標(biāo)題: Titlebook: Macroeconomic Forecasting in the Era of Big Data; Theory and Practice Peter Fuleky Book 2020 Springer Nature Switzerland AG 2020 Big Data.M [打印本頁] 作者: Hoover 時(shí)間: 2025-3-21 16:44
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書目名稱Macroeconomic Forecasting in the Era of Big Data讀者反饋學(xué)科排名
作者: allude 時(shí)間: 2025-3-21 23:09 作者: 安心地散步 時(shí)間: 2025-3-22 03:27
https://doi.org/10.1007/978-3-030-31150-6Big Data; Macroeconomic forecasting; Dimension reduction; Shrinkage; Model forecast combination; Dynamic 作者: Encumber 時(shí)間: 2025-3-22 06:20 作者: 談判 時(shí)間: 2025-3-22 11:25
Advanced Studies in Theoretical and Applied Econometricshttp://image.papertrans.cn/m/image/620986.jpg作者: 食物 時(shí)間: 2025-3-22 14:29
Dynamic Factor Modelsoven to be indispensable in macroeconomic forecasting. This chapter surveys the evolution of these models from their pre-big-data origins to the large-scale models of recent years. We review the associated estimation theory, forecasting approaches, and several extensions of the basic framework.作者: 增長(zhǎng) 時(shí)間: 2025-3-22 17:07
Bootstrap Aggregating and Random Foresting and Decision Trees. In this chapter, we explore Bagging, Random Forest, and their variants in various aspects of theory and practice. We also discuss applications based on these methods in economic forecasting and inference.作者: MORPH 時(shí)間: 2025-3-23 00:19 作者: myelography 時(shí)間: 2025-3-23 02:05 作者: 不合 時(shí)間: 2025-3-23 08:23
Martin Feldkircher,Florian Huber,Michael Pfarrhofern selbst, der die M?chte des Todes besiegt. Eine signifikante Umdeutung des Odysseus-Mythos erfolgt an der Schwelle zur Neuzeit bei Dante, der die Sicht aus Vergils . übernimmt. In der . ist Odysseus der vom Welthunger getriebene Abenteurer, der Grenzen missachtet, Schiffbruch erleidet und schlie?li作者: Omniscient 時(shí)間: 2025-3-23 12:42
Joshua C. C. ChanErleuchtungssehnsucht, die wie ein roter Faden durch Bednarz’ Erz?hlung l?uft. Anhand ausgew?hlter Akteure und Texte vom 18. Jahrhundert bis in die Gegenwart wird der Wirkm?chtigkeit des Begriffs in wissenschaftlichen, theologischen, religi?sen und literarischen Verwendungen nachgegangen, sowie sein作者: 臥虎藏龍 時(shí)間: 2025-3-23 15:14 作者: 蹣跚 時(shí)間: 2025-3-23 20:28
Thomas R. CookErleuchtungssehnsucht, die wie ein roter Faden durch Bednarz’ Erz?hlung l?uft. Anhand ausgew?hlter Akteure und Texte vom 18. Jahrhundert bis in die Gegenwart wird der Wirkm?chtigkeit des Begriffs in wissenschaftlichen, theologischen, religi?sen und literarischen Verwendungen nachgegangen, sowie sein作者: 類型 時(shí)間: 2025-3-24 02:05
ichkeiten (?sthetik) dar. Wesentliche Motive, um ihr Verh?ltnis zu denken, sind die freie Wiederholung von Wirklichkeit, der Primat der M?glichkeit gegenüber fixierter Wirklichkeit und die ?Sph?re“ als Denken eines sprachlich-intersubjektiven Weltumgangs. Diese Motive werden im zweiten Teil des Arti作者: 推延 時(shí)間: 2025-3-24 03:22 作者: Gnrh670 時(shí)間: 2025-3-24 07:20
Tae-Hwy Lee,Aman Ullah,Ran Wang. Hierüber herrscht sogar seltene gesamtdeutsche Einmütigkeit. Doch was im Schreiben eines Plenzdorf, Braun oder Kunert zur Erstreitung der Rechte des Individuums in einer auf kollektive Verhaltensmuster festgelegten Gesellschaft seine Funktion hat, ger?t in der Bundesrepublik zur Farce. Ob Mutterfr作者: condemn 時(shí)間: 2025-3-24 11:52
Jianghao Chu,Tae-Hwy Lee,Aman Ullah,Ran Wangser Zeit die Vorstadien aufzusuchen und zu propagieren, hei?t, im Alltag die M?glichkeiten der Ver?nderbarkeit in Richtung des Zu-Wort-Kommen-Lassens der Arbeit aufzuspüren, mehr noch: sie verallgemeinern zu helfen, sie . — zun?chst vielleicht nur: . — zu machen. Solange operatives Arbeiten noch ein作者: plasma-cells 時(shí)間: 2025-3-24 15:14
Federico Bassetti,Roberto Casarin,Francesco Ravazzolokratie und Sowjetmarxismus, w?hrend der eigentliche Verfasser, der schlesische Lyriker Friedrich von Logau, im Aphorismus die religi?se Unbedingtheit des barocken Mystikers formuliert hat; man k?nnte sich schlie?lich den Spruch auch als Haussegen eines preu?ischen Offiziers vorstellen. Das soll hei?作者: 弄皺 時(shí)間: 2025-3-24 20:15
Neural Networksation functions, and model architecture. Following this, two additional types of neural network model are discussed: recurrent neural networks and encoder-decoder models. The chapter concludes with an empirical application of all three models to the task of forecasting unemployment.作者: AGATE 時(shí)間: 2025-3-25 00:41 作者: Handedness 時(shí)間: 2025-3-25 05:39 作者: 兩種語言 時(shí)間: 2025-3-25 11:22 作者: Pessary 時(shí)間: 2025-3-25 12:16 作者: Exploit 時(shí)間: 2025-3-25 17:15 作者: 爭(zhēng)議的蘋果 時(shí)間: 2025-3-25 22:32 作者: Thrombolysis 時(shí)間: 2025-3-26 02:48
