Econometrics and Statistics最新文献

Empirical best predictors under multivariate Fay-Herriot models and their numerical approximation 多变量费-赫里奥特模型下的经验最佳预测值及其数值近似值

IF 1.9

Econometrics and Statistics Pub Date : 2024-09-10 DOI: 10.1016/j.ecosta.2024.09.001

Jan Pablo Burgard, Joscha Krause, Domingo Morales, Anna-Lena Wölwer

引用次数: 0

Forecasting with Machine Learning methods and multiple large datasets[formula omitted] 使用机器学习方法和多个大型数据集进行预测[公式省略]

IF 1.9

Econometrics and Statistics Pub Date : 2024-09-06 DOI: 10.1016/j.ecosta.2024.08.003

Nikoleta Anesti, Eleni Kalamara, George Kapetanios

{"title":"Forecasting with Machine Learning methods and multiple large datasets[formula omitted]","authors":"Nikoleta Anesti, Eleni Kalamara, George Kapetanios","doi":"10.1016/j.ecosta.2024.08.003","DOIUrl":"https://doi.org/10.1016/j.ecosta.2024.08.003","url":null,"abstract":"The usefulness of machine learning techniques for forecasting macroeconomic variables using multiple large datasets is considered. The predictive content of surveys is compared with text-based indicators from newspaper articles and a standard macroeconomic dataset, extending the evidence on the contribution of each dataset in predicting economic activity. Among the linear models, the Ridge regression and the Partial Least Squares models report the largest gains consistently for most of the forecasting horizons, and among the non linear machine learning models, Support Vector Regression performs better at shorter horizons compared to the Neural Networks and Random Forest that yield more accurate forecasts up to two years ahead. Text based indicators have similar informational content to surveys, albeit combining the two datasets provides with more accurate forecasts for most of the forecast horizons. The largest forecasting gains are overwhelmingly concentrated at the shorter horizons for the majority of models and datasets and they decrease significantly after one year. Non-linear machine learning models appear to be mostly useful during the Great Financial Crisis and perform similarly to their linear counterparts in more normal periods.","PeriodicalId":54125,"journal":{"name":"Econometrics and Statistics","volume":"27 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142218394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Specification tests for normal/gamma and stable/gamma stochastic frontier models based on empirical transforms 基于经验变换的正态/伽马和稳定/伽马随机前沿模型的规格检验

IF 1.9

Econometrics and Statistics Pub Date : 2024-08-28 DOI: 10.1016/j.ecosta.2024.08.002

Christos K. Papadimitriou, Simos G. Meintanis, Bernardo B. Andrade, Mike G. Tsionas

引用次数: 0

A Bayesian flexible model for testing Granger causality 检验格兰杰因果关系的贝叶斯灵活模型

IF 1.9

Econometrics and Statistics Pub Date : 2024-08-03 DOI: 10.1016/j.ecosta.2024.08.001

Iván Gutiérrez, Danilo Alvares, Luis Gutiérrez

引用次数: 0

Differentially Private Goodness-of-Fit Tests for Continuous Variables 连续变量的差分私有拟合优度测试

IF 2

Econometrics and Statistics Pub Date : 2024-07-01 DOI: 10.1016/j.ecosta.2021.09.007

Seung Woo Kwak , Jeongyoun Ahn , Jaewoo Lee , Cheolwoo Park

引用次数: 0

Semiparametric Averaging of Nonlinear Marginal Logistic Regressions and Forecasting for Time Series Classification 非线性边际 Logistic 回归的半参数平均化和时间序列分类预测

IF 2

Econometrics and Statistics Pub Date : 2024-07-01 DOI: 10.1016/j.ecosta.2021.11.001

