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Studies in Nonlinear Dynamics & Econometrics最新文献

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Power of Unit Root Tests Against Nonlinear and Noncausal Alternatives with an Application to the Brent Crude Oil Price 以布伦特原油价格为例：非线性和非因果替代方法的单位根检验功率
Studies in Nonlinear Dynamics & Econometrics Pub Date : 2023-12-14 DOI: 10.1515/snde-2022-0084
Frédérique Bec, Alain Guay, Heino Bohn Nielsen, Sarra Saïdi
{"title":"Power of Unit Root Tests Against Nonlinear and Noncausal Alternatives with an Application to the Brent Crude Oil Price","authors":"Frédérique Bec, Alain Guay, Heino Bohn Nielsen, Sarra Saïdi","doi":"10.1515/snde-2022-0084","DOIUrl":"https://doi.org/10.1515/snde-2022-0084","url":null,"abstract":"The increasing sophistication of economic and financial time series modelling creates a need for a test of the time dependence structure of the series which does not require a proper specification of the alternative. Indeed, the latter is unknown beforehand. Yet, the stationarity has to be established before proceeding to the estimation and testing of causal/noncausal or linear/nonlinear models as their econometric theory has been developed under the maintained assumption of stationarity. In this paper, we propose a new unit root test statistics which is both asymptotically consistent against all stationary alternatives and still keeps good power properties in finite sample. A large simulation study is performed to assess the power of our test compared to existing unit root tests built specifically for various kinds of stationary alternatives, when the true DGP is either causal or noncausal, linear or nonlinear stationary. Based on various sample sizes and degrees of persistence, it turns out that our new test performs very well in terms of power in finite sample, no matter the alternative under consideration. The proposed approach is illustrated using recent Brent crude oil price data.","PeriodicalId":501448,"journal":{"name":"Studies in Nonlinear Dynamics & Econometrics","volume":"30 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138691869","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
Combining Large Numbers of Density Predictions with Bayesian Predictive Synthesis 将大量密度预测与贝叶斯预测合成相结合
Studies in Nonlinear Dynamics & Econometrics Pub Date : 2023-12-14 DOI: 10.1515/snde-2022-0108
Tony Chernis
{"title":"Combining Large Numbers of Density Predictions with Bayesian Predictive Synthesis","authors":"Tony Chernis","doi":"10.1515/snde-2022-0108","DOIUrl":"https://doi.org/10.1515/snde-2022-0108","url":null,"abstract":"Bayesian Predictive Synthesis is a flexible method of combining density predictions. The flexibility comes from the ability to choose an arbitrary synthesis function to combine predictions. I study choice of synthesis function when combining large numbers of predictions – a common occurrence in macroeconomics. Estimating combination weights with many predictions is difficult, so I consider shrinkage priors and factor modelling techniques to address this problem. These techniques provide an interesting contrast between the sparse weights implied by shrinkage priors and dense weights of factor modelling techniques. I find that the sparse weights of shrinkage priors perform well across exercises.","PeriodicalId":501448,"journal":{"name":"Studies in Nonlinear Dynamics & Econometrics","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138691585","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
Modeling Corporate CDS Spreads Using Markov Switching Regressions 利用马尔科夫切换回归建立公司 CDS 利差模型
Studies in Nonlinear Dynamics & Econometrics Pub Date : 2023-12-11 DOI: 10.1515/snde-2022-0106
Ovielt Baltodano López, Giacomo Bulfone, Roberto Casarin, Francesco Ravazzolo