Variable Selection and Feature Screeningdinal data, and survival data. From the perspective of modeling, we review various models including linear model, generalized linear model, additive model, varying-coefficient model, Cox model, etc. We also cover some model-free feature screening procedures.作者: 火花 時(shí)間: 2025-3-26 06:14
Bayesian Model Averaging genre categorization, but also variables derived from social media content which capture sentiment and volume of Twitter messages. We discuss the impact of different model specifications and describe how results are obtained using the open-source package BMS available for the R environment for statistical computing and graphics.作者: 閑蕩 時(shí)間: 2025-3-26 09:52 作者: 牲畜欄 時(shí)間: 2025-3-26 16:24 作者: Increment 時(shí)間: 2025-3-26 20:01 作者: 支柱 時(shí)間: 2025-3-26 22:35
Volatility Forecasting in a Data Rich Environmentonal dimension diverges, unless strong restrictions are imposed on the model’s dynamics. In the latter case, the models might become feasible at the expense of reduced economic intuition that can be recovered from the model fit. In turn, this could have a negative impact on the forecast and the identification of its drivers.作者: abolish 時(shí)間: 2025-3-27 04:39 作者: 泥土謙卑 時(shí)間: 2025-3-27 06:56
Book 2020; and how to evaluate forecasts, among others. Each chapter is self-contained with references, and provides solid background information, while also reviewing the latest advances in the field. Accordingly, the book offers a valuable resource for researchers, professional forecasters, and students of quantitative economics..作者: 巫婆 時(shí)間: 2025-3-27 10:18 作者: Occipital-Lobe 時(shí)間: 2025-3-27 14:22 作者: Entreaty 時(shí)間: 2025-3-27 21:15 作者: SLING 時(shí)間: 2025-3-27 22:31
Large Bayesian Vector Autoregressionsd considered only small systems with a few variables due to parameter proliferation concern and computational limitations. We first review a variety of shrinkage priors that are useful for tackling the parameter proliferation problem in large Bayesian VARs. This is followed by a detailed discussion 作者: 禁令 時(shí)間: 2025-3-28 03:59
Volatility Forecasting in a Data Rich Environments literature and on the challenges posed by the increased availability of data. There are limits to the feasibility of all models when the cross-sectional dimension diverges, unless strong restrictions are imposed on the model’s dynamics. In the latter case, the models might become feasible at the e作者: Diuretic 時(shí)間: 2025-3-28 07:24 作者: 發(fā)出眩目光芒 時(shí)間: 2025-3-28 12:46
Penalized Time Series Regressionied work, namely Ridge Regression, the Least Absolute Shrinkage and Selection Operator (Lasso), the Elastic Net, the adaptive versions of the Lasso as well as Elastic Net and the group Lasso. Other penalties are briefly presented. We discuss theoretical properties such as consistent variable selecti作者: 上坡 時(shí)間: 2025-3-28 17:57
Principal Component and Static Factor Analysisn reduction. In this chapter, we consider the forecasting problem using factor models, with special consideration to large datasets. In factor model estimation, we focus on principal component methods, and show how the estimated factors can be used to assist forecasting. Machine learning methods are作者: 持久 時(shí)間: 2025-3-28 21:16
Subspace Methodsace methods are a new class of dimension reduction methods that have been found to yield precise forecasts when applied to macroeconomic and financial data. In this chapter, we review three subspace methods: subset regression, random projection regression, and compressed regression. We provide curre作者: 變量 時(shí)間: 2025-3-28 23:47
Variable Selection and Feature Screeningthe ultra-high dimensionality of the feature space to a moderate size in a fast and efficient way and meanwhile retaining all the important features in the reduced feature space. This is referred to as the sure screening property. After feature screening, more sophisticated methods can be applied to作者: 疼死我了 時(shí)間: 2025-3-29 06:44 作者: Inferior 時(shí)間: 2025-3-29 08:16 作者: 抗體 時(shí)間: 2025-3-29 15:09 作者: 變色龍 時(shí)間: 2025-3-29 18:11
Boostingomic researches, especially when the data available is high-dimensional, i.e., the number of explanatory variables (.) is greater than the length of the sample size (.). Common approaches include factor models, the principal component analysis, and regularized regressions. However, these methods req作者: 相一致 時(shí)間: 2025-3-29 20:05
Density Forecastinge the accuracy of density forecasts are reviewed and calibration methods for improving the accuracy of forecasts are presented. The manuscript provides some numerical simulation tools to approximate predictive densities with a focus on parallel computing on graphical process units. Some simple examp作者: HOWL 時(shí)間: 2025-3-30 00:42 作者: DRAFT 時(shí)間: 2025-3-30 07:10 作者: graphy 時(shí)間: 2025-3-30 11:30
