International Journal of Forecasting最新文献

{"title":"Return predictability, dividend growth, and the persistence of the price–dividend ratio","authors":"Adam Goliński ,&nbsp;João Madeira ,&nbsp;Dooruj Rambaccussing","doi":"10.1016/j.ijforecast.2024.03.005","DOIUrl":"10.1016/j.ijforecast.2024.03.005","url":null,"abstract":"<div><div>Empirical evidence shows that the order of integration of returns and dividend growth is approximately equal to the order of integration of the first-differenced price–dividend ratio, which is about 0.7. Yet the present-value identity implies that the three series should be integrated of the same order. We reconcile this puzzle by showing that the aggregation of antipersistent expected returns and expected dividends gives rise to a price–dividend ratio with properties that mimic long memory in finite samples. In an empirical implementation, we extend and estimate the state-space present-value model by allowing for fractional integration in expected returns and expected dividend growth. This extension improves the model’s forecasting power in-sample and out-of-sample. In addition, expected returns and expected dividend growth modeled as ARFIMA processes are more closely related to future macroeconomic variables, which makes them suitable as leading business cycle indicators.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 1","pages":"Pages 92-110"},"PeriodicalIF":6.9,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140781596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Coupling LSTM neural networks and state-space models through analytically tractable inference","authors":"Van-Dai Vuong,&nbsp;Luong-Ha Nguyen,&nbsp;James-A. Goulet","doi":"10.1016/j.ijforecast.2024.04.002","DOIUrl":"10.1016/j.ijforecast.2024.04.002","url":null,"abstract":"<div><div>Long short-term memory (LSTM) neural networks and state-space models (SSMs) are effective tools for time series forecasting. Coupling these methods to exploit their advantages is not a trivial task because their respective inference procedures rely on different mechanisms. In this paper, we present formulations that allow for analytically tractable inference in Bayesian LSTMs and the probabilistic coupling between Bayesian LSTMs and SSMs. This is enabled by using analytical Gaussian inference as a single mechanism for inferring both the LSTM’s parameters as well as the posterior for the SSM’s hidden states. We show through several experimental comparisons that the resulting hybrid model retains the interpretability feature of SSMs, while exploiting the ability of LSTMs to learn complex seasonal patterns with minimal manual setups.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 1","pages":"Pages 128-140"},"PeriodicalIF":6.9,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140773384","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Local and global trend Bayesian exponential smoothing models","authors":"Slawek Smyl ,&nbsp;Christoph Bergmeir ,&nbsp;Alexander Dokumentov ,&nbsp;Xueying Long ,&nbsp;Erwin Wibowo ,&nbsp;Daniel Schmidt","doi":"10.1016/j.ijforecast.2024.03.006","DOIUrl":"10.1016/j.ijforecast.2024.03.006","url":null,"abstract":"<div><div>This paper describes a family of seasonal and non-seasonal time series models that can be viewed as generalisations of additive and multiplicative exponential smoothing models to model series that grow faster than linear but slower than exponential. Their development is motivated by fast-growing, volatile time series. In particular, our models have a global trend that can smoothly change from additive to multiplicative and is combined with a linear local trend. Seasonality, when used, is multiplicative in our models, and the error is always additive but heteroscedastic and can grow through a parameter sigma. We leverage state-of-the-art Bayesian fitting techniques to fit these models accurately, which are more complex and flexible than standard exponential smoothing models. When applied to the M3 competition data set, our models outperform the best algorithms in the competition and other benchmarks, thus achieving, to the best of our knowledge, the best results of per-series univariate methods on this dataset in the literature. An open-source software package of our method is available.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 1","pages":"Pages 111-127"},"PeriodicalIF":6.9,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142704582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Forecasting presidential elections: Accuracy of ANES voter intentions","authors":"Hyein Ko ,&nbsp;Natalie Jackson ,&nbsp;Tracy Osborn ,&nbsp;Michael S. Lewis-Beck","doi":"10.1016/j.ijforecast.2024.03.003","DOIUrl":"10.1016/j.ijforecast.2024.03.003","url":null,"abstract":"<div><div>Despite research on the accuracy of polls as tools for forecasting presidential elections, we lack an assessment of how accurately the ANES, arguably the most used survey in political science, measures aggregate vote intention relative to the actual election results. Our ANES 1952–2020 results indicate that the reported vote from the post-election surveys accurately measures the actual vote (e.g., it is off by 2.23 percentage points, on average). Moreover, the intended vote measure from the pre-election surveys reasonably accurately predicts the actual aggregate popular vote outcome. While outliers may exist, they do not appear to come from variations in the survey mode, sample weights, time, political party, or turnout. We conclude that political scientists can confidently use the intended vote measure, keeping in mind that forecasting the popular vote may not always reveal the actual winner.