Abstract
Hong and Kao (2004) proposed a panel data test for serial correlation of unknown form. However, their test is computationally difficult to implement, and simulation studies show the test to have bad small-sample properties. We extend Gencay’s (2011) time series test for serial correlation to the panel data case in the framework proposed by Hong and Kao (2004). Our new test maintains the advantages of the Hong and Kao (2004) test, and it is simpler and easier to implement. Furthermore, simulation results show that our test has quicker convergence and hence better small-sample properties.
Highlights
Correlated errors in regression models have several implications for econometric modeling, such as making parameter estimation inefficient and invalidating commonly used Student’s t test and F tests
We propose an alternative serial correlation test for panel data models that maintains the strengths of the Hong and Kao (2004) test and at the same time has a more simplified structure, higher convergence rate and better small-sample properties
When the series Z {Zt, t 0, . . . , T − 1} is pure white noise, as the variance of white noise is evenly distributed in the whole frequency interval, after the DWT and MODWT transform, the variance of wavelet and scaling coefficients should be evenly distributed as well
Summary
Correlated errors in regression models have several implications for econometric modeling, such as making parameter estimation inefficient and invalidating commonly used Student’s t test and F tests.
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