Econometrics-I-24

# Bu β i i i i t y gives the sign of y y | y is a draw

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Unformatted text preview: Bu β i i i i t, y gives the sign of y * . y * | , y is a draw from the truncated normal (above if y=0, below if y=1) Part 24: Bayesian Estimation Gibbs Sampling for the Probit Model &#152;&#152;™™ ™ 13/34 i i (1) Choose an initial value for (maybe the MLE) (2) Generate y * by sampling N observations from the truncated normal with mean and variance 1, truncated above 0 if y 0, from below if y ′ = i β x β i-1-1 1. (3) Generate by drawing a random normal vector with mean vector ( ) * and variance matrix ( ) (4) Return to 2 10,000 times, retaining the last 5,000 draws - first 5,000 are the = β X'X X'y X'X 'burn in.' (5) Estimate the posterior mean of by averaging the last 5,000 draws. (This corresponds to a uniform prior over .) β β Part 24: Bayesian Estimation Generating Random Draws from f(X) &#152;&#152;™™ ™ 14/34-1 The inverse probability method of sampling random draws: If F(x) is the CDF of random variable x, then a random draw on x may be obtained as F (u) where u is a draw from the standard uniform (0,1). Exampl θ θ θ θ Φ Φ μ Φ Φ μ-1-1 i i es: Exponential: f(x)= exp(- x); F(x)=1-exp(- x) x = -(1/ )log(1-u) Normal: F(x) = (x); x = (u) Truncated Normal: x= + [1-(1-u)* ( )] for y=1; x μ Φ Φ μ-1 i i = + [u (- )] for y=0. Part 24: Bayesian Estimation Example: Simulated Probit ? Generate raw data Sample ; 1 - 1000 \$ Create ; x1=rnn(0,1) ; x2 = rnn(0,1) \$ Create ; ys = .2 + .5*x1 - .5*x2 + rnn(0,1) ; y = ys > 0 \$ Namelist; x=one,x1,x2\$ Matrix ; xx=x'x ; xxi = <xx> \$ Calc ; Rep = 200 ; Ri = 1/Rep\$ Probit ; lhs=y;rhs=x\$ ? Gibbs sampler Matrix ; beta=[0/0/0] ; bbar=init(3,1,0);bv=init(3,3,0)\$\$ Proc = gibbs\$ Do for ; simulate ; r =1,Rep \$ Create ; mui = x'beta ; f = rnu(0,1) ; if(y=1) ysg = mui + inp(1-(1-f)*phi( mui)); (else) ysg = mui + inp( f *phi(-mui)) \$ Matrix ; mb = xxi*x'ysg ; beta = rndm(mb,xxi) ; bbar=bbar+beta ; bv=bv+beta*beta'\$ Enddo ; simulate \$ Endproc \$ Execute ; Proc = Gibbs \$ (Note, did not discard burn-in) Matrix ; bbar=ri*bbar ; bv=ri*bv-bbar*bbar' \$ Matrix ; Stat(bbar,bv); Stat(b,varb) \$ &#152;&#152;™™ ™ 15/34 Part 24: Bayesian Estimation Example: Probit MLE vs. Gibbs--> Matrix ; Stat(bbar,bv); Stat(b,varb) \$ +---------------------------------------------------+ |Number of observations in current sample = 1000 | |Number of parameters computed here = 3 | |Number of degrees of freedom = 997 | +---------------------------------------------------+ +---------+--------------+----------------+--------+---------+ |Variable | Coefficient | Standard Error |b/St.Er.|P[|Z|>z] | +---------+--------------+----------------+--------+---------+...
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