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 1","pages":"Pages 66-75"},"PeriodicalIF":6.9,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140789070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Locally tail-scale invariant scoring rules for evaluation of extreme value forecasts","authors":"Helga Kristin Olafsdottir ,&nbsp;Holger Rootzén ,&nbsp;David Bolin","doi":"10.1016/j.ijforecast.2024.02.007","DOIUrl":"10.1016/j.ijforecast.2024.02.007","url":null,"abstract":"<div><p>Statistical analysis of extremes can be used to predict the probability of future extreme events, such as large rainfalls or devastating windstorms. The quality of these forecasts can be measured through scoring rules. Locally scale invariant scoring rules give equal importance to the forecasts at different locations regardless of differences in the prediction uncertainty. This is a useful feature when computing average scores but can be an unnecessarily strict requirement when one is mostly concerned with extremes. We propose the concept of local weight-scale invariance, describing scoring rules fulfilling local scale invariance in a certain region of interest, and as a special case, local tail-scale invariance for large events. Moreover, a new version of the weighted continuous ranked probability score (wCRPS) called the scaled wCRPS (swCRPS) that possesses this property is developed and studied. The score is a suitable alternative for scoring extreme value models over areas with a varying scale of extreme events, and we derive explicit formulas of the score for the generalised extreme value distribution. The scoring rules are compared through simulations, and their usage is illustrated by modelling extreme water levels and annual maximum rainfall, and in an application to non-extreme forecasts for the prediction of air pollution.</p></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"40 4","pages":"Pages 1701-1720"},"PeriodicalIF":6.9,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0169207024000128/pdfft?md5=0dc152533b3e57e00ccdf1336680c44d&pid=1-s2.0-S0169207024000128-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142098321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Forecasting adversarial actions using judgment decomposition-recomposition","authors":"Yolanda Gomez ,&nbsp;Jesus Rios ,&nbsp;David Rios Insua ,&nbsp;Jose Vila","doi":"10.1016/j.ijforecast.2024.03.004","DOIUrl":"10.1016/j.ijforecast.2024.03.004","url":null,"abstract":"<div><div><span>In domains such as homeland security, cybersecurity, and competitive marketing, it is frequently the case that analysts need to forecast actions by other intelligent agents that impact the problem of interest. Standard structured expert judgment elicitation techniques may fall short in this type of problem as they do not explicitly take into account intentionality. We present a decomposition technique based on adversarial </span>risk analysis<span> followed by a behavioural recomposition using discrete choice models that facilitate such elicitation process and illustrate its reasonable performance through behavioural experiments.</span></div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 1","pages":"Pages 76-91"},"PeriodicalIF":6.9,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142705040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Conditionally optimal weights and forward-looking approaches to combining forecasts","authors":"Christopher G. Gibbs,&nbsp;Andrey L. Vasnev","doi":"10.1016/j.ijforecast.2024.03.002","DOIUrl":"10.1016/j.ijforecast.2024.03.002","url":null,"abstract":"<div><p>In forecasting, there is a tradeoff between in-sample fit and out-of-sample forecast accuracy. Parsimonious model specifications typically outperform richer model specifications. Consequently, information is often withheld from a forecast to prevent over-fitting the data. We show that one way to exploit this information is through forecast combination. Optimal combination weights in this environment minimize the conditional mean squared error that balances the conditional bias and the conditional variance of the combination. The bias-adjusted conditionally optimal forecast weights are time varying and forward looking. Real-time tests of conditionally optimal combinations of model-based forecasts and surveys of professional forecasters show significant gains in forecast accuracy relative to standard benchmarks for inflation and other macroeconomic variables.</p></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"40 4","pages":"Pages 1734-1751"},"PeriodicalIF":6.9,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S016920702400027X/pdfft?md5=a9a2fe1ffd1994772550f8bd88d4ead2&pid=1-s2.0-S016920702400027X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142098323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A loss discounting framework for model averaging and selection in time series models","authors":"Dawid Bernaciak,&nbsp;Jim E. Griffin","doi":"10.1016/j.ijforecast.2024.03.001","DOIUrl":"10.1016/j.ijforecast.2024.03.001","url":null,"abstract":"<div><p>We introduce a loss discounting framework for model and forecast combination, which generalises and combines Bayesian model synthesis and generalized Bayes methodologies. We use a loss function to score the performance of different models and introduce a multilevel discounting scheme that allows for a flexible specification of the dynamics of the model weights. This novel and simple model combination approach can be easily applied to large-scale model averaging/selection, handle unusual features such as sudden regime changes and be tailored to different forecasting problems. We compare our method to established and state-of-the-art methods for several macroeconomic forecasting examples. The proposed method offers an attractive, computationally efficient alternative to the benchmark methodologies and often outperforms more complex techniques.