ERN: Time-Series Models (Multiple) (Topic)最新文献

The Multifaceted Impact of US Trade Policy on Financial Markets 美国贸易政策对金融市场的多方面影响

ERN: Time-Series Models (Multiple) (Topic) Pub Date : 2021-07-01 DOI: 10.2139/ssrn.3895690

L. Boer, Lukas Menkhoff, Malte Rieth

引用次数: 0

A note on spurious regression and random walks with zero, local, or constant drifts 关于伪回归和零漂移、局部漂移或常数漂移的随机游走的说明

ERN: Time-Series Models (Multiple) (Topic) Pub Date : 2020-11-10 DOI: 10.2139/ssrn.3381194

Jay Dennis, Kaiji Motegi

引用次数: 0

A Consumption-Based Identification of Global Economic Uncertainty 基于消费的全球经济不确定性识别

ERN: Time-Series Models (Multiple) (Topic) Pub Date : 2020-08-20 DOI: 10.2139/ssrn.3678136

Hwagyun Kim, Eunhee Lee, Joon Y. Park

引用次数: 1

Joint Bayesian Inference about Impulse Responses in VAR Models VAR模型中脉冲响应的联合贝叶斯推断

ERN: Time-Series Models (Multiple) (Topic) Pub Date : 2020-07-01 DOI: 10.2139/ssrn.3759335

A. Inoue, L. Kilian

{"title":"Joint Bayesian Inference about Impulse Responses in VAR Models","authors":"A. Inoue, L. Kilian","doi":"10.2139/ssrn.3759335","DOIUrl":"https://doi.org/10.2139/ssrn.3759335","url":null,"abstract":"Structural VAR models are routinely estimated by Bayesian methods. Several recent studies have voiced concerns about the common use of posterior median (or mean) response functions in applied VAR analysis. In this paper, we show that these response functions can be misleading because in empirically relevant settings there need not exist a posterior draw for the impulse response function that matches the posterior median or mean response function, even as the number of posterior draws approaches infinity. As a result, the use of these summary statistics may distort the shape of the impulse response function which is of foremost interest in applied work. The same concern applies to error bands based on the upper and lower quantiles of the marginal posterior distributions of the impulse responses. In addition, these error bands fail to capture the full uncertainty about the estimates of the structural impulse responses. In response to these concerns, we propose new estimators of impulse response functions under quadratic loss, under absolute loss and under Dirac delta loss that are consistent with Bayesian statistical decision theory, that are optimal in the relevant sense, that respect the dynamics of the impulse response functions and that are easy to implement. We also propose joint credible sets for these estimators derived under the same loss function. Our analysis covers a much wider range of structural VAR models than previous proposals in the literature including models that combine short-run and long-run exclusion restrictions and models that combine zero restrictions, sign restrictions and narrative restrictions.","PeriodicalId":191102,"journal":{"name":"ERN: Time-Series Models (Multiple) (Topic)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124039816","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: 34

New Approaches to Robust Inference on Market (Non-)Efficiency, Volatility Clustering and Nonlinear Dependence 基于市场(非)效率、波动聚类和非线性依赖的鲁棒推断新方法

ERN: Time-Series Models (Multiple) (Topic) Pub Date : 2020-06-01 DOI: 10.2139/ssrn.3580916

A. Skrobotov, R. Pedersen, R. Ibragimov

{"title":"New Approaches to Robust Inference on Market (Non-)Efficiency, Volatility Clustering and Nonlinear Dependence","authors":"A. Skrobotov, R. Pedersen, R. Ibragimov","doi":"10.2139/ssrn.3580916","DOIUrl":"https://doi.org/10.2139/ssrn.3580916","url":null,"abstract":"Many key variables in finance, economics and risk management, including financial returns and foreign exchange rates, exhibit nonlinear dependence, heterogeneity and heavy-tailedness of some usually largely unknown type. The presence of non-linear dependence (usually modelled using GARCH-type dynamics) and heavy-tailedness may make problematic the analysis of (non-)efficiency, volatility clustering and predictive regressions in economic and financial markets using traditional approaches that appeal to asymptotic normality of sample autocorrelation functions (ACFs) of returns and their squares. The paper presents several new approaches to deal with the above problems. We provide the results that motivate the use of measures of market (non-)efficiency, volatility clustering and nonlinear dependence based on (small) powers of absolute returns and their signed versions. The paper provides asymptotic theory for sample analogues of the above measures in the case of general time series, including GARCH-type processes. It further develops new approaches to robust inference on them in the case of general GARCH-type processes exhibiting heavy-tailedness properties. The approaches are based on robust inference methods exploiting conservativeness properties of t-statistics Ibragimov and Muller (2010,2016) and several new results on their applicability in the settings considered. In the approaches, estimates of parameters of interest are computed for groups of data and the inference is based on t-statistics in resulting group estimates. This results in valid robust inference under a wide range of heterogeneity and dependence assumptions satisfied in financial and economic markets. Numerical results and empirical applications confirm advantages of the new approaches over existing ones and their wide applicability.","PeriodicalId":191102,"journal":{"name":"ERN: Time-Series Models (Multiple) (Topic)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117113172","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: 6

