Bayesian Estimation dseries /extract

I have tried to estimate my model through bayesian Maximum likelihood method, but dynare is generating
Error using dseries/extract (line 61)
dseries::extract: Variable y is not a member of A!
Error in dseries/subsref (line 236)
B = extract(A,S(1).subs{:});
Error in makedataset (line 141)
DynareDataset = DynareDataset{DynareOptions.varobs{:}};
Error in dynare_estimation_init (line 538)
[dataset_, dataset_info, newdatainterfaceflag] = makedataset(options_,
options_.dsge_var*options_.dsge_varlag, gsa_flag);
Error in dynare_estimation_1 (line 116)
dynare_estimation_init(var_list_, dname, [], M_, options_, oo_, estim_params_,
Error in dynare_estimation (line 105)
Error in new2 (line 761)
Error in dynare (line 235)
evalin(‘base’,fname) ;
I have log HP filter transformed time series data the variables in the excel file are in the same order as they are in the matlab code. I am unable to understand what is wrong here. I have attached the data file as well as the mod file in this email please guide me in this regard. I will be grateful for the (40.0 KB)

Your xls_range does not include the top row with the variable names. Dynare is unable to tell which variable is which.

Thank you so much professor for the guidance. I have made this adjustment and tried again but there was some errors again and I followed the advice given by your goodself in 2018 regarding the usage of Use_Calibration command and drop some observable to tackle the issue which I did and obtained the appended results a few things are strange here. Please have a look and guide what can be done to improve the outcome. I will be grateful for the guidance.
Estimation Outcome.pdf (111.0 KB) estimation1.mod (12.8 KB)

Again, if you have a linearized model, why are your steady states not 0?

Dear Professor jpfeifer,
Thank you so much for the reply, I have tried to make them exect zero by changing the steady state values and the parameters values but couldn’t do that. Please guide me how can we achieve this milestone and is it the main reason behind such estimates.

This indicates that your linearization is wrong. Please start with a simpler model.

Dear Professor,
Thank you so much for the guidance, I will start from a simple one.