</p></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"40 4","pages":"Pages 1721-1733"},"PeriodicalIF":6.9,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0169207024000268/pdfft?md5=e82af80360f437576bd13d36da34ea30&pid=1-s2.0-S0169207024000268-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142098322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hierarchical forecasting at scale","authors":"Olivier Sprangers ,&nbsp;Wander Wadman ,&nbsp;Sebastian Schelter ,&nbsp;Maarten de Rijke","doi":"10.1016/j.ijforecast.2024.02.006","DOIUrl":"10.1016/j.ijforecast.2024.02.006","url":null,"abstract":"<div><p>Hierarchical forecasting techniques allow for the creation of forecasts that are coherent with respect to a pre-specified hierarchy of the underlying time series. This targets a key problem in e-commerce, where we often find millions of products across many product hierarchies, and forecasts must be made for individual products and product aggregations. However, existing hierarchical forecasting techniques scale poorly when the number of time series increases, which limits their applicability at a scale of millions of products.</p><p>In this paper, we propose to learn a coherent forecast for millions of products with a single bottom-level forecast model by using a loss function that directly optimizes the hierarchical product structure. We implement our loss function using sparse linear algebra, such that the number of operations in our loss function scales quadratically rather than cubically with the number of products and levels in the hierarchical structure. The benefit of our sparse hierarchical loss function is that it provides practitioners with a method of producing bottom-level forecasts that are coherent to any chosen cross-sectional or temporal hierarchy. In addition, removing the need for a post-processing step as required in traditional hierarchical forecasting techniques reduces the computational cost of the prediction phase in the forecasting pipeline and its deployment complexity.</p><p>In our tests on the public M5 dataset, our sparse hierarchical loss function performs up to 10% better as measured by RMSE and MAE than the baseline loss function. Next, we implement our sparse hierarchical loss function within a gradient boosting-based forecasting model at bol.com, a large European e-commerce platform. At bol.com, each day, a forecast for the weekly demand of every product for the next twelve weeks is required. In this setting, our sparse hierarchical loss resulted in an improved forecasting performance as measured by RMSE of about 2% at the product level, compared to the baseline model, and an improvement of about 10% at the product level as measured by MAE. Finally, we found an increase in forecasting performance of about 5%–10% (both RMSE and MAE) when evaluating the forecasting performance across the cross-sectional hierarchies we defined. These results demonstrate the usefulness of our sparse hierarchical loss applied to a production forecasting system at a major e-commerce platform.</p></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"40 4","pages":"Pages 1689-1700"},"PeriodicalIF":6.9,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0169207024000116/pdfft?md5=9360c19bcfcd3805bc4065518de97167&pid=1-s2.0-S0169207024000116-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142098320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Factor-augmented forecasting in big data","authors":"","doi":"10.1016/j.ijforecast.2024.02.004","DOIUrl":"10.1016/j.ijforecast.2024.02.004","url":null,"abstract":"<div><p>This paper evaluates the predictive performance of various factor estimation methods in big data. Extensive forecasting experiments are examined using seven factor estimation methods with 13 decision rules determining the number of factors. The out-of-sample forecasting results show that the first Partial Least Squares factor (1-PLS) tends to be the best-performing method among all the possible alternatives. This finding is prevalent in many target variables under different forecasting horizons and models. This significant improvement can be explained by the PLS factor estimation strategy that considers the covariance with the target variable. Second, using a consistently estimated number of factors may not necessarily improve forecasting performance. The greatest predictive gain often derives from decision rules that do not consistently estimate the true number of factors.</p></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"40 4","pages":"Pages 1660-1688"},"PeriodicalIF":6.9,"publicationDate":"2024-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0169207024000098/pdfft?md5=bc3f0812065997cb8e01528b63ed0435&pid=1-s2.0-S0169207024000098-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140151749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}