{"title":"Modeling Corporate CDS Spreads Using Markov Switching Regressions","authors":"Ovielt Baltodano López, Giacomo Bulfone, Roberto Casarin, Francesco Ravazzolo","doi":"10.1515/snde-2022-0106","DOIUrl":"https://doi.org/10.1515/snde-2022-0106","url":null,"abstract":"This paper investigates the determinants of the European iTraxx corporate CDS index considering a large set of explanatory variables within a Markov switching model framework. The influence of financial and economic variables on CDS spreads are compared using linear, two, three, and four-regime models in a sample post-subprime financial crisis up to the COVID-19 pandemic. Results indicate that four regimes are necessary to model the CDS spreads. The fourth regime was activated during the COVID-19 pandemic and in high volatility periods. Further, the effect of the covariates differs significantly across regimes. Brent and term structure factors became relevant after the outbreak of the COVID-19 pandemic.","PeriodicalId":501448,"journal":{"name":"Studies in Nonlinear Dynamics & Econometrics","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138572363","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
Artificial Neural Networks and Time Series of Counts: A Class of Nonlinear INGARCH Models 人工神经网络和计数时间序列：一类非线性 INGARCH 模型
Studies in Nonlinear Dynamics & Econometrics Pub Date : 2023-12-07 DOI: 10.1515/snde-2022-0095
Malte Jahn
{"title":"Artificial Neural Networks and Time Series of Counts: A Class of Nonlinear INGARCH Models","authors":"Malte Jahn","doi":"10.1515/snde-2022-0095","DOIUrl":"https://doi.org/10.1515/snde-2022-0095","url":null,"abstract":"Time series of counts are frequently analyzed using generalized integer-valued autoregressive models with conditional heteroskedasticity (INGARCH). These models employ response functions to map a vector of past observations and past conditional expectations to the conditional expectation of the present observation. In this paper, it is shown how INGARCH models can be combined with artificial neural network (ANN) response functions to obtain a class of nonlinear INGARCH models. The ANN framework allows for the interpretation of many existing INGARCH models as a degenerate version of a corresponding neural model. Details on maximum likelihood estimation, marginal effects and confidence intervals are given. The empirical analysis of time series of bounded and unbounded counts reveals that the neural INGARCH models are able to outperform reasonable degenerate competitor models in terms of the information loss.","PeriodicalId":501448,"journal":{"name":"Studies in Nonlinear Dynamics & Econometrics","volume":"55 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138562200","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
Should You Use GARCH Models for Forecasting Volatility? A Comparison to GRU Neural Networks 你应该使用GARCH模型来预测波动率吗?与GRU神经网络的比较
Studies in Nonlinear Dynamics & Econometrics Pub Date : 2023-12-04 DOI: 10.1515/snde-2022-0025
Alberto Pallotta, Vito Ciciretti
{"title":"Should You Use GARCH Models for Forecasting Volatility? A Comparison to GRU Neural Networks","authors":"Alberto Pallotta, Vito Ciciretti","doi":"10.1515/snde-2022-0025","DOIUrl":"https://doi.org/10.1515/snde-2022-0025","url":null,"abstract":"The GARCH model is the most used technique for forecasting conditional volatility. However, the nearly integrated behaviour of the conditional variance originates from structural changes which are not accounted for by standard GARCH models. We compare the forecasting performance of the GARCH model to three regime switching models: namely, the Markov Switching GARCH, the Hidden Markov Model, and the Gated Recurrent Unit neural network. We define the number of optimal states by means of three methods: piecewise linear regression, Baum–Welch algorithm and Markov Chain Monte Carlo. Since forecasting volatility models face the bias-variance trade-off, we compare their out-of-sample forecasting performance via a walk-forward methodology. Moreover, we provide a robustness check for the results by applying k-fold cross-validation to the original time series. The Gated Recurrent Unit network is the best suited for volatility forecasting, while the Hidden Markov Model is the best at discerning the market regimes.","PeriodicalId":501448,"journal":{"name":"Studies in Nonlinear Dynamics & Econometrics","volume":"22 3-4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138534750","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
