Relevant bibliographies by topics / Heteroscedasticity. Autoregression (Statistics) Time-series analysis

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters

Academic literature on the topic 'Heteroscedasticity. Autoregression (Statistics) Time-series analysis'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Heteroscedasticity. Autoregression (Statistics) Time-series analysis"

1

Liu, Meili, Liwei Wang, Chun-Te Lee, and Jeng-Eng Lin. "Volatility Analysis and Visualization of Climate Data Based on Wavelets." Journal of Mathematics Research 13, no. 4 (2021): 50. http://dx.doi.org/10.5539/jmr.v13n4p50.

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In this article, we analyze the real meteorological data recorded by Wenzhou Meteorological Bureau from 1951 to 1997. The data has not been used elsewhere and is available at Meteorological Station Wenzhou (ID: CHM00058659) at https://geographic.org/global_weather/china. We perform the time series volatility analysis including ARMA, ARIMA, ARCH-LM, PARCH, SARMA and Morlet wavelet analysis and use the Mann-Kendall (M-K) test to analyze both the trend and mutation defined by statistics sequence. In addition, a Morete wavelet time-frequency model is established to show that both the precipitation
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Dong, Kai, Ching-Ter Chang, Shaonan Wang, and Xiaoxi Liu. "The Dynamic Correlation among Financial Leverage, House Price, and Consumer Expenditure in China." Sustainability 13, no. 5 (2021): 2617. http://dx.doi.org/10.3390/su13052617.

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With the help of the time-varying parameter vector autoregression with stochastic volatility (TVP-SV-VAR) model and the Bayesian dynamic conditional correlational autoregressive conditional heteroscedasticity (Bayesian DCC-GARCH) model, this study analyzes the interaction mechanism and dynamic correlation among financial leverage, house price, and consumer expenditure (the survey data are collected from China’s National Bureau of Statistics from January 2000 to December 2019; the data on financial leverage and consumer expenditure are from the Wind economic database, and the price of commercia
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L., Wiri, Sibeate P.U., and Isaac D.E. "Markov Switching Intercept Vector Autoregressive Model (MSI(2)-VAR(2)) of Nigeria Inflation Rate and Crude Oil Price (Using Views 11)." African Journal of Mathematics and Statistics Studies 4, no. 2 (2021): 88–100. http://dx.doi.org/10.52589/ajmss-vy1oocxz.

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To model inflation rate and crude oil prices, we used Markov Switching intercept heteroscedasticity Vector Autoregressive models. The data for this analysis was gathered from the Central Bank of Nigeria Statistical Bulletin monthly. The upward and downward movement in the series revealed by the time plot suggests that the series exhibit a regime-switching pattern: the period of expansion and contraction. The variable was stationary at first differences, the Augmented Dickey-Fuller test was used to screen for stationarity. The information criteria were used to test the number of regime and regi
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Li, Xuedi, Jie Ma, Zhu Chen, and Haitao Zheng. "Linkage Analysis among China’s Seven Emissions Trading Scheme Pilots." Sustainability 10, no. 10 (2018): 3389. http://dx.doi.org/10.3390/su10103389.

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This paper focuses on the time-varying correlation among China’s seven emissions trading scheme markets. Correlation analysis shows a weak connection among these markets for the whole sample period, which spans from 9 June 2014 to 30 June 2017. The return rate series of the seven markets show the characteristics of a fat-tailed and skewed distribution, and the Vector Autoregression (VAR) residuals present a significant Autoregressive Conditional Heteroscedasticity (ARCH) effect. Therefore, we adopt Vector Autoregression Generalized ARCH model with Dynamic Conditional Correlation (VAR-DCC-GARCH
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Jones, Richard H. "Time series regression with unequally spaced data." Journal of Applied Probability 23, A (1986): 89–98. http://dx.doi.org/10.2307/3214345.

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Regression analysis with stationary errors is extended to the case when observations are not equally spaced. The errors are modelled as either a discrete-time ARMA process with missing observations, or as a continuous-time autoregression with observational error observed at arbitrary times. Using a state-space representation, a Kalman filter is used to calculate the exact likelihood. The linear regression coefficients are separated out of the likelihood so non-linear optimization is required only with respect to the parameters modelling the error structure.
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Jones, Richard H. "Time series regression with unequally spaced data." Journal of Applied Probability 23, A (1986): 89–98. http://dx.doi.org/10.1017/s0021900200117000.

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Regression analysis with stationary errors is extended to the case when observations are not equally spaced. The errors are modelled as either a discrete-time ARMA process with missing observations, or as a continuous-time autoregression with observational error observed at arbitrary times. Using a state-space representation, a Kalman filter is used to calculate the exact likelihood. The linear regression coefficients are separated out of the likelihood so non-linear optimization is required only with respect to the parameters modelling the error structure.
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7

Kokoszka, Piotr, Gregory Rice, and Han Lin Shang. "Inference for the autocovariance of a functional time series under conditional heteroscedasticity." Journal of Multivariate Analysis 162 (November 2017): 32–50. http://dx.doi.org/10.1016/j.jmva.2017.08.004.

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Yang, Kai, Dehui Wang, and Han Li. "Threshold autoregression analysis for finite-range time series of counts with an application on measles data." Journal of Statistical Computation and Simulation 88, no. 3 (2017): 597–614. http://dx.doi.org/10.1080/00949655.2017.1400032.

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Collomb, Gbbabb. "Jfon parametric time series analysis and prediction: uniform almost sure convergence of the window and jt-nn autoregression estimates." Statistics 16, no. 2 (1985): 297–307. http://dx.doi.org/10.1080/02331888508801858.

