ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)最新文献_第3页

Limited Dynamic Forecasting of Hidden Markov Models 隐马尔可夫模型的有限动态预测

ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic) Pub Date : 2018-07-01 DOI: 10.2139/ssrn.3206425

A. Gopalakrishnan, Eric T. Bradlow, P. Fader

{"title":"Limited Dynamic Forecasting of Hidden Markov Models","authors":"A. Gopalakrishnan, Eric T. Bradlow, P. Fader","doi":"10.2139/ssrn.3206425","DOIUrl":"https://doi.org/10.2139/ssrn.3206425","url":null,"abstract":"Hidden Markov Models (HMMs) have emerged as an empirical “workhorse” in the marketing literature in capturing and forecasting within-customer non-stationary behaviors. Extant research has demonstrated that HMMs typically outperform nested benchmarks when examining fit statistics aggregated over individuals and time, but have remained largely silent on the set of dynamic out-of-sample forecasting paths offered by an HMM at the individual level. We examine the capabilities of a two-state HMM using theory and reveal a surprising result: an HMM’s forecasting paths are generally limited to monotonic mean-reverting trajectories. Specifically, they lack the notable flexibility associated with the in-sample state-switching imputations, which are generally (but, as we show, erroneously) presumed to exist in the holdout sample as well. Further, we find that common HMM extensions such as adding more hidden states, allowing for heterogeneity, allowing for covariates, and using hidden semi-Markov models do not alleviate the limited forecasting flexibility. Using a simulation design, we show how these limitations can affect forecasting performance empirically. We discuss implications of the limited forecasting properties of HMMs for researchers and managers.","PeriodicalId":308524,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128190845","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

A Paradigm for Assessing the Scope and Performance of Predictive Analytics 评估预测分析的范围和性能的范例

ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic) Pub Date : 2018-06-20 DOI: 10.2139/ssrn.3199961

Jeffrey T. Prince

引用次数: 1

Forecasting Bitcoin Risk Measures: A Robust Approach 预测比特币风险措施:一种稳健的方法

ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic) Pub Date : 2018-06-02 DOI: 10.2139/ssrn.3189446

Carlos Trucíos

引用次数: 73

Nonparametric Forecasting of Multivariate Probability Density Functions 多元概率密度函数的非参数预测

ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic) Pub Date : 2018-03-19 DOI: 10.2139/ssrn.3192342

D. Guégan, Matteo Iacopini

{"title":"Nonparametric Forecasting of Multivariate Probability Density Functions","authors":"D. Guégan, Matteo Iacopini","doi":"10.2139/ssrn.3192342","DOIUrl":"https://doi.org/10.2139/ssrn.3192342","url":null,"abstract":"The study of dependence between random variables is the core of theoretical and applied statistics. Static and dynamic copula models are useful for describing the dependence structure, which is fully encrypted in the copula probability density function. However, these models are not always able to describe the temporal change of the dependence patterns, which is a key characteristic of financial data. We propose a novel nonparametric framework for modelling a time series of copula probability density functions, which allows to forecast the entire function without the need of post-processing procedures to grant positiveness and unit integral. We exploit a suitable isometry that allows to transfer the analysis in a subset of the space of square integrable functions, where we build on nonparametric functional data analysis techniques to perform the analysis. The framework does not assume the densities to belong to any parametric family and it can be successfully applied also to general multivariate probability density functions with bounded or unbounded support. Finally, a noteworthy field of application pertains the study of time varying networks represented through vine copula models. We apply the proposed methodology for estimating and forecasting the time varying dependence structure between the S&P500 and NASDAQ indices.","PeriodicalId":308524,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)","volume":"338 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134228711","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

Forecasting with Bayesian Vector Autoregressions with Time Variation in the Mean 均值随时间变化的贝叶斯向量自回归预测

ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic) Pub Date : 2018-03-08 DOI: 10.2139/ssrn.3145055

