forward rolling prediction of daily tourism volume. Results show that keywords of famous scenic spots, weather, historical tourism volume, and tourism strategies in the search engine data significantly improve the prediction accuracy of daily tourism volume. The proposed maximum correlation minimum redundancy feature selection and long short-term memory model performs better than other models, such as autoregressive integrated moving average, multiple regression, support vector machine, and long short-term memory. 相似文献
7.
Xuejun Chen;Ying Wang;Haitao Zhang;Jianzhou Wang; 《Journal of forecasting》2024,43(5):1682-1705
Accurate wind speed prediction is of great importance for the operation of wind farms, and extensive efforts have been made to develop effective forecasting methods in this regard. However, the feature selection of data input as well as optimization of deep learning models have received comparatively less attention, leading to unreliable forecasting results. This research proposes a novel hybrid model that integrates data preprocessing, feature selection, and optimized forecasting for improved wind speed prediction. Specifically, a powerful preprocessing technique is utilized to reduce data noise disturbances, while an innovative two-stage feature selection is designed to achieve the optimal input data format for forecasting purposes. Moreover, a hybrid forecasting module based on long-short term memory, which is optimized by the Bayesian optimization algorithm, has been developed to enhance the efficiency and reliability of the model. The empirical study used 10-min interval wind speed data of four seasons for presentation and evaluation results demonstrated its superior performance in effectively learning the volatility and irregularity features of wind speed series, which established a solid foundation for practical applications in wind power systems. 相似文献
8.
In this article, we propose a regression model for sparse high‐dimensional data from aggregated store‐level sales data. The modeling procedure includes two sub‐models of topic model and hierarchical factor regressions. These are applied in sequence to accommodate high dimensionality and sparseness and facilitate managerial interpretation. First, the topic model is applied to aggregated data to decompose the daily aggregated sales volume of a product into sub‐sales for several topics by allocating each unit sale (“word” in text analysis) in a day (“document”) into a topic based on joint‐purchase information. This stage reduces the dimensionality of data inside topics because the topic distribution is nonuniform and product sales are mostly allocated into smaller numbers of topics. Next, the market response regression model for the topic is estimated from information about items in the same topic. The hierarchical factor regression model we introduce, based on canonical correlation analysis for original high‐dimensional sample spaces, further reduces the dimensionality within topics. Feature selection is then performed on the basis of the credible interval of the parameters' posterior density. Empirical results show that (i) our model allows managerial implications from topic‐wise market responses according to the particular context, and (ii) it performs better than do conventional category regressions in both in‐sample and out‐of‐sample forecasts. 相似文献
9.
We consider finite state-space non-homogeneous hidden Markov models for forecasting univariate time series. Given a set of predictors, the time series are modeled via predictive regressions with state-dependent coefficients and time-varying transition probabilities that depend on the predictors via a logistic/multinomial function. In a hidden Markov setting, inference for logistic regression coefficients becomes complicated and in some cases impossible due to convergence issues. In this paper, we aim to address this problem utilizing the recently proposed Pólya-Gamma latent variable scheme. Also, we allow for model uncertainty regarding the predictors that affect the series both linearly — in the mean — and non-linearly — in the transition matrix. Predictor selection and inference on the model parameters are based on an automatic Markov chain Monte Carlo scheme with reversible jump steps. Hence the proposed methodology can be used as a black box for predicting time series. Using simulation experiments, we illustrate the performance of our algorithm in various setups, in terms of mixing properties, model selection and predictive ability. An empirical study on realized volatility data shows that our methodology gives improved forecasts compared to benchmark models. 相似文献