Heteroskedastic Proxy Vector Autoregressions 异方差代理向量自回归

ERN: Time-Series Models (Multiple) (Topic) Pub Date : 2020-06-01 DOI: 10.2139/ssrn.3634441

H. Lütkepohl, Thore Schlaak

引用次数: 3

Analyzing Differences between Scenarios 分析不同场景的差异

ERN: Time-Series Models (Multiple) (Topic) Pub Date : 2020-04-21 DOI: 10.2139/ssrn.3581855

D. Hendry, F. Pretis

{"title":"Analyzing Differences between Scenarios","authors":"D. Hendry, F. Pretis","doi":"10.2139/ssrn.3581855","DOIUrl":"https://doi.org/10.2139/ssrn.3581855","url":null,"abstract":"Comparisons between alternative scenarios are used in many disciplines from macroeconomics to climate science to help with planning future responses. Differences between scenario paths are often interpreted as signifying likely differences between outcomes that would materialise in reality. However, even when using correctly specified statistical models of the in-sample data generation process, additional conditions are needed to sustain inferences about differences between scenario paths. We consider two questions in scenario analyses: First, does testing the difference between scenarios yield additional insight beyond simple tests conducted on the model estimated in-sample? Second, when does the estimated scenario difference yield unbiased estimates of the true difference in outcomes? Answering the first question, we show that the calculation of uncertainties around scenario differences raises difficult issues since the underlying in-sample distributions are identical for both ‘potential’ outcomes when the reported paths are deterministic functions. Under these circumstances, a scenario comparison adds little beyond testing for the significance of the perturbed variable in the estimated model. Resolving the second question, when models include multiple covariates, inferences about scenario differences depend on the relationships between the conditioning variables, especially their invariance to the interventions. Tests for invariance based on automatic detection of structural breaks can help identify in-sample invariance of models to evaluate likely constancy in projected scenarios. Applications of scenario analyses to impacts on the UK’s wage share from unemployment and agricultural growth from climate change illustrate the concepts.","PeriodicalId":191102,"journal":{"name":"ERN: Time-Series Models (Multiple) (Topic)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125618112","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

Automatic Time Series Modelling and Forecasting: A Replication Case Study of Forecasting Real GDP, the Unemployment Rate, and the Impact of Leading Economic Indicators 自动时间序列建模和预测:预测实际GDP、失业率和领先经济指标影响的复制案例研究

ERN: Time-Series Models (Multiple) (Topic) Pub Date : 2020-04-13 DOI: 10.2139/ssrn.3577323

J. Guerard, D. Thomakos, Foteinh Kyriazh

{"title":"Automatic Time Series Modelling and Forecasting: A Replication Case Study of Forecasting Real GDP, the Unemployment Rate, and the Impact of Leading Economic Indicators","authors":"J. Guerard, D. Thomakos, Foteinh Kyriazh","doi":"10.2139/ssrn.3577323","DOIUrl":"https://doi.org/10.2139/ssrn.3577323","url":null,"abstract":"We test and report on time series modelling and forecasting using several U.S. Leading Economic Indicators (LEI) as an input to forecasting real U.S. GDP and the unemployment rate. These time series have been addressed before, but our results are more statistically significant using more recently developed time series modelling techniques and software. Montgomery, Zarnowitz, Tsay, and Tiao (1998) modeled the U.S. unemployment rate as a function of the weekly unemployment claims time series, 1948 – 1992. In this replication case study, we apply the Hendry and Doornik automatic time series PC-Give (AutoMetrics) methodology to the well-studied macroeconomics series, U.S. real GDP and the unemployment rate. The Autometrics system substantially reduces regression sum of squares measures relative to traditional variations on the random walk with drift model. The LEI are a statistically significant input to real GDP. A similar conclusion is found for the impact of the LEI and weekly unemployment claims series leading the unemployment rate series. We tested the forecasting ability of best univariate and best bivariate models over 60- and 120-period rolling windows and report considerable forecast error reductions. The adaptive averaging autoregressive model forecast ADA-AR and the adaptive learning forecast, ADL, produced the smallest root mean square errors and lowest mean absolute errors.","PeriodicalId":191102,"journal":{"name":"ERN: Time-Series Models (Multiple) (Topic)","volume":"153 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126108641","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: 1

Local Projections, Autocorrelation, and Efficiency 局部投影、自相关和效率

ERN: Time-Series Models (Multiple) (Topic) Pub Date : 2020-03-29 DOI: 10.2139/ssrn.3563697

Amaze Lusompa

引用次数: 13

Forecasting Natural Gas Prices Using Highly Flexible Time-Varying Parameter Models 利用高度灵活的时变参数模型预测天然气价格

ERN: Time-Series Models (Multiple) (Topic) Pub Date : 2020-03-26 DOI: 10.2139/ssrn.3562074

Shen Gao, Chenghan Hou, B. Nguyen

引用次数: 9