標題: Titlebook: Econometrics with Machine Learning; Felix Chan,László Mátyás Book 2022 The Editor(s) (if applicable) and The Author(s), under exclusive li [打印本頁] 作者: 一個希拉里 時間: 2025-3-21 19:43
書目名稱Econometrics with Machine Learning影響因子(影響力)
書目名稱Econometrics with Machine Learning影響因子(影響力)學科排名
書目名稱Econometrics with Machine Learning網(wǎng)絡公開度
書目名稱Econometrics with Machine Learning網(wǎng)絡公開度學科排名
書目名稱Econometrics with Machine Learning被引頻次
書目名稱Econometrics with Machine Learning被引頻次學科排名
書目名稱Econometrics with Machine Learning年度引用
書目名稱Econometrics with Machine Learning年度引用學科排名
書目名稱Econometrics with Machine Learning讀者反饋
書目名稱Econometrics with Machine Learning讀者反饋學科排名
作者: NAVEN 時間: 2025-3-21 21:55
Marion A. Hersh,Michael A. Johnsoniscrete outcome, problems. Overall, the chapter attempts to identify the nexus between these ML methods and conventional techniques ubiquitously used in applied econometrics. This includes a discussion of the advantages and disadvantages of each approach. Several benefits, as well as strong connecti作者: 臨時抱佛腳 時間: 2025-3-22 04:15
Non-Coding RNA Function and Structure,. Epidemiologists have generally approached such problems using propensity score matching or inverse probability treatment weighting within a potential outcomes framework. This approach still focuses on the estimation of a parameter in a structural model. A more recent method, known as doubly robust作者: AORTA 時間: 2025-3-22 05:23 作者: MEET 時間: 2025-3-22 12:24
,Log-Arithmetic, with Single and?Dual Base,y projecting the unconstrained index into the null space of this operator or by directly finding the closest solution of the functional equation into this null space.We also acknowledge that policymakers may incur costs when moving away from the status quo. . is thus introduced as an intermediate se作者: formula 時間: 2025-3-22 15:13
The Use of Machine Learning in Treatment Effect Estimation,作者: formula 時間: 2025-3-22 17:41
1570-5811 ven more readily applicable in econometrics?.As the data structures in economic and financial data become more complex and models become more sophisticated, the book takes a multidisciplinary approach in develo978-3-031-15151-4978-3-031-15149-1Series ISSN 1570-5811 Series E-ISSN 2214-7977 作者: 星球的光亮度 時間: 2025-3-22 22:33 作者: 激勵 時間: 2025-3-23 04:05 作者: FRAUD 時間: 2025-3-23 06:14 作者: 和平主義者 時間: 2025-3-23 11:17
Fairness in Machine Learning and Econometrics,y projecting the unconstrained index into the null space of this operator or by directly finding the closest solution of the functional equation into this null space.We also acknowledge that policymakers may incur costs when moving away from the status quo. . is thus introduced as an intermediate se作者: Infraction 時間: 2025-3-23 13:51 作者: CURL 時間: 2025-3-23 20:12
1570-5811 heory and in practice.Takes a multidisciplinary approach in This book helps and promotes the use of machine learning tools and techniques in econometrics and explains how machine learning can enhance and expand the econometrics toolbox in theory and in practice.?.Throughout the volume, the authors r作者: 溝通 時間: 2025-3-24 00:25 作者: 散步 時間: 2025-3-24 04:31 作者: 針葉樹 時間: 2025-3-24 10:24 作者: 半圓鑿 時間: 2025-3-24 14:13
Martin Gro?,Birgit Hennig,Frank Wallhoff use of partially penalized methods for statistical inference. Monte Carlo simulations suggest that these methods perform reasonably well. Extensions of these estimators to a panel data setting are also discussed, especially in relation to fixed effects models.作者: ELATE 時間: 2025-3-24 16:28 作者: 含沙射影 時間: 2025-3-24 21:26 作者: cloture 時間: 2025-3-25 01:52 作者: 修飾語 時間: 2025-3-25 03:58
Poverty, Inequality and Development Studies with Machine Learning,iques have been the main contribution in the theoretical arena, whereas taking advantage of the increased availability of new data sources to build or improve the outcome variable has been the main contribution in the empirical front. These inputs would not have been possible without the improvement in computational power.作者: BLUSH 時間: 2025-3-25 08:11
Toward a Concrete Logic: Discretastic discount factor and purposefully to test and evaluate existing asset pricing models. Beyond those pertinent applications, machine learning techniques also lend themselves to prediction problems in the domain of empirical asset pricing.作者: Diaphragm 時間: 2025-3-25 14:54 作者: 確定的事 時間: 2025-3-25 17:04 作者: 初學者 時間: 2025-3-25 22:04
978-3-031-15151-4The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl作者: pericardium 時間: 2025-3-26 02:47