Volatility Forecasting Using Quasi-Score-Driven Models with an Application to the Coronavirus Pandemic Period 准分数驱动模型的波动率预测及其在冠状病毒大流行期的应用
Studies in Nonlinear Dynamics & Econometrics Pub Date : 2023-11-30 DOI: 10.1515/snde-2022-0085
Astrid Ayala, Szabolcs Blazsek, Adrian Licht
{"title":"Volatility Forecasting Using Quasi-Score-Driven Models with an Application to the Coronavirus Pandemic Period","authors":"Astrid Ayala, Szabolcs Blazsek, Adrian Licht","doi":"10.1515/snde-2022-0085","DOIUrl":"https://doi.org/10.1515/snde-2022-0085","url":null,"abstract":"We study the statistical and volatility forecasting performances of the recent quasi-score-driven EGARCH (exponential generalized autoregressive conditional heteroscedasticity) models. We compare the quasi-score-driven EGARCH models with GARCH, asymmetric power ARCH (A-PARCH), and all relevant score-driven EGARCH models of the literature. For score-driven and quasi-score-driven EGARCH, we use the following seven score-driven probability distributions: Student’s <jats:italic>t</jats:italic>-distribution; general error distribution (GED); generalized <jats:italic>t</jats:italic>-distribution (Gen-<jats:italic>t</jats:italic>); skewed generalized <jats:italic>t</jats:italic>-distribution (Skew-Gen-<jats:italic>t</jats:italic>); exponential generalized beta distribution of the second kind (EGB2); normal-inverse Gaussian distribution (NIG); Meixner distribution (MXN). We use all combinations of those distributions for (i) the probability distribution of the dependent variable, and (ii) the probability distribution which defines the quasi-score function updating term of the quasi-score-driven filters. We use daily data for the Standard &amp; Poor’s 500 (S&amp;P 500) index. We find that both in-sample and out-of-sample, quasi-score-driven EGARCH is superior to GARCH, A-PARCH, and score-driven EGARCH. We report in-sample results for the period of January 2000 to December 2020, providing evidence in favor of the quasi-score-driven EGARCH model for the last two decades. We report out-of-sample volatility forecasting results for a period within the coronavirus disease 2019 (COVID-19) pandemic, providing evidence in favor of the quasi-score-driven EGARCH model for a crisis period.","PeriodicalId":501448,"journal":{"name":"Studies in Nonlinear Dynamics & Econometrics","volume":"35 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138534749","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
Interfuel Substitution and Inflation Dynamics in India 印度燃料间替代和通货膨胀动态
Studies in Nonlinear Dynamics & Econometrics Pub Date : 2023-11-30 DOI: 10.1515/snde-2022-0083
Anirban Sengupta, Apostolos Serletis, Libo Xu
{"title":"Interfuel Substitution and Inflation Dynamics in India","authors":"Anirban Sengupta, Apostolos Serletis, Libo Xu","doi":"10.1515/snde-2022-0083","DOIUrl":"https://doi.org/10.1515/snde-2022-0083","url":null,"abstract":"This paper uses neoclassical microeconomic theory to investigate the demand for energy and interfuel substitution in India at the sectoral level. It makes full use of the relevant economic theory and econometrics and generates inference in terms of Allen and Morishima elasticities of substitution that are internally consistent with the data and nonlinear models used. The results indicate that the interfuel substitution elasticities are consistently below unity in the household and power sectors, revealing the limited ability to substitute between major energy commodities in these two sectors. However, significant substitution relationships are found in the industrial and transportation sectors, suggesting that energy price changes in these sectors will significantly shift the demand for energy and consumption. Based on measured elasticities of substitution, we also discuss implications of energy price shocks on inflation and inflation targeting strategies by the central bank.","PeriodicalId":501448,"journal":{"name":"Studies in Nonlinear Dynamics & Econometrics","volume":"66 7-8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138534752","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
Commitment Issues: Does the Fed Have an Inflation Incentive to Commit? 承诺问题:美联储有承诺通胀的动机吗?
