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

{"title":"Estimating and Testing for Multiple Distributional Structural Breaks via a Characteristic Function Approach","authors":"Zhonghao Fu, Yongmiao Hong, Xia Wang","doi":"10.2139/ssrn.3559941","DOIUrl":"https://doi.org/10.2139/ssrn.3559941","url":null,"abstract":"We estimate and test for multiple structural breaks in distribution with unknown break dates via a characteristic function approach. By minimizing the sum of squared generalized residuals, we can consistently estimate the break fractions. We propose a sup-F type test for structural breaks in distribution and suggest an information criterion and a sequential testing procedure to determine the number of breaks. We further construct a class of derivative tests to gauge the possible source of structural breaks, which is asymptotically more powerful than the nonparametric-based tests for structural breaks. Simulation studies show that our method performs well in determining the appropriate number of breaks and estimating the unknown breaks. Furthermore, the proposed tests have reasonable size and excellent power. In an application to exchange rate returns, our tests are able to detect structural breaks in distribution and locate the break dates. Our derivate tests indicate that the documented breaks appear to occur in variance and higher order moments, but not so often in mean.","PeriodicalId":191102,"journal":{"name":"ERN: Time-Series Models (Multiple) (Topic)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125390983","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}

{"title":"Non‐Linearity and Cross‐Country Dependence of Income Inequality","authors":"Leena Kalliovirta, Tuomas Malinen","doi":"10.1111/roiw.12377","DOIUrl":"https://doi.org/10.1111/roiw.12377","url":null,"abstract":"We use top income data and the newly developed regime switching Gaussian mixture vector autoregressive model to explain the dynamics of income inequality in developed economies within the last 100 years. Our results indicate that the process of income inequality consists of two equilibriums identifiable by high inequality, high income fluctuations and low inequality, low income fluctuations. Our results also show that income inequality in the U.S. is the driver of income inequality in other developed economies. Both economic and institutional changes emanating from the U.S. explain this dominance.","PeriodicalId":191102,"journal":{"name":"ERN: Time-Series Models (Multiple) (Topic)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121657233","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}

{"title":"Development of a Method for Forecasting Random Events during Instability Periods","authors":"S. Petrovska","doi":"10.15587/2312-8372.2020.198436","DOIUrl":"https://doi.org/10.15587/2312-8372.2020.198436","url":null,"abstract":"The object of research is random events in the formation of new economic and financial models; in particular; with cardinal changes in economic and social strategies. The scope and variety of methods used in the prediction of random processes is large. Promising mathematical apparatus for solving the problem are statistical methods of analysis. Today; there are many methods for predicting random processes; but most existing models are not suitable for predicting non-stationary processes. One of the most problematic places in forecasting time series is that there is no single methodology by which to analyze the characteristics of a non-stationary random process. Therefore; it is necessary to develop special methods of analysis that can be applied to individual cases of unsteady processes. The optimal solution to the problem may be the approximation of the time series by finely rational functions or the so-called Padé approximation. Such an approach should take advantage of polynomial approximation. In polynomial approximation; polynomial can’t have horizontal asymptotes; which makes it impossible to make long-term forecasts. A rational approximation is guaranteed to tend to horizontal asymptotes; with all the poles of the finely rational function lying on the left side of the p-plane; that is; the Laplace transform plane. A method for predicting non-stationary time series with high accuracy of estimation and flexibility of settings is proposed. To ensure the stability of the method and the stability of the obtained results; it is proposed that the poles of the approximating function be introduced into the stability zone – the unit circle of the z-plane in compliance with the rules of conformal transformation. Namely; by transforming linear dimensions and preserving the angles between the orthogonal coordinates on infinitely small neighborhoods of the coordinate plane (the so-called conservatism of angles). It is shown that; subject to the conformity of the proposed transformation; the dynamic characteristics of the estimation and forecasting system are stored. This method can be especially successfully applied in the presence of non-stationarity of various natures.","PeriodicalId":191102,"journal":{"name":"ERN: Time-Series Models (Multiple) (Topic)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124898981","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}

