• Login
  • Register
  • Search

An Improved Nonlinear Granger Causality Test Method

Nan Zhang, Mingwei Li, Qinqin Tang

Abstract


In order to solve the problem that the traditional linear Granger causality test method cannot capture nonlinear characteristics, this paper proposes the nonlinear Granger causality test method by improving the smooth transition function of the smooth transition autoregressive (STAR) model. The empirical study examines the Granger causality between Consumer Price Index (CPI) and Producer Price Index for Industrial Products (PPI) from both linear and nonlinear perspectives, and the results show that the method has higher robustness.


Keywords


Nonlinear Granger Causality Test; Smooth Transition Autoregressive Model; Consumer Price Index; Producer Price Index For Industrial Products

Full Text:

PDF

Included Database


References


Granger CWJ, Investigating Causal Relations by Econometric Models and Cross-spectral Methods, Econometrica, 37 (3) (1969) 424-438.

Ruan J. et al., Fuzzy Correlation Measurement Algorithms for Big Data and Application to Exchange Rates and Stock Prices, in IEEE Transactions on Industrial Informatics, 16 (2) (2020) 1296-1309.

Ji Hwan Park, Woojin Chang, Jae Wook Song, Link prediction in the Granger causality network of the global currency market, Physica A: Statistical Mechanics and its Applications, 553 (2020) 124668.

Wang GJ, Si HB, Chen YY, Xie C, Chevallier J, Time domain and frequency domain Granger causality networks: Application to China’s financial institutions, Finance Research Letters, 39 (2021) 101662.

Baek, E.G., Brock, A.W., A General Test for Nonlinear Granger Causality: Bivariate Model, Technical Report, Korean Development Institute and University of Wisconsin-Madison, (1992).

Hiemstra, C., Jones, J. D., Testing for Linear and Nonlinear Granger Causality in the Stock Price-Volume Relation, The Journal of Finance, 49 (5) (1994) 1639–1664.

Cees Diks, Valentyn Panchenko, A new statistic and practical guidelines for nonparametric Granger causality testing, Journal of Economic Dynamics and Control, 30 (9–10) (2006) 1647-1669.

Diks C, Wolski M. Nonlinear Granger Causality: Guidelines for Multivariate Analysis, Journal of Applied Econometrics, 31 (7) (2016) 1333-1351.

Lee TH, Yang WP, Granger-causality in quantiles between financial markets: Using copula approach, International Review of Financial Analysis, 33 (2014) 70-78.

Ren WJ, Li BS, Han M. A novel Granger causality method based on HSIC-Lasso for revealing nonlinear relationship between multivariate time series[J]. Physica A: Statistical Mechanics and its Applications, 2020, 541.

Granger CWJ, Teräsvirta T, Modeling Nonlinear Economic Relationships, Oxford University Press, New York, 1993.

Broock WA, Scheinkman JA, Dechert WD & LeBaron B, A test for independence based on the correlation dimension, Econometric Reviews, 15 (3) (1996) 197-235.




DOI: http://dx.doi.org/10.18686/fm.v8i2.6980

Refbacks