ERN: Time-Series Models (Single) (Topic)最新文献_第2页

On Policy Evaluation with Aggregate Time-Series Shocks 考虑累计时间序列冲击的政策评估

ERN: Time-Series Models (Single) (Topic) Pub Date : 2019-05-31 DOI: 10.2139/ssrn.3619495

D. Arkhangelsky, V. Korovkin

引用次数: 4

Time Series Model Comparisons 时间序列模型比较

ERN: Time-Series Models (Single) (Topic) Pub Date : 2019-05-02 DOI: 10.2139/ssrn.3398018

Sahil Puri

引用次数: 0

Time Series Simulation by Conditional Generative Adversarial Net 条件生成对抗网络的时间序列仿真

ERN: Time-Series Models (Single) (Topic) Pub Date : 2019-04-17 DOI: 10.2139/ssrn.3373730

Rao Fu, Jie Chen, Shutian Zeng, Yiping Zhuang, A. Sudjianto

{"title":"Time Series Simulation by Conditional Generative Adversarial Net","authors":"Rao Fu, Jie Chen, Shutian Zeng, Yiping Zhuang, A. Sudjianto","doi":"10.2139/ssrn.3373730","DOIUrl":"https://doi.org/10.2139/ssrn.3373730","url":null,"abstract":"Generative Adversarial Net (GAN) has proved to be a powerful machine learning tool in image data analysis and generation. In this paper, we propose to use Conditional Generative Adversarial Net (CGAN) to learn and simulate time series data. The conditions include both categorical and continuous variables with different auxiliary information. Our simulation studies show that CGAN has the capability to learn different types of normal and heavy-tailed distributions, as well as dependent structures of different time series. It also has the capability to generate conditional predictive distributions consistent with training data distributions. We also provide an in-depth discussion on the rationale behind GAN and the neural networks as hierarchical splines to establish a clear connection with existing statistical methods of distribution generation. In practice, CGAN has a wide range of applications in market risk and counterparty risk analysis: it can be applied to learn historical data and generate scenarios for the calculation of Value-at-Risk (VaR) and Expected Shortfall (ES), and it can also predict the movement of the market risk factors. We present a real data analysis including a backtesting to demonstrate that CGAN can outperform Historical Simulation (HS), a popular method in market risk analysis to calculate VaR. CGAN can also be applied in economic time series modeling and forecasting. In this regard, we have included an example of hypothetical shock analysis for economic models and the generation of potential CCAR scenarios by CGAN at the end of the paper","PeriodicalId":418701,"journal":{"name":"ERN: Time-Series Models (Single) (Topic)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116857739","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: 40

Volatility Persistence and Asymmetry Under the Microscope: The Role of Information Demand for Gold and Oil 显微镜下的波动性、持久性和不对称性:黄金和石油信息需求的作用

ERN: Time-Series Models (Single) (Topic) Pub Date : 2019-02-01 DOI: 10.1111/sjpe.12177

Georgios Bampinas, Christina Rouska

引用次数: 9

A Time-Varying Parameter Model for Local Explosions 局部爆炸的时变参数模型

ERN: Time-Series Models (Single) (Topic) Pub Date : 2018-11-16 DOI: 10.2139/ssrn.3285850

F. Blasques, S. J. Koopman, Marc Nientker

引用次数: 6

Measuring the Impact of Financial Bailouts: An Interrupted Time Series Approach 衡量金融救助的影响:一个中断的时间序列方法

ERN: Time-Series Models (Single) (Topic) Pub Date : 2018-08-09 DOI: 10.2139/ssrn.3229002

J. Owusu

引用次数: 0

A Cointegration Model of Money and Wealth 货币与财富的协整模型

ERN: Time-Series Models (Single) (Topic) Pub Date : 2018-08-07 DOI: 10.2139/ssrn.3339569

Katrin Assenmacher, A. Beyer

引用次数: 3

Trajectory Balancing: A General Reweighting Approach to Causal Inference With Time-Series Cross-Sectional Data 轨迹平衡:时间序列横截面数据因果推理的一般重加权方法

ERN: Time-Series Models (Single) (Topic) Pub Date : 2018-08-05 DOI: 10.2139/ssrn.3214231

