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Estimating Smooth Structural Change in Cointegration Models
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Cited by:
- Dong, Chaohua & Linton, Oliver, 2018.
"Additive nonparametric models with time variable and both stationary and nonstationary regressors,"
Journal of Econometrics, Elsevier, vol. 207(1), pages 212-236.
- Chaohua Dong & Oliver Linton, 2017. "Additive nonparametric models with time variable and both stationary and nonstationary regressions," CeMMAP working papers 59/17, Institute for Fiscal Studies.
- Chaohua Dong & Oliver Linton, 2017. "Additive nonparametric models with time variable and both stationary and nonstationary regressions," CeMMAP working papers CWP59/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Yanbo Liu & Peter C. B. Phillips & Jun Yu, 2023.
"A Panel Clustering Approach To Analyzing Bubble Behavior,"
International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 64(4), pages 1347-1395, November.
- Yanbo Liu & Peter C. B. Phillips & Jun Yu, 2022. "A Panel Clustering Approach to Analyzing Bubble Behavior," Cowles Foundation Discussion Papers 2323, Cowles Foundation for Research in Economics, Yale University.
- Liu, Yanbo & Phillips, Peter C. B. & Yu, Jun, 2022. "A Panel Clustering Approach to Analyzing Bubble Behavior," Economics and Statistics Working Papers 1-2022, Singapore Management University, School of Economics.
- Casas Villalba, Maria Isabel, 2020. "Adaptative predictability of stock market returns," DES - Working Papers. Statistics and Econometrics. WS 31648, Universidad Carlos III de Madrid. Departamento de EstadÃstica.
- Li, Degui & Phillips, Peter C. B. & Gao, Jiti, 2016.
"Uniform Consistency Of Nonstationary Kernel-Weighted Sample Covariances For Nonparametric Regression,"
Econometric Theory, Cambridge University Press, vol. 32(3), pages 655-685, June.
- Degui Li & Peter C. B. Phillips & Jiti Gao, 2013. "Uniform Consistency of Nonstationary Kernel-Weighted Sample Covariances for Nonparametric Regression," Monash Econometrics and Business Statistics Working Papers 27/13, Monash University, Department of Econometrics and Business Statistics.
- Degui Li & Peter C.B. Phillips & Jiti Gao, 2013. "Uniform Consistency of Nonstationary Kernel-Weighted Sample Covariances for Nonparametric Regression," Cowles Foundation Discussion Papers 1929, Cowles Foundation for Research in Economics, Yale University.
- Arčabić, Vladimir & Gelo, Tomislav & Sonora, Robert J. & Šimurina, Jurica, 2021. "Cointegration of electricity consumption and GDP in the presence of smooth structural changes," Energy Economics, Elsevier, vol. 97(C).
- George Kapetanios & Stephen Millard & Katerina Petrova & Simon Price, 2018.
"Time varying cointegration and the UK great ratios,"
CAMA Working Papers
2018-53, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
- Kapetanios, George & Millard, Stephen & Petrova, Katerina & Price, Simon, 2019. "Time-varying cointegration and the UK great ratios," Bank of England working papers 789, Bank of England.
- Kapetanios, George & Millard, Stephen & Price, Simon & Petrova, Katerina, 2018. "Time varying cointegration and the UK Great Ratios," Essex Finance Centre Working Papers 23320, University of Essex, Essex Business School.
- Isabel Casas & Eva Ferreira & Susan Orbe, 2021.
"Time-Varying Coefficient Estimation in SURE Models. Application to Portfolio Management,"
Journal of Financial Econometrics, Oxford University Press, vol. 19(4), pages 707-745.
- Isabel Casas & Eva Ferreira & Susan Orbe, 2017. "Time-varying coefficient estimation in SURE models. Application to portfolio management," CREATES Research Papers 2017-33, Department of Economics and Business Economics, Aarhus University.
- Isabel Casas & Jiti Gao & Shangyu Xie, 2018.
"Modelling time-varying income elasticities of health care expenditure for the OECD,"
Monash Econometrics and Business Statistics Working Papers
22/18, Monash University, Department of Econometrics and Business Statistics.
- Isabel Casas & Jiti Gao & Shangyu Xie, 2018. "Modelling Time-Varying Income Elasticities of Health Care Expenditure for the OECD," CREATES Research Papers 2018-29, Department of Economics and Business Economics, Aarhus University.
