Determining lag length in a panel dataset.

[Ritika Khurana]

Hi all,

I'm using -xtscc- command to estimate a macroeconomic mode. This dataset set has 133 countries and 33 years (unbalanced panel).
My question pertains to lag length used in a fixed effect model.

Variables: dlpccarb: log of per capita carbon emissions, dlrgdp: log of per capita GDP, dlpopden: log of population density, frleg: institutional integrity, fr_lrgdp: interaction between institutional integrity and gdp, lfoss: log of fossil fuels, renew: renewables (% of eneregy consumption)

xtscc dlpccarb L.dlpccarb dlrgdp dlrgdp2 D.frleg D.frleg2 dlpopden  D.fr_lrgdp D.fr2_lrgdp2 D.renew D.lfoss period*, fe lag(7)
xtscc dlpccarb L.dlpccarb dlrgdp dlrgdp2 D.frleg D.frleg2 dlpopden  D.fr_lrgdp D.fr2_lrgdp2 D.renew D.lfoss period*, fe lag(6)

My results from both these models are pretty similar. However, standard error with lag 6 are greater than with lag 7.

Generally, to decide on lag length we use AIC BIC. But, in the post estimation command of XTSCC I couldn't find the option of -estat ic- which we use otherwise. (Question 1). How do I decide which is more appropriate lag length?)

I also thought of determining lag length based on -xtunitroot fisher-

. xtunitroot fisher lrgdp, dfuller trend lags(7)
(551 missing values generated)

Fisher-type unit-root test for lrgdp
Based on augmented Dickey-Fuller tests
--------------------------------------
Ho: All panels contain unit roots           Number of panels       =    133
Ha: At least one panel is stationary        Avg. number of periods =  32.37

AR parameter: Panel-specific                Asymptotics: T -> Infinity
Panel means:  Included
Time trend:   Included
Drift term:   Not included                  ADF regressions: 7 lags
------------------------------------------------------------------------------
                                  Statistic      p-value
------------------------------------------------------------------------------
 Inverse chi-squared(266)  P       349.1656       0.0005
 Inverse normal            Z         2.5666       0.9949
 Inverse logit t(659)      L*        1.5204       0.9356
 Modified inv. chi-squared Pm        3.6057       0.0002
------------------------------------------------------------------------------
 P statistic requires number of panels to be finite.
 Other statistics are suitable for finite or infinite number of panels.
------------------------------------------------------------------------------

. xtunitroot fisher lrgdp, dfuller trend lags(6)
(551 missing values generated)

Fisher-type unit-root test for lrgdp
Based on augmented Dickey-Fuller tests
--------------------------------------
Ho: All panels contain unit roots           Number of panels       =    133
Ha: At least one panel is stationary        Avg. number of periods =  32.37

AR parameter: Panel-specific                Asymptotics: T -> Infinity
Panel means:  Included
Time trend:   Included
Drift term:   Not included                  ADF regressions: 6 lags
------------------------------------------------------------------------------
                                  Statistic      p-value
------------------------------------------------------------------------------
 Inverse chi-squared(266)  P       313.2160       0.0247
 Inverse normal            Z         2.7447       0.9970
 Inverse logit t(654)      L*        1.9702       0.9754
 Modified inv. chi-squared Pm        2.0471       0.0203
------------------------------------------------------------------------------
 P statistic requires number of panels to be finite.
 Other statistics are suitable for finite or infinite number of panels.
------------------------------------------------------------------------------

In this case, two tests suggest reject unit root and two suggest- do not reject. (Question 2). Could you please tell me which one should be the most appropriate test among the 4 (inverse chi sq, inverse normal, inverse logit and modified inv. chi-sq) to decide on lag length.