用贝叶斯法估计dsge模型参数，显示Impossible to find the steady state (the sum of square residuals of the static equations is 23015.1716). Either the model doesn't have a steady state, there are an infinity of steady states, or the guess values are too far from the solution.改了几次参数初值还是这样，是不是模型有问题，新手小白不太会，有大佬帮忙看看吗，感谢！假设有价格粘性，垄断竞争市场，家庭部门同时向中间品厂商和金融科技公司提供劳动。代码如下
var Y Y_obs I I_obs Cn Cn_obs A_m A_f C C_m C_f D N_f N_m w_m w_f R R_l R_a L pi P P_star K_m K_f I_m I_f Y_m v eta mc n;
varexo e_a e_af e_n;  
parameters beta chi phi beta_m delta alpha X theta psi theta_1 theta_2 rho_n x_n beta_f rho_a rho_af eta_ss rho_n;
beta=0.98;
chi=0.31;
phi=1;
beta_m=0.94;
delta=0.5;
alpha=0.5;
X=0.5;
theta=0.75;
psi=1.2;
theta_1=0.1;
theta_2=0.11;
rho_n=0.9;
x_n=0.5;
beta_f=0.94;
rho_a=0.8;
rho_af=0.9;
eta_ss=0.131;
rho_n=0.6;


model;

% (1) 家庭部门
C=1/(chi/D+beta*(1+R)/(C(+1)*pi(+1)));  对消费的一节条件
w_m=(N_m)^phi*C;  劳动供给
w_f=(N_f)^phi*C;  劳动供给
pi=P/P(-1);  通货膨胀率

%(2)中间品厂商
w_m=(1-alpha)*(A_m*N_m^(-alpha)*K_m(-1)^alpha)/X;  劳动需求一阶条件
log(A_m)=rho_a*log(A_m(-1))+e_a;  全要素生产率
Y_m=A_m*N_m^(1-alpha)*K_m(-1)^alpha; 生产函数
K_m=(1-delta)*K_m(-1)+I_m; 资本存量
C_m=pi(+1)/(beta_m*R_l(+1)); 消费一阶条件
1=C_m/C_m(+1)*beta_m*(1-delta-(alpha*A_m(+1)*N_m(+1)^(1-alpha)*K_m^(alpha-1))/X);  贷款一阶条件
C_m+R_l*L(-1)/pi+w_m*N_m+I_m=Y_m/X+L;  预算约束

%(3)最终品厂商
P^(1-psi)=theta*P(-1)^(1-psi)+(1-theta)*(P_star)^(1-psi);
P_star=(psi/(psi-1))*1/(1-beta*theta)*mc;
mc=1/C_m;

%银行
1=beta*C/C(+1)*(1+R)+theta_1/(1+theta_2*((1-eta_ss)*(L+v-D)));  存款一阶条件
1=beta*C/C(+1)*(R_l+1-delta)+theta_1*(1-eta_ss)/(1+theta_2*(1-eta_ss)*(L+v-D));  存款一阶条件
1=beta*C/C(+1)*(1+R_a)+theta_1*(1-eta_ss)/(1+theta_2*(1-eta_ss)*(L+v-D));  金融产品一阶条件



%(4)金融科技公司
w_f=(1-alpha)*A_f*(N_f)^(-alpha)*K_f(-1)^alpha;
K_f=(1-delta)*K_f(-1)+I_f;
v=A_f*(N_f)^(1-alpha)*(K_f(-1))^alpha;
log(A_f)=rho_af*log(A_f)+e_af;
1=C_f/C_f(+1)*beta_f*(1-delta+alpha*A_f(+1)*N_f(+1)^(1-alpha)*K_f^(alpha-1));
C_f+w_f*N_f+I_f=v;

%(5)审慎政策
eta=eta_ss^(1-rho_n)*eta(-1)^rho_n*(L/L(-1)-Y/Y(+1))^(x_n*(1-rho_n))*n;
log(n)=rho_n*log(n(-1))+e_n;

%均衡条件
Y=Y_m+v;
I=I_m+I_f;
Cn=C+C_m+C_f;
Y=Cn+I-theta_1*log(1+theta_2*(L+v-D-eta_ss*(L+v)));

%观测方程
Y_obs=log(Y)-log(Y(-1));
I_obs=log(I)-log(I(-1));
Cn_obs=log(Cn)-log(Cn(-1));

end;

%贝叶斯参数估计


estimated_params;
rho_a,beta_pdf,0.8,0.02;
rho_af,beta_pdf,0.9,0.02;
beta_f,beta_pdf,0.94,0.02;
rho_n,beta_pdf,0.6,0.02;
stderr e_a,inv_gamma2_pdf,0.0297,inf;
stderr e_af,inv_gamma2_pdf,0.0397,inf;
stderr e_n,inv_gamma2_pdf,0.015,inf;
end;

estimated_params_init;
rho_a,0.8;
rho_af,0.8;
beta_f,0.94;
rho_n,0.6;
stderr e_a,0.0297;
stderr e_af,0.0397;
stderr e_n,0.015;
end;

varobs Y_obs I_obs Cn_obs;

estimation(datafile=sj,mode_compute=6,mh_replic=20000,mh_jscale=0.8,mode_check,bayesian_irf) Y I Cn;