- Gao, Jiti & Peng, Bin & Wu, Wei Biao & Yan, Yayi, 2024.
"Time-varying multivariate causal processes,"
Journal of Econometrics, Elsevier, vol. 240(1).
- Jiti Gao & Bin Peng & Wei Biao Wu & Yayi Yan, 2022. "Time-Varying Multivariate Causal Processes," Papers 2206.00409, arXiv.org.
- Tu, Yundong & Wang, Ying, 2022. "Spurious functional-coefficient regression models and robust inference with marginal integration," Journal of Econometrics, Elsevier, vol. 229(2), pages 396-421.
- Zhang, Yue-Jun & Zhang, Han, 2023. "Volatility forecasting of crude oil futures market: Which structural change-based HAR models have better performance?," International Review of Financial Analysis, Elsevier, vol. 85(C).
- Isabel Casas & Jiti Gao & Bin Peng & Shangyu Xie, 2021.
"Time‐varying income elasticities of healthcare expenditure for the OECD and Eurozone,"
Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(3), pages 328-345, April.
- Isabel Casas & Jiti Gao & Bin Peng & Shangyu Xie, 2019. "Time-Varying Income Elasticities of Healthcare Expenditure for the OECD and Eurozone," Monash Econometrics and Business Statistics Working Papers 28/19, Monash University, Department of Econometrics and Business Statistics.
- Li, Degui & Phillips, Peter C.B. & Gao, Jiti, 2020.
"Kernel-based Inference in Time-Varying Coefficient Cointegrating Regression,"
Journal of Econometrics, Elsevier, vol. 215(2), pages 607-632.
- Degui Li & Peter C.B. Phillips & Jiti Gao, 2017. "Kernel-Based Inference In Time-Varying Coefficient Cointegrating Regression," Cowles Foundation Discussion Papers 2109, Cowles Foundation for Research in Economics, Yale University.
- Tingting Cheng & Jiti Gao & Oliver Linton, 2017.
"Multi-step non- and semi-parametric predictive regressions for short and long horizon stock return prediction,"
Monash Econometrics and Business Statistics Working Papers
13/17, Monash University, Department of Econometrics and Business Statistics.
- Tingting Cheng & Jiti Gao & Oliver Linton, 2018. "Multi-step non- and semi-parametric predictive regressions for short and long horizon stock return prediction," CeMMAP working papers CWP03/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Polbin, Andrey & Skrobotov, Anton, 2022. "On decrease in oil price elasticity of GDP and investment in Russia," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 66, pages 5-24.
- Friedrich, Marina & Lin, Yicong, 2024. "Sieve bootstrap inference for linear time-varying coefficient models," Journal of Econometrics, Elsevier, vol. 239(1).
- Lin, Yingqian & Tu, Yundong & Yao, Qiwei, 2020. "Estimation for double-nonlinear cointegration," Journal of Econometrics, Elsevier, vol. 216(1), pages 175-191.
- Kunpeng Li & Degui Li & Zhongwen Liang & Cheng Hsiao, 2017. "Estimation of semi-varying coefficient models with nonstationary regressors," Econometric Reviews, Taylor & Francis Journals, vol. 36(1-3), pages 354-369, March.
- Gao, Jiti & Linton, Oliver & Peng, Bin, 2020.
"Inference On A Semiparametric Model With Global Power Law And Local Nonparametric Trends,"
Econometric Theory, Cambridge University Press, vol. 36(2), pages 223-249, April.
- Jiti Gao & Oliver Linton & Bin Peng, 2017. "Inference on a Semiparametric Model with Global Power Law and Local Nonparametric Trends," Monash Econometrics and Business Statistics Working Papers 10/17, Monash University, Department of Econometrics and Business Statistics.
- Jiti Gao & Oliver Linton & Bin Peng, 2018. "Inference on a semiparametric model with global power law and local nonparametric trends," CeMMAP working papers CWP05/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Peter C. B. Phillips, 2022. "Asymptotics of Polynomial Time Trend Estimation and Hypothesis Testing under Rank Deficiency," Cowles Foundation Discussion Papers 2332, Cowles Foundation for Research in Economics, Yale University.
