Trending time-varying coefficient time series models with serially correlated errors

By Zongwu Cai. 2007

364 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/90ac293a988242bb966415267d2a50559bb8c5fb

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. Liangjun Su
2. Xia Wang
3. Jiti Gao
4. Sainan Jin
5. Peter C. B. Phillips
6. Zhou Zhou
7. Degui Li
8. Yiguo Sun

In Finance

1. Liangjun Su
2. Jiti Gao
3. Zhou Zhou
4. Peter C. B. Phillips
5. Xia Wang
6. Sainan Jin
7. Wei Biao Wu
8. Joon Y. Park

In Statistics and Probability

1. Liangjun Su
2. Jiti Gao
3. Xia Wang
4. Wei Biao Wu
5. Sainan Jin
6. Qi Li
7. Peter C. B. Phillips
8. Ting Zhang

Citations in the corpus

• The effects of a “black swan” event (COVID-19) on herding behavior in cryptocurrency markets (2021, neutral): “First, as an unknown function of time,b ztð Þ ¼ f sð Þ, as proposed in Robinson (1989), and further developed by Cai (2007a, 2007b) and Chen et al. (2017).”
• Sovereign bond yield spreads: A time-varying coefficient approach (2012, neutral): “As stated by Cai (2007), a misspecification of the underlying coefficient function leads to serious bias in the estimation results.”
• A varying-coefficient panel data model with fixed effects: Theory and an application to US commercial banks (2017, methodology): “This normalization technique is similar to the one employed by Cai (2007) and Chen et al.”
• On Time-Varying Factor Models: Estimation and Testing ∗ (2017, methodology): “Chen et al. (2014) propose a two-stage procedure to detect big breaks in factor loadings by testing the parameter stability in the regression of one of the estimated factors on the remaining estimated factors. Corradi and Swanson (2014) propose a test to check structural stability of both factor loa”
• Estimating Smooth Structural Change in Cointegration Models (2017, background): “Nonparametric inference about time-varying parameters has received attention for modeling stationary or locally stationary time series data - see, for instance, Robinson (1989), Cai (2007), Li et al (2011), Chen and Hong (2012), and Zhang and Wu (2012).”
• GAUSSIAN APPROXIMATIONS FOR NON-STATIONARY MULTIPLE TIME SERIES (2011, background): “With the regression parameter β(·) being time-varying, one is able to explore the dynamic or time-varying associations between the response series {yi} and the explanatory series {xi}; see Robinson (1989, 1991), Orbe, Ferreira, and Rodriguez-Poo (2005, 2006), and Cai (2007). A fundamental problem in”
• Nonparametric Estimation and Forecasting for Time-Varying Coefficient Realized Volatility Models (2018, methodology): “The TVC models have been applied to several areas in finance, including the popular capital asset pricing model and the term structure of interest rates model (see Cochrane 2001; Tsay 2002; Cai 2007).”
• Functional-coefficient models for nonstationary time series data (2009, background): “(1.1) has been tackled by Robinson (1989, 1991), Cai (2007) and Chen andHong (2007) for stationary Xt , by Park and Hahn (1999) and Chang and MartinezChombo (2003) for nonstationary Xt , and by Cai and Wang (2008) for nearly integrated Xt .”
• Testing Conditional Factor Models: A Nonparametric Approach (2011, neutral): “, Cameron and Trivedi (2005); Mishra et al.”
• Inference of time-varying regression models (2012, background): “1) has been considered by [6, 41, 45, 46, 56] among others.”
• Simultaneous inference of linear models with time varying coefficients (2010, background): “It has attracted considerable attention; see Robinson (1989, 1991), Orbe et al. (2005, 2006) and Cai (2007) among others.”
• UNIFORM CONVERGENCE RATES OF KERNEL ESTIMATORS WITH HETEROGENEOUS DEPENDENT DATA (2009, neutral): “Alternatively, one could instead use a local linear estimator or a boundary kernel because these will not suffer from boundary biases (cf., respectively, Cai, 2007; and Chen, 2000), but we here maintain the preceding estimator for simplicity.”