{"title":"From Fixed-Event to Fixed-Horizon Density Forecasts: Obtaining Measures of Multi-Horizon Uncertainty from Survey Density Forecasts","authors":"G. Gánics, B. Rossi, Tatevik Sekhposyan","doi":"10.2139/ssrn.3507117","DOIUrl":"https://doi.org/10.2139/ssrn.3507117","url":null,"abstract":"Surveys of professional forecasters produce precise and timely point forecasts for key macroeconomic variables. However, the accompanying density forecasts are not as widely utilized, and there is no consensus about their quality. This is partly because such surveys are often conducted for “fixed events”. For example, in each quarter panelists are asked to forecast output growth and inflation for the current calendar year and the next, implying that the forecast horizon changes with each survey round. The fixed-event nature limits the usefulness of survey density predictions for policymakers and market participants, who often wish to characterize uncertainty a fixed number of periods ahead (“fixed-horizon”). Is it possible to obtain fixed-horizon density forecasts using the available fixed-event ones? We propose a density combination approach that weights fixed-event density forecasts according to a uniformity of the probability integral transform criterion, aiming at obtaining a correctly calibrated fixed-horizon density forecast. Using data from the US Survey of Professional Forecasters, we show that our combination method produces competitive density forecasts relative to widely used alternatives based on historical forecast errors or Bayesian VARs. Thus, our proposed fixed-horizon predictive densities are a new and useful tool for researchers and policy makers.","PeriodicalId":191102,"journal":{"name":"ERN: Time-Series Models (Multiple) (Topic)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126150561","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}

{"title":"Turning Point and Oscillatory Cycles","authors":"Mariano Kulish, A. Pagan","doi":"10.2139/ssrn.3459301","DOIUrl":"https://doi.org/10.2139/ssrn.3459301","url":null,"abstract":"The early history of cycles research involved locating turning points in the data. Later, the development of methods such as spectral analysis led to a focus on oscillations. A distinction between cycles and oscillations is needed - both imply turning points, but turning points do not necessarily imply oscillations. Comin and Gertler (2006) argue that attention should be paid to medium term oscillations of 8-30 years rather than the standard 2-8 years of the business cycle, while Beaudry et al. (2019) suggest that there is a cycle of 9/10 years in series such as hours per capita. We investigate what the evidence is for the latter and find that it explains little of the variance of the data. We then show that some of the fillters being used to locate either turning points or oscillations in the series are not appropriate for the nature of the series being analyzed, specifically whether they are I(1) or I(0). Finally, we assess if the concepts of medium term and 9/10 year cycles are useful for comparing models and data. This is done by examining models of endogenous versus exogenous technology as well as limit cycles due to accumulation and complementarity mechanisms.","PeriodicalId":191102,"journal":{"name":"ERN: Time-Series Models (Multiple) (Topic)","volume":"119 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123186260","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}

{"title":"Discount Rates and Cash Flows: A Local Projection Approach","authors":"Matthijs Lof, Henri Nyberg","doi":"10.2139/ssrn.3372138","DOIUrl":"https://doi.org/10.2139/ssrn.3372138","url":null,"abstract":"We develop a volatility decomposition derived from flexible and robust local projections to quantify the relative contributions of expected discount rates and cash flows to the variation of dividend yields. Local projections enable the incorporation of large information sets, the use of monthly data along with annual data, and to consider time variation in the volatility decomposition. While the variation of expected discount rates remains the dominant contributor to market volatility, we find that the contribution of expected cash flows is non-negligible when moving beyond the standard model with the dividend yield as the single state variable.","PeriodicalId":191102,"journal":{"name":"ERN: Time-Series Models (Multiple) (Topic)","volume":"271 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134569448","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}

{"title":"An Automated Prior Robustness Analysis in Bayesian Model Comparison","authors":"J. Chan, Liana Jacobi, Dan Zhu","doi":"10.2139/ssrn.3409549","DOIUrl":"https://doi.org/10.2139/ssrn.3409549","url":null,"abstract":"The marginal likelihood is the gold standard for Bayesian model comparison although it is well-known that the value of marginal likelihood could be sensitive to the choice of prior hyperparameters. Most models require computationally intense simulation-based methods to evaluate the typically high-dimensional integral of the marginal likelihood expression. Hence, despite the recognition that prior sensitivity analysis is important in this context, it is rarely done in practice. In this paper we develop efficient and feasible methods to compute the sensitivities of marginal likelihood, obtained via two common simulation-based methods, with respect to any prior hyperparameter alongside the MCMC estimation algorithm. Our approach builds on Automatic Differentiation (AD), which has only recently been introduced to the more computationally intensive setting of Markov chain Monte Carlo simulation. We illustrate our approach with two empirical applications in the context of widely used multivariate time series models.","PeriodicalId":191102,"journal":{"name":"ERN: Time-Series Models (Multiple) (Topic)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132088634","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}

{"title":"Second Order Time Dependent Inflation Persistence in the United States: a GARCH-in-Mean Model with Time Varying Coefficients","authors":"A. Canepa, M. Karanasos, A. Paraskevopoulos","doi":"10.2139/ssrn.3521144","DOIUrl":"https://doi.org/10.2139/ssrn.3521144","url":null,"abstract":"In this paper we investigate the behavior of in?ation persistence in the United States. To model in?ation we estimate an autoregressive GARCH-in-mean model with variable coe¢ cients and we propose a new measure of second-order time varying persistence, which not only distinguishes between changes in the dynamics of in?ation and its volatility, but it also allows for feedback from nominal uncertainty to in?ation. Our empirical results suggest that in?ation persistence in the United States is best described as unchanged. Another important result relates to the Monte Carlo experiment evidence which reveal that if the model is misspeci?ed, then commonly used unit root tests will misclassify in?ation of being a nonstationary, rather than a stationary process.","PeriodicalId":191102,"journal":{"name":"ERN: Time-Series Models (Multiple) (Topic)","volume":"137 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116723168","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}

{"title":"A Spatial Autoregressive Combination Stochastic Frontier Model Separating Spatial Dependence in the Frontier and in Technical Inefficiency","authors":"T. Tsukamoto","doi":"10.2139/ssrn.3306953","DOIUrl":"https://doi.org/10.2139/ssrn.3306953","url":null,"abstract":"We identify the attributes and limitations of spatial autoregressive stochastic frontier models, spatial inefficiency error stochastic frontier models, and straightforward integrated models. We also propose a separable spatial autoregressive combination stochastic frontier model that overcomes the limitations. The proposed model’s main features are as follows: (a) spatial dependence in the frontier and in technical inefficiency are separately identified; (b) a negative spatial autocorrelation in technical inefficiency is permitted; (c) technical inefficiency follows a truncated-normal distribution.","PeriodicalId":191102,"journal":{"name":"ERN: Time-Series Models (Multiple) (Topic)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121295658","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}

{"title":"Commodity-Currencies or Currency-Commodities: Evidence from Causality Tests","authors":"Ariel R Belasen, Rıza Demirer","doi":"10.2139/ssrn.3128354","DOIUrl":"https://doi.org/10.2139/ssrn.3128354","url":null,"abstract":"Abstract This paper presents a comparative analysis of the return and volatility spillovers across the commodity and currency markets for an expanded set of commodity-exporters and currencies that includes several emerging commodity-exporting nations in addition to the developed exporters that have often been the focus in the literature. Strong causal effects are observed, largely in the direction of commodities from currencies with several cases of bidirectional causality, particularly between gold and the New Zealand dollar, Brent oil and the Brazilian real, and copper and the Chilean peso. The causal effects from currencies to commodities are not only limited to return causality, but exist in the case of volatility as well, implying the presence of significant risk transmissions from currencies. We also show that causal effects from currencies to commodities have become more widespread during the period following the 2007–2008 global financial crisis. Overall, our findings imply that currency market dynamics have informative value for commodity return and volatility with significant implications for volatility forecasting and active management of commodity price fluctuations.","PeriodicalId":191102,"journal":{"name":"ERN: Time-Series Models (Multiple) (Topic)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129989996","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}