- Harris, A.R. & Rogers, Michelle Marinich & Miller, Carol J. & McElmurry, Shawn P. & Wang, Caisheng, 2015. "Residential emissions reductions through variable timing of electricity consumption," Applied Energy, Elsevier, vol. 158(C), pages 484-489.
- Peng, Zhen & Dong, Chaohua, 2022. "Augmented cointegrating linear models with possibly strongly correlated stationary and nonstationary regressors," Finance Research Letters, Elsevier, vol. 47(PB).
- Lin, Yingqian & Tu, Yundong & Yao, Qiwei, 2020. "Estimation for double-nonlinear cointegration," LSE Research Online Documents on Economics 103830, London School of Economics and Political Science, LSE Library.
- Isabel Casas & Xiuping Mao & Helena Veiga, 2018. "Reexamining financial and economic predictability with new estimators of realized variance and variance risk premium," CREATES Research Papers 2018-10, Department of Economics and Business Economics, Aarhus University.
- Ayman Mnasri & Zouhair Mrabet & Mouyad Alsamara, 2023. "A new quadratic asymmetric error correction model: does size matter?," Empirical Economics, Springer, vol. 65(1), pages 33-64, July.
- Qiying Wang & Peter C. B. Phillips & Ying Wang, 2023. "New asymptotics applied to functional coefficient regression and climate sensitivity analysis," Cowles Foundation Discussion Papers 2365, Cowles Foundation for Research in Economics, Yale University.
- Haiqi Li Author-Name-First: Haiqi & Jing Zhang & Chaowen Zheng, 2023. "Estimating and Testing for Functional Coefficient Quantile Cointegrating Regression," Economics Discussion Papers em-dp2023-07, Department of Economics, University of Reading.
- Zhishui Hu & Ioannis Kasparis & Qiying Wang, 2020. "Locally trimmed least squares: conventional inference in possibly nonstationary models," Papers 2006.12595, arXiv.org.
- Yousuf, Kashif & Ng, Serena, 2021.
"Boosting high dimensional predictive regressions with time varying parameters,"
Journal of Econometrics, Elsevier, vol. 224(1), pages 60-87.
- Kashif Yousuf & Serena Ng, 2019. "Boosting High Dimensional Predictive Regressions with Time Varying Parameters," Papers 1910.03109, arXiv.org.
- David I. Harvey & Stephen J. Leybourne & Yang Zu, 2023. "Estimation of the variance function in structural break autoregressive models with non‐stationary and explosive segments," Journal of Time Series Analysis, Wiley Blackwell, vol. 44(2), pages 181-205, March.
- Kapetanios, George & Millard, Stephen & Petrova, Katerina & Price, Simon, 2020. "Time-varying cointegration with an application to the UK Great Ratios," Economics Letters, Elsevier, vol. 193(C).
- Yu, Deshui & Chen, Li & Li, Luyang, 2023. "Time-varying predictability of the long horizon equity premium based on semiparametric regressions," Economics Letters, Elsevier, vol. 224(C).
- Dong, Chaohua & Linton, Oliver & Peng, Bin, 2021. "A weighted sieve estimator for nonparametric time series models with nonstationary variables," Journal of Econometrics, Elsevier, vol. 222(2), pages 909-932.
- Tu, Yundong & Liang, Han-Ying & Wang, Qiying, 2022. "Nonparametric inference for quantile cointegrations with stationary covariates," Journal of Econometrics, Elsevier, vol. 230(2), pages 453-482.
- Li, Li & Tu, Yundong, 2022. "The varying spillover of U.S. systemic risk: A functional-coefficient cointegration approach," Economics Letters, Elsevier, vol. 212(C).
- Yicong Lin & Mingxuan Song, 2023. "Robust bootstrap inference for linear time-varying coefficient models: Some Monte Carlo evidence," Tinbergen Institute Discussion Papers 23-049/III, Tinbergen Institute.
- Shan Dai & Ngai Hang Chan, 2023. "Testing of Constant Parameters for Semi‐Parametric Functional Coefficient Models with Integrated Covariates," Journal of Time Series Analysis, Wiley Blackwell, vol. 44(5-6), pages 474-486, September.
- Yayi Yan & Jiti Gao & Bin Peng, 2021. "Asymptotics for Time-Varying Vector MA(∞) Processes," Monash Econometrics and Business Statistics Working Papers 22/21, Monash University, Department of Econometrics and Business Statistics.