Testing for higher order serial correlation in stata

Please see for example help xtseqreg postestimation. The specification is not rejected if the AR 1 test rejects the null hypothesis of no first-order serial correlation but the AR 2 and all higher-order tests do not reject the null hypothesis of no second-order or higher serial correlation. The Hansen test should not reject the null hypothesis of joint validity of the overidentifying restrictions, i. You need to run estat serial after the first command, i.

It tests for the serial correlation in the first stage. Please can you help me with good synthax when I use this syntax:xtdpdgmm llfdi L. In your first case, xtseqreg only estimates the second stage. It is currently not possible to use estat serial after this second-stage estimation. The second case should work in principle. Could you please double check whether you really have specified the option aux with xtdpdgmm.

Without this option, you would indeed receive this error message from xtseqreg but with the option it should work. Please also make sure that you are using the latest version of xtseqreg : Code:. Please professor I have a same problem. When I update xtseqreg that is the message that i receive from stat: adoupdate xtseqreg, update note: adoupdate updates user-written files; type -update- to check for updates to official Stata Checking status of specified packages Typically use in dynamic models.

Example is from plm package. Assume there is no cross-sectional correlation Robust against intragroup heteroskedasticity and serial correlation. Suited when n is much larger than T long panel However, inefficient under groupwise heteorskedasticity. MNAR MAR A Guide on Data Analysis. Panel data structure is like having n samples of time series data Characteristics Information both across individuals and over time cross-sectional and time-series N individuals and T time periods Data can be either Balanced: all individuals are observed in all time periods Unbalanced: all individuals are not observed in all time periods.

Types Short panel: many individuals and few time periods. Between variation: variation between individuals Within variation: variation within individuals over time. Demean Approach is mathematically equivalent to Dummy Approach If you have only 1 period, all 3 are the same. You can give a parameter for the direction if needed. Issues: You could have fundamental difference between switchers and non-switchers. You can estimate FE for different units not just individuals. FE can be unbiased, but not consistent i.

Notes: Under a within i. Types: honda : Honda Default bp : T. Breusch and Pagan for unbalanced panels kw : M. Recommended for random effects. Problem: One of the major problems faced during the panel data analysis was data management. If the data is not arranged properly then it is very difficult to get the regression results. Even if the results are obtained, they will not be robust.

Solution: While conducting the panel data analysis the data should be saved in a particular format. For example, if we have data for 5 countries for 5 years then data for one country country A in this case should be in the following format. If the variables are string then it is not possible to conduct any analysis. We can either replace the string variable or create a new variable. Problem 4: Since panel data consists of both the time series and cross-sectional data, the usual descriptive analysis procedure do not give much logical result.

While performing regression analysis using panel data, it is important to check the basic assumptions. These assumptions can be tested using the following tests:. One of the basic assumptions of the panel data is Normality. Filtered by:. But, Kiviet , suggested that p-value of AR 1 should be less than 0.

Tags: None. Sebastian Kripfganz. First of all, do not use p-values such as 0. The world does not suddently change if you jump from one side of the threshold to the other.

The aim of the Arellano-Bond tests is to check whether the idiosyncratic error term is serially correlated. The test is conducted for the first-differenced errors. If the error term in levels is serially uncorrelated, this implies that the error term in first differences has negative first-order serial correlation with a correlation coefficient of