C. Hazlett, Yiqing Xu

{"title":"Trajectory Balancing: A General Reweighting Approach to Causal Inference With Time-Series Cross-Sectional Data","authors":"C. Hazlett, Yiqing Xu","doi":"10.2139/ssrn.3214231","DOIUrl":"https://doi.org/10.2139/ssrn.3214231","url":null,"abstract":"We introduce trajectory balancing, a general reweighting approach to causal inference with time-series cross-sectional (TSCS) data. We focus on settings in which one or more units is exposed to treatment at a given time, while a set of control units remain untreated throughout a time window of interest. First, we show that many commonly used TSCS methods imply an assumption that a unit's non-treatment potential outcomes in the post-treatment period are linear in that unit's pre-treatment outcomes as well as time-invariant covariates. Under this assumption, we introduce the mean balancing method that reweights the control units such that the averages of the pre-treatment outcomes and covariates are approximately equal between the treatment and (reweighted) control groups. Second, we relax the linearity assumption and propose the kernel balancing method that seeks an approximate balance on a kernel-based feature expansion of the pre-treatment outcomes and covariates. The resulting approach inherits the property of handling time-vary confounders as in synthetic control and latent factor models, but has the advantages of: (1) improving feasibility and stability with reduced user discretion compared to existing approaches; (2) accommodating both short and long pre-treatment time periods with many or few treated units; and (3) achieving balance on the high-order \"trajectory\" of pre-treatment outcomes rather than their simple average at each time period. We illustrate this method with simulations and two empirical examples.","PeriodicalId":418701,"journal":{"name":"ERN: Time-Series Models (Single) (Topic)","volume":"07 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127139931","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: 47

Unobserved Components with Stochastic Volatility in U.S. Inflation: Estimation and Signal Extraction 美国通货膨胀随机波动的不可观测成分:估计和信号提取

ERN: Time-Series Models (Single) (Topic) Pub Date : 2018-03-21 DOI: 10.2139/ssrn.3145075

Mengheng Li, S. J. Koopman

{"title":"Unobserved Components with Stochastic Volatility in U.S. Inflation: Estimation and Signal Extraction","authors":"Mengheng Li, S. J. Koopman","doi":"10.2139/ssrn.3145075","DOIUrl":"https://doi.org/10.2139/ssrn.3145075","url":null,"abstract":"We consider unobserved components time series models where the components are stochastically evolving over time and are subject to stochastic volatility. It enables the disentanglement of dynamic structures in both the mean and the variance of the observed time series. We develop a simulated maximum likelihood estimation method based on importance sampling and assess its performance in a Monte Carlo study. This modelling framework with trend, seasonal and irregular components is applied to quarterly and monthly US inflation in an empirical study. We find that the persistence of quarterly inflation has increased during the 2008 financial crisis while it has recently returned to its pre-crisis level. The extracted volatility pattern for the trend component can be associated with the energy shocks in the 1970s while that for the irregular component responds to the monetary regime changes from the 1980s. The scale of the changes in the seasonal component has been largest during the beginning of the 1990s. We finally present empirical evidence of relative improvements in the accuracies of point and density forecasts for monthly US inflation.","PeriodicalId":418701,"journal":{"name":"ERN: Time-Series Models (Single) (Topic)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131695751","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: 4

The Effect of Macroeconomic Announcements on Credit Markets: An Autometric General-to-Specific Analysis of the Greenspan Era 宏观经济公告对信贷市场的影响:格林斯潘时代的一般到具体的自动计量分析

ERN: Time-Series Models (Single) (Topic) Pub Date : 2018-02-02 DOI: 10.2139/ssrn.3117090

J. Forest

{"title":"The Effect of Macroeconomic Announcements on Credit Markets: An Autometric General-to-Specific Analysis of the Greenspan Era","authors":"J. Forest","doi":"10.2139/ssrn.3117090","DOIUrl":"https://doi.org/10.2139/ssrn.3117090","url":null,"abstract":"I show that a congruent, parsimonious, encompassing model discovered using David Hendry’s econometric modelling approach and Autometrics can overcome the many inadequacies of the typical static models of US Treasury returns regressed on macroeconomic announcements. The typical specification tends to fail most, if not all, specification tests. Further, the techniques employed are able to expand our knowledge of time varying risk premia and asymmetric news responses in financial markets. Previously studied within a GARCH framework, such methods offered little evidence as to the precise sources of the asymmetries. Asymmetric effects are shown to be concentrated in a handful of announcements, such as the Employment Cost Index and Core PPI. Results suggest a place for general-to-specific modelling in financial economics, a place where it has only recently begun to be employed. These results underscore the contributions of David F. Hendry and his collaborators in econometric modelling, they also and demonstrate the need for better models in finance that may be alleviated by employing modelling practices advocated by econometricians doing research in the LSE/Oxford tradition.","PeriodicalId":418701,"journal":{"name":"ERN: Time-Series Models (Single) (Topic)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129145990","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