Marta Bańbura, Andries van Vlodrop

引用次数: 18

DSGE Forecasts of the Lost Recovery DSGE对失去的复苏的预测

ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic) Pub Date : 2018-03-01 DOI: 10.2139/ssrn.3138589

Michael D Cai, Marco Del Negro, M. Giannoni, Abhijit Sen Gupta, Pearl Li, Erica Moszkowski

引用次数: 24

Forecasting Calendar Futures Spreads of Crude Oil Using Kalman Filter 利用卡尔曼滤波预测原油日历期货价差

ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic) Pub Date : 2017-10-17 DOI: 10.2139/ssrn.3405998

Xu Ren, G. Mitra, Zryan A Sadik

{"title":"Forecasting Calendar Futures Spreads of Crude Oil Using Kalman Filter","authors":"Xu Ren, G. Mitra, Zryan A Sadik","doi":"10.2139/ssrn.3405998","DOIUrl":"https://doi.org/10.2139/ssrn.3405998","url":null,"abstract":"The aim of this project is to forecast futures spreads of WTI Crude Oil. The motivation for this project springs from the fact that trading with calendar futures spreads is much more advantageous than trading with many other financial instruments. We make use of the fact that futures prices follow the mean-reverting process (Ornstein-Uhlenbeck process, OU). We develop a method, which was first proposed by Islyaev (2014) and the approach then extended by Sadik et al. (2020), that combines three linear Gaussian state space models, namely one factor model, one factor model with risk premium, and one factor model with seasonality. Thereafter, we directly model futures spreads. Kalman filter and the Maximum Likelihood Estimate (MLE) are used to estimate the model parameters. It is shown that this new approach, using the ratio between the nearest prices over spot prices as a latent variable and calendar futures spreads vector as the observed variable, is more accurate and robust than the indirect forecasting method which inputs both spot prices and futures prices as the latent variable and the observed variable respectively. Results on calibration and comparison for three models and two methods, as well as out-of-sample forecasting results are then presented and discussed.","PeriodicalId":308524,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133609666","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

What Do Professional Forecasters Actually Predict? 专业预测师到底预测了什么?

ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic) Pub Date : 2017-10-13 DOI: 10.2139/ssrn.2640878

D. Nibbering, R. Paap, Michel van der Wel

引用次数: 0

Predicting Conflict Events in Africa at Subnational Level 非洲次国家层面的冲突事件预测

ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic) Pub Date : 2017-08-16 DOI: 10.2139/ssrn.3019940

Stijn van Weezel

{"title":"Predicting Conflict Events in Africa at Subnational Level","authors":"Stijn van Weezel","doi":"10.2139/ssrn.3019940","DOIUrl":"https://doi.org/10.2139/ssrn.3019940","url":null,"abstract":"This study reviews the contribution in predictive accuracy of a number of geographic and socio-economic factors that are commonly linked to conflict incidence. A logit model is fitted to sub-national data for Africa at grid-cell level covering the years 2000-2009, generating an out-of-sample forecast for the period 2010-2015. Results show that the strongest predictor of future conflict is current conflict incidence in the grid-cell and neighbouring cells. Additionally, the infant mortality rate, which serves as a proxy for socio-economic well-being, shows some prowess in contributing to accurate predictions. This in contrast with factors such as the share of mountainous terrain. Travel time to the nearest city, to proxy for urban-rural differences, is also a strong predictor, but it must be noted that this could be the result of reporting bias in the outcome variable. In general the results highlight that it is difficult to improve accuracy beyond the contribution of conflict dynamics. Finally, the presented results are based on a relatively simple regression model commonly used in the literature and more sophisticated statistical techniques such as machine learning could improve predictions.","PeriodicalId":308524,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)","volume":"354 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115925693","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: 3

Structural Scenario Analysis with SVARs 结构情景分析与svar

ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic) Pub Date : 2017-08-01 DOI: 10.2139/ssrn.3669856

Juan Antolín-Díaz, Ivan Petrella, J. Rubio-Ramirez

引用次数: 38