Econometrics with Machine Learning978-3-031-15149-1Series ISSN 1570-5811 Series E-ISSN 2214-7977 作者: 神化怪物 時間: 2025-3-26 07:45 作者: chapel 時間: 2025-3-26 10:00
Advanced Studies in Theoretical and Applied Econometricshttp://image.papertrans.cn/e/image/301474.jpg作者: 消毒 時間: 2025-3-26 16:27
Martin Gro?,Birgit Hennig,Frank Wallhofftric analysis. Specifically, it examines their applicability in the context of linear regression models. The asymptotic properties of these estimators are discussed and the implications on statistical inference are explored. Given the existing knowledge of these estimators, the chapter advocates the作者: 吃掉 時間: 2025-3-26 17:35 作者: 錯誤 時間: 2025-3-26 23:09
https://doi.org/10.1007/978-3-031-51183-7data when a large set of predictors is available and the target variable is a scalar. We start by defining the forecasting scheme setup as well as different approaches to compare forecasts generated by different models/methods. More specifically, we review three important techniques to compare forec作者: ARK 時間: 2025-3-27 02:53 作者: 文件夾 時間: 2025-3-27 06:54
Linkage Disequilibrium Mapping Concepts,g economic theory but are also helpful in examining their applications in empirical analyses. This has been particularly the case recently as data associated with networks are often readily available. While researchers may have access to real-world network structured data, in many cases, their volum作者: Heart-Rate 時間: 2025-3-27 13:09
,Log-Arithmetic, with Single and?Dual Base,gates individual characteristics of the observations of the learning sample. But this information aggregation does not consider any potential selection on unobservables and any status quo biases which may be contained in the training sample. The latter bias has raised concerns around the so-called .作者: ABIDE 時間: 2025-3-27 17:17 作者: 粗語 時間: 2025-3-27 20:48
Assortment and Merchandising Strategylopment (PID) studies. It proposes a novel taxonomy to classify the contributions of ML methods and new data sources used in this field. Contributions lie in two main categories. The first is making available better measurements and forecasts of PID indicators in terms of frequency, granularity, and作者: stress-response 時間: 2025-3-28 00:11
Toward a Concrete Logic: Discreta additional benefits that machine learning – in addition to, or in combination with, standard econometric approaches – can bring to the table. This issue is of particular importance because in recent years, improved data availability and increased computational facilities have had huge effects on fi作者: 放棄 時間: 2025-3-28 05:43
Linear Econometric Models with Machine Learning,tric analysis. Specifically, it examines their applicability in the context of linear regression models. The asymptotic properties of these estimators are discussed and the implications on statistical inference are explored. Given the existing knowledge of these estimators, the chapter advocates the作者: 平常 時間: 2025-3-28 09:09 作者: Melanoma 時間: 2025-3-28 13:45
Forecasting with Machine Learning Methods,data when a large set of predictors is available and the target variable is a scalar. We start by defining the forecasting scheme setup as well as different approaches to compare forecasts generated by different models/methods. More specifically, we review three important techniques to compare forec作者: 充滿人 時間: 2025-3-28 18:35 作者: 領巾 時間: 2025-3-28 22:13 作者: Consequence 時間: 2025-3-29 02:48
Fairness in Machine Learning and Econometrics,gates individual characteristics of the observations of the learning sample. But this information aggregation does not consider any potential selection on unobservables and any status quo biases which may be contained in the training sample. The latter bias has raised concerns around the so-called .作者: amygdala 時間: 2025-3-29 04:06 作者: 索賠 時間: 2025-3-29 09:59
Poverty, Inequality and Development Studies with Machine Learning,lopment (PID) studies. It proposes a novel taxonomy to classify the contributions of ML methods and new data sources used in this field. Contributions lie in two main categories. The first is making available better measurements and forecasts of PID indicators in terms of frequency, granularity, and作者: opinionated 時間: 2025-3-29 13:32 作者: orient 時間: 2025-3-29 17:10 作者: 濃縮 時間: 2025-3-29 23:15
Clare Kosnik,Pooja Dharamshi,Lydia Mennaust a few steps on a dry, insulative carpet. Static electricity can also cause the slip (or shirt) to cling to your body, computers to go down, fibers to filter, or tankers to explode. But it is also by use of static electric effects that we can clean the smoke from power plants, make photocopies or
