Dear Professor Pfeifer,

I have ecountered a problem when doing the Bayestion estimation. Would you please give some advice? Thank you very much.

(1) When I put ‘‘identification;’’ before estimation, the result displays as

==== Identification analysis ====

Testing current parameter values

The number of moments with non-zero derivative is smaller than the number of parameters

Try increasing ar = 2

The number of moments with non-zero derivative is smaller than the number of parameters

Try increasing ar = 3

The number of moments with non-zero derivative is smaller than the number of parameters

Try increasing ar = 4

The number of moments with non-zero derivative is smaller than the number of parameters

Try increasing ar = 5

The number of moments with non-zero derivative is smaller than the number of parameters

Try increasing ar = 6

The number of moments with non-zero derivative is smaller than the number of parameters

Try increasing ar = 7

The number of moments with non-zero derivative is smaller than the number of parameters

Try increasing ar = 8

The number of moments with non-zero derivative is smaller than the number of parameters

Try increasing ar = 9

The number of moments with non-zero derivative is smaller than the number of parameters

Try increasing ar = 10

The number of moments with non-zero derivative is smaller than the number of parameters

up to 10 lags: check your model

Either further increase ar or reduce the list of estimated parameters

What does this mean and what should I do to improve it ?

And today when I run the code in dynare 4.5.6 (the results above are in dynare 4.4.3), it displays:

There are NaN’s in the theoretical moments: make sure that for

non-stationary models stationary transformations of

non-stationary observables are used for checking identification.

[TIP: use first differences].

What does this mean?

(2) If I want to choose labor ( L in Y=A*K^alpha*L^(1-alpha) ) as observed variable, which data should I use? Is the number of employments appropriate?

Any reply will be appreciated. Thank you!