Rong Peng , Zudi Lu

{"title":"Semiparametric Averaging of Nonlinear Marginal Logistic Regressions and Forecasting for Time Series Classification","authors":"Rong Peng , Zudi Lu","doi":"10.1016/j.ecosta.2021.11.001","DOIUrl":"10.1016/j.ecosta.2021.11.001","url":null,"abstract":"<div><p>Binary classification is an important issue in many applications but mostly studied for independent data in the literature. A binary time series classification is investigated by proposing a semiparametric procedure named “Model Averaging nonlinear MArginal LOgistic Regressions” (MAMaLoR) for binary time series data based on the time series information of predictor variables. The procedure involves approximating the logistic multivariate conditional regression function by combining low-dimensional non-parametric nonlinear marginal logistic regressions, in the sense of Kullback-Leibler distance. A time series conditional likelihood method is suggested for estimating the optimal averaging weights together with local maximum likelihood estimations of the nonparametric marginal time series logistic (auto)regressions. The asymptotic properties of the procedure are established under mild conditions on the time series observations that are of <span><math><mi>β</mi></math></span>-mixing property. The procedure is less computationally demanding and can avoid the “curse of dimensionality” for, and be easily applied to, high dimensional lagged information based nonlinear time series classification forecasting. The performances of the procedure are further confirmed both by Monte-Carlo simulation and an empirical study for market moving direction forecasting of the financial FTSE 100 index data.</p></div>","PeriodicalId":54125,"journal":{"name":"Econometrics and Statistics","volume":"31 ","pages":"Pages 19-37"},"PeriodicalIF":2.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86385087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Conditional Quantile Functions for Zero-Inflated Longitudinal Count Data 零膨胀纵向计数数据的条件量子函数

IF 2

Econometrics and Statistics Pub Date : 2024-07-01 DOI: 10.1016/j.ecosta.2021.09.003

Carlos Lamarche , Xuan Shi , Derek S. Young

引用次数: 0

Forecasting Near-equivalence of Linear Dimension Reduction Methods in Large Panels of Macro-variables 大型宏观变量面板中线性降维方法的预测近似性

IF 2

Econometrics and Statistics Pub Date : 2024-07-01 DOI: 10.1016/j.ecosta.2021.10.007

Alessandro Barbarino , Efstathia Bura

{"title":"Forecasting Near-equivalence of Linear Dimension Reduction Methods in Large Panels of Macro-variables","authors":"Alessandro Barbarino , Efstathia Bura","doi":"10.1016/j.ecosta.2021.10.007","DOIUrl":"10.1016/j.ecosta.2021.10.007","url":null,"abstract":"<div><p>In an extensive pseudo out-of-sample horserace, classical estimators (dynamic factor models, RIDGE and partial least squares regression) and the novel to forecasting, Regularized Sliced Inverse Regression, exhibit almost near-equivalent forecasting accuracy in a large panel of macroeconomic variables across targets, horizons and subsamples. This finding motivates the theoretical contributions in this paper. Most widely used linear dimension reduction methods are shown to solve closely related maximization problems with solutions that can be decomposed in <em>signal</em> and <em>scaling</em> components. They are organized under a common scheme that sheds light on their commonalities and differences as well as on their functionality. Regularized Sliced Inverse Regression delivers the most parsimonious forecast model and obtains the greatest reduction of the complexity of the forecasting problem. Nevertheless, the study’s findings are that (a) the intrinsic relationship between forecast target and the other macroseries in the panel is linear and (b) targeting contributes in reducing the complexity of modeling yet does not induce significant gains in macroeconomic forecasting accuracy.</p></div>","PeriodicalId":54125,"journal":{"name":"Econometrics and Statistics","volume":"31 ","pages":"Pages 1-18"},"PeriodicalIF":2.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80948953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bias correction for Vandermonde low-rank approximation 范德蒙德低秩近似的偏差修正

IF 2

Econometrics and Statistics Pub Date : 2024-07-01 DOI: 10.1016/j.ecosta.2021.09.001

Antonio Fazzi , Alexander Kukush , Ivan Markovsky

引用次数: 0

Multivariate Count Time Series Modelling 多变量计数时间序列建模

IF 2

Econometrics and Statistics Pub Date : 2024-07-01 DOI: 10.1016/j.ecosta.2021.11.006

Konstantinos Fokianos

引用次数: 0