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The Metropolis-Hastings Algorithm
Frank SchorfheideUniversity of Pennsylvania
EABCN Training School
May 10, 2016
Markov Chain Monte Carlo (MCMC)
• Main idea: create a sequence of serially correlated draws such thatthe distribution of θi converges to the posterior distribution p(θ|Y ).
Frank Schorfheide The Metropolis-Hastings Algorithm
Generic Metropolis-Hastings Algorithm
For i = 1 to N:
1 Draw ϑ from a density q(ϑ|θi−1).
2 Set θi = ϑ with probability
α(ϑ|θi−1) = min
1,
p(Y |ϑ)p(ϑ)/q(ϑ|θi−1)
p(Y |θi−1)p(θi−1)/q(θi−1|ϑ)
and θi = θi−1 otherwise.
Recall p(θ|Y ) ∝ p(Y |θ)p(θ).
We draw θi conditional on a parameter draw θi−1: leads to Markovtransition kernel K (θ|θ).
Frank Schorfheide The Metropolis-Hastings Algorithm
Importance Invariance Property
• It can be shown that
p(θ|Y ) =
∫K (θ|θ)p(θ|Y )d θ.
• Write
K (θ|θ) = u(θ|θ) + r(θ)δθ(θ).
• u(θ|θ) is the density kernel (note that u(θ|·) does not integrated toone) for accepted draws:
u(θ|θ) = α(θ|θ)q(θ|θ).
• Rejection probability:
r(θ) =
∫ [1− α(θ|θ)
]q(θ|θ)dθ = 1−
∫u(θ|θ)dθ.
Frank Schorfheide The Metropolis-Hastings Algorithm
Importance Invariance Property
• Reversibility: Conditional on the sampler not rejecting the proposeddraw, the density associated with a transition from θ to θ is identicalto the density associated with a transition from θ to θ:
p(θ|Y )u(θ|θ) = p(θ|Y )q(θ|θ) min
1,
p(θ|Y )/q(θ|θ)
p(θ|Y )/q(θ|θ)
= min
p(θ|Y )q(θ|θ), p(θ|Y )q(θ|θ)
= p(θ|Y )q(θ|θ) min
p(θ|Y )/q(θ|θ)
p(θ|Y )/q(θ|θ), 1
= p(θ|Y )u(θ|θ).
• Using the reversibility result, we can now verify the invarianceproperty:∫
K (θ|θ)p(θ|Y )d θ =
∫u(θ|θ)p(θ|Y )d θ +
∫r(θ)δθ(θ)p(θ|Y )d θ
=
∫u(θ|θ)p(θ|Y )d θ + r(θ)p(θ|Y )
= p(θ|Y )
Frank Schorfheide The Metropolis-Hastings Algorithm
A Discrete Example
• Suppose parameter vector θ is scalar and takes only two values:
Θ = τ1, τ2
• The posterior distribution p(θ|Y ) can be represented by a set ofprobabilities collected in the vector π, say π = [π1, π2] with π2 > π1.
• Suppose we obtain ϑ based on transition matrix Q:
Q =
[q (1− q)
(1− q) q
].
Frank Schorfheide The Metropolis-Hastings Algorithm
Discrete MH Algorithm
• Iteration i : suppose that θi−1 = τj . Based on transition matrix
Q =
[q (1− q)
(1− q) q
],
determine a proposed state ϑ = τs .
• With probability α(τs |τj) the proposed state is accepted. Setθi = ϑ = τs .
• With probability 1− α(τs |τj) stay in old state and set θi = θi−1 = τj .
• Choose (Q terms cancel because of symmetry)
α(τs |τj) = min
1,πsπj
.
Frank Schorfheide The Metropolis-Hastings Algorithm
Discrete MH Algorithm: Transition Matrix
• The resulting chain’s transition matrix is:
K =
[q (1− q)
(1− q)π1
π2q + (1− q)
(1− π1
π2
) ].
• Straightforward calculations reveal that the transition matrix K haseigenvalues:
λ1(K ) = 1, λ2(K ) = q − (1− q)π1
1− π1.
• Equilibrium distribution is eigenvector associated with uniteigenvalue.
• For q ∈ [0, 1) the equilibrium distribution is unique.
Frank Schorfheide The Metropolis-Hastings Algorithm
Convergence
• The persistence of the Markov chain depends on second eigenvalue,which depends on the proposal distribution Q.
• Define the transformed parameter
ξi =θi − τ1τ2 − τ1
.
• We can represent the Markov chain associated with ξi as first-orderautoregressive process
ξi = (1− k22) + λ2(K )ξi−1 + ν i .
• Conditional on ξi = j , j = 0, 1, the innovation ν i has support on kjjand (1− kjj), its conditional mean is equal to zero, and itsconditional variance is equal to kjj(1− kjj).
Frank Schorfheide The Metropolis-Hastings Algorithm
Convergence
• Autocovariance function of h(θi ):
COV (h(θi ), h(θ(i−l)))
=(h(τ2)− h(τ1)
)2π1(1− π1)
(q − (1− q)
π11− π1
)l
= Vπ[h]
(q − (1− q)
π11− π1
)l
• If q = π1 then the autocovariances are equal to zero and the drawsh(θi ) are serially uncorrelated (in fact, in our simple discrete settingthey are also independent).
Frank