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Wu, Edward Ming-Yang, and Shu-Lung Kuo. "VARMA-EGARCH Model for Air-Quality Analyses and Application in Southern Taiwan." Atmosphere 11, no. 10 (2020): 1096. http://dx.doi.org/10.3390/atmos11101096.

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This study adopted the Exponential Generalized Autoregressive Conditional Heteroscedasticity (EGARCH) model to analyze seven air pollutants (or the seven variables in this study) from ten air quality monitoring stations in the Kaohsiung–Pingtung Air Pollutant Control Area located in southern Taiwan. Before the verification analysis of the EGARCH model is conducted, the air quality data collected at the ten air quality monitoring stations in the Kaohsiung–Pingtung area are classified into three major factors using the factor analyses in multiple statistical analyses. The factors with the most s
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Dissertations / Theses on the topic "Heteroscedasticity. Autoregression (Statistics) Time-series analysis"

1

黃香 and Heung Wong. "Topics in conditional heteroscedastic time series modelling." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1995. http://hub.hku.hk/bib/B31234513.

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Wong, Heung. "Topics in conditional heteroscedastic time series modelling /." Hong Kong : University of Hong Kong, 1995. http://sunzi.lib.hku.hk/hkuto/record.jsp?B14035492.

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Xiong, Yimin. "Time series clustering using ARMA models /." View abstract or full-text, 2004. http://library.ust.hk/cgi/db/thesis.pl?COMP%202004%20XIONG.

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Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2004.<br>Includes bibliographical references (leaves 49-55). Also available in electronic version. Access restricted to campus users.
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Li, Chun-wah. "On a double threshold autoregressive heteroskedastic time series model /." [Hong Kong : University of Hong Kong], 1994. http://sunzi.lib.hku.hk/hkuto/record.jsp?B13745037.

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李振華 and Chun-wah Li. "On a double threshold autoregressive heteroskedastic time seriesmodel." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1994. http://hub.hku.hk/bib/B31211653.

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Tai, Man Tang. "Portmanteau statistics for partially nonstationary multivariate AR and ARMA models /." View Abstract or Full-Text, 2003. http://library.ust.hk/cgi/db/thesis.pl?MATH%202003%20TAI.

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Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2003.<br>Includes bibliographical references (leaves 63-64). Also available in electronic version. Access restricted to campus users.
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Pang, Kwok-wing. "Statistical analysis of high frequency data using autoregressive conditional duration models /." Hong Kong : University of Hong Kong, 2001. http://sunzi.lib.hku.hk/hkuto/record.jsp?B2275314x.

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Ma, Sai-shing, and 馬世晟. "On the long memory autoregressive conditional duration models." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2014. http://hdl.handle.net/10722/197101.

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In financial markets, transaction durations refer to the duration time between two consecutive trades. It is common that more frequent trades are expected to be followed by shorter durations between consecutive transactions, while less frequent trades are expected to be followed by longer durations. Autoregressive conditional duration (ACD) model was developed to model transaction durations, based on the assumption that the expected average duration is dependent on the past durations. Empirically, transaction durations possess much longer memory than expected. The autocorrelation functions
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Sze, Mei Ki. "Mixed portmanteau test for ARMA-GARCH models /." View abstract or full-text, 2009. http://library.ust.hk/cgi/db/thesis.pl?MATH%202009%20SZE.

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Chong, Ching Yee. "Portmanteau testing for nonstationary autoregressive moving-average models /." View Abstract or Full-Text, 2003. http://library.ust.hk/cgi/db/thesis.pl?MATH%202003%20CHONG.

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Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2003.<br>Includes bibliographical references (leaves 37-39). Also available in electronic version. Access restricted to campus users.
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Books on the topic "Heteroscedasticity. Autoregression (Statistics) Time-series analysis"

1

Paparoditis, Efstathios. Vektorautokorrelationen stochastischer Prozesse und die Spezifikation von ARMA-Modellen. Physica-Verlag, 1990.

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Wensink, Hans Einar. Autoregressive model inference in finite samples =: Autoregressief modelleren op basis van een eindig aantal waarnemingen. Delft University Press, 1996.

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Reimers, Hans-Eggert. Analyse kointegrierter Variablen mittels vektorautoregressiver Modelle. Physica-Verlag, 1991.

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Ishiguro, M. ARdock, an auto-regressive model analyzer. Institute of Statistical Mathematics, 1999.

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Ishiguro, M. ARdock, an auto-regressive model analyzer. Institute of Statistical Mathematics, 1999.

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6

Robuste Schätzung von ARMA-Modellen unter Verwendung von robust geschätzten Autokovarianzen. P. Lang, 1994.

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Mocan, H. Naci. Business cycles and fertility dynamics in the U.S.: A vector-autoregressive model. National Bureau of Economic Research, 1989.

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Joyce, Theodore J. The dynamic relationship between low birthweight and induced abortion in New York City: An aggregate time-series analysis. National Bureau of Economic Research, 1989.

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9

Rebucci, Alessandro. On t he heterogeneity bias of pooled estimators in stationary VAR specifications. International Monetary Fund, Research Department, 2003.

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Models for Dependent Time Series (Monographs on Statistics and Applied Probability). Chapman & Hall/CRC, 2009.

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Book chapters on the topic "Heteroscedasticity. Autoregression (Statistics) Time-series analysis"

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Heiler, Siegfried. "Nonparametric Time Series Analysis: Nonparametric Regression, Locally Weighted Regression, Autoregression, and Quantile Regression." In Wiley Series in Probability and Statistics. John Wiley & Sons, Inc., 2011. http://dx.doi.org/10.1002/9781118032978.ch12.

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