Econometrics最新文献_第9页

Algorithmic Modelling of Financial Conditions for Macro Predictive Purposes: Pilot Application to USA Data 用于宏观预测目的的金融状况算法建模:美国数据的试点应用

IF 1.5

Econometrics Pub Date : 2022-04-19 DOI: 10.3390/econometrics10020022

D. Qin, Sophie van Huellen, Qing Chao Wang, Thanos Moraitis

引用次数: 1

A Conversation with Søren Johansen 与瑟伦·约翰森的对话

IF 1.5

Econometrics Pub Date : 2022-04-13 DOI: 10.3390/econometrics10020021

R. Mosconi, P. Paruolo

引用次数: 1

A Conversation with Katarina Juselius 与卡特琳娜·尤塞利乌斯的对话

IF 1.5

Econometrics Pub Date : 2022-04-13 DOI: 10.3390/econometrics10020020

R. Mosconi, P. Paruolo

引用次数: 3

Using the SARIMA Model to Forecast the Fourth Global Wave of Cumulative Deaths from COVID-19: Evidence from 12 Hard-Hit Big Countries 使用SARIMA模型预测COVID-19全球第四次累积死亡浪潮:来自12个重灾区大国的证据

IF 1.5

Econometrics Pub Date : 2022-04-09 DOI: 10.3390/econometrics10020018

Gaetano Perone

{"title":"Using the SARIMA Model to Forecast the Fourth Global Wave of Cumulative Deaths from COVID-19: Evidence from 12 Hard-Hit Big Countries","authors":"Gaetano Perone","doi":"10.3390/econometrics10020018","DOIUrl":"https://doi.org/10.3390/econometrics10020018","url":null,"abstract":"The COVID-19 pandemic is a serious threat to all of us. It has caused an unprecedented shock to the world’s economy, and it has interrupted the lives and livelihood of millions of people. In the last two years, a large body of literature has attempted to forecast the main dimensions of the COVID-19 outbreak using a wide set of models. In this paper, I forecast the short- to mid-term cumulative deaths from COVID-19 in 12 hard-hit big countries around the world as of 20 August 2021. The data used in the analysis were extracted from the Our World in Data COVID-19 dataset. Both non-seasonal and seasonal autoregressive integrated moving averages (ARIMA and SARIMA) were estimated. The analysis showed that: (i) ARIMA/SARIMA forecasts were sufficiently accurate in both the training and test set by always outperforming the simple alternative forecasting techniques chosen as benchmarks (Mean, Naïve, and Seasonal Naïve); (ii) SARIMA models outperformed ARIMA models in 47 out 48 metrics (in forecasting future values), i.e., on 97.9% of all the considered forecast accuracy measures (mean absolute error [MAE], mean absolute percentage error [MAPE], mean absolute scaled error [MASE], and the root mean squared error [RMSE]), suggesting a clear seasonal pattern in the data; and (iii) the forecasted values from SARIMA models fitted very well the observed (real-time) data for the period 21 August 2021–19 September 2021 for almost all the countries analyzed. This article shows that SARIMA can be safely used for both the short- and medium-term predictions of COVID-19 deaths. Thus, this approach can help government authorities to monitor and manage the huge pressure that COVID-19 is exerting on national healthcare systems.","PeriodicalId":11499,"journal":{"name":"Econometrics","volume":" ","pages":""},"PeriodicalIF":1.5,"publicationDate":"2022-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42378091","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: 7

Model Validation and DSGE Modeling 模型验证和DSGE建模

IF 1.5

Econometrics Pub Date : 2022-04-07 DOI: 10.3390/econometrics10020017

Niraj Poudyal, A. Spanos

引用次数: 0

A Theory-Consistent CVAR Scenario for a Monetary Model with Forward-Looking Expectations 具有前瞻性预期的货币模型的理论一致CVAR情景

IF 1.5

Econometrics Pub Date : 2022-04-06 DOI: 10.3390/econometrics10020016

K. Juselius

引用次数: 3

Learning Forecast-Efficient Yield Curve Factor Decompositions with Neural Networks 基于神经网络的学习预测高效产量曲线因子分解

IF 1.5

Econometrics Pub Date : 2022-03-25 DOI: 10.3390/econometrics10020015

Piero C. Kauffmann, H. H. Takada, Ana T. Terada, Julio Michael Stern

引用次数: 0

A Binary Choice Model with Sample Selection and Covariate-Related Misclassification 具有样本选择和协变量相关错分类的二元选择模型

IF 1.5

Econometrics Pub Date : 2022-03-23 DOI: 10.3390/econometrics10020013

Jorge González Chapela

引用次数: 0

Missing Values in Panel Data Unit Root Tests 面板数据单元根测试中缺少值

IF 1.5

Econometrics Pub Date : 2022-03-16 DOI: 10.3390/econometrics10010012

Yiannis Karavias, Elias Tzavalis, Haotian Zhang

引用次数: 3

Green Bonds for the Transition to a Low-Carbon Economy 绿色债券助力低碳经济转型

IF 1.5

Econometrics Pub Date : 2022-03-02 DOI: 10.3390/econometrics10010011

Andreas Lichtenberger, Joao Paulo Braga, W. Semmler

{"title":"Green Bonds for the Transition to a Low-Carbon Economy","authors":"Andreas Lichtenberger, Joao Paulo Braga, W. Semmler","doi":"10.3390/econometrics10010011","DOIUrl":"https://doi.org/10.3390/econometrics10010011","url":null,"abstract":"The green bond market is emerging as an impactful financing mechanism in climate change mitigation efforts. The effectiveness of the financial market for this transition to a low-carbon economy depends on attracting investors and removing financial market roadblocks. This paper investigates the differential bond performance of green vs non-green bonds with (1) a dynamic portfolio model that integrates negative as well as positive externality effects and via (2) econometric analyses of aggregate green bond and corporate energy time-series indices; as well as a cross-sectional set of individual bonds issued between 1 January 2017, and 1 October 2020. The asset pricing model demonstrates that, in the long-run, the positive externalities of green bonds benefit the economy through positive social returns. We use a deterministic and a stochastic version of the dynamic portfolio approach to obtain model-driven results and evaluate those through our empirical evidence using harmonic estimations. The econometric analysis of this study focuses on volatility and the risk–return performance (Sharpe ratio) of green and non-green bonds, and extends recent econometric studies that focused on yield differentials of green and non-green bonds. A modified Sharpe ratio analysis, cross-sectional methods, harmonic estimations, bond pairing estimations, as well as regression tree methodology, indicate that green bonds tend to show lower volatility and deliver superior Sharpe ratios (while the evidence for green premia is mixed). As a result, green bond investment can protect investors and portfolios from oil price and business cycle fluctuations, and stabilize portfolio returns and volatility. Policymakers are encouraged to make use of the financial benefits of green instruments and increase the financial flows towards sustainable economic activities to accelerate a low-carbon transition.","PeriodicalId":11499,"journal":{"name":"Econometrics","volume":" ","pages":""},"PeriodicalIF":1.5,"publicationDate":"2022-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41539233","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: 8