Studies in Nonlinear Dynamics & Econometrics Pub Date : 2023-11-27 DOI: 10.1515/snde-2022-0034
C. Patrick Scott
{"title":"Commitment Issues: Does the Fed Have an Inflation Incentive to Commit?","authors":"C. Patrick Scott","doi":"10.1515/snde-2022-0034","DOIUrl":"https://doi.org/10.1515/snde-2022-0034","url":null,"abstract":"Long-run results indicate that for price and wage inflation there is little disincentive for discretionary policy when monetary policy is at or near the zero-lower bound. Optimal commitment and discretionary policy are examined in a popular DSGE framework. The monetary authority targets a convex combination of price and wage inflationary gaps around time-varying inflation targets. A joint hypothesis test is derived to determine if the central bank faces an inflationary disincentive for activist policy. Considering price and wage inflation separately, there are significant short-run disincentives to discretionary policy. Discretion and commitment policies are not different for price and wage inflation when nominal interest rates are persistently low.","PeriodicalId":501448,"journal":{"name":"Studies in Nonlinear Dynamics & Econometrics","volume":"33 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138535174","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
Markov-Switching Models with Unknown Error Distributions: Identification and Inference Within the Bayesian Framework 误差分布未知的马尔可夫切换模型:贝叶斯框架下的识别与推理
Studies in Nonlinear Dynamics & Econometrics Pub Date : 2023-11-27 DOI: 10.1515/snde-2022-0055
Shih-Tang Hwu, Chang-Jin Kim
{"title":"Markov-Switching Models with Unknown Error Distributions: Identification and Inference Within the Bayesian Framework","authors":"Shih-Tang Hwu, Chang-Jin Kim","doi":"10.1515/snde-2022-0055","DOIUrl":"https://doi.org/10.1515/snde-2022-0055","url":null,"abstract":"The basic Markov-switching model has been extended in various ways ever since the seminal work of Hamilton (1989. “A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle.” <jats:italic>Econometrica</jats:italic> 57: 357–84). However, the estimation of Markov-switching models in the literature has relied upon parametric assumptions on the distribution of the error term. In this paper, we present a Bayesian approach for estimating Markov-switching models with unknown and potentially non-normal error distributions. We approximate the unknown distribution of the error term by the Dirichlet process mixture of normals, in which the number of mixtures is treated as a parameter to estimate. In doing so, we pay special attention to the identification of the model. We then apply the proposed model and MCMC procedure to the growth of the postwar U.S. industrial production index. Our model can effectively control for irregular components that are not related to business conditions. This leads to sharp and accurate inferences on recession probabilities.","PeriodicalId":501448,"journal":{"name":"Studies in Nonlinear Dynamics & Econometrics","volume":"79 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138534751","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
Investor Sentiment Mining Based on Bi-LSTM Model and its Impact on Stock Price Bubbles 基于 Bi-LSTM 模型的投资者情绪挖掘及其对股价泡沫的影响
Studies in Nonlinear Dynamics & Econometrics Pub Date : 2023-11-27 DOI: 10.1515/snde-2022-0028
Haiyuan Yin, Qingsong Yang
{"title":"Investor Sentiment Mining Based on Bi-LSTM Model and its Impact on Stock Price Bubbles","authors":"Haiyuan Yin, Qingsong Yang","doi":"10.1515/snde-2022-0028","DOIUrl":"https://doi.org/10.1515/snde-2022-0028","url":null,"abstract":"Abstract We built a Bi-Directional long-term and short-term memory (Bi-LSTM) model to identify and classify the Chinese posting text of stocks on the Eastmoney website in China and constructed the daily index of Chinese investors’ sentiment. Furthermore, based on the GSADF method, we examine the stock price bubbles and study the impact of investor sentiment and stock price bubbles. We found investor sentiment has a positive effect on the existence of stock bubbles, as well as their intensity. This effect is more significant in small-scale, high-equity concentration, and non-state-owned enterprises. Investor sentiment has an impact on stock price bubbles through volatility, and stock price bubbles are often accompanied by higher premium risk. The conclusion is helpful to understand the mechanism of investor sentiment on stock bubbles from a micro perspective, and it also can be a reference in identifying stock bubbles from the viewpoint of investor sentiment.","PeriodicalId":501448,"journal":{"name":"Studies in Nonlinear Dynamics & Econometrics","volume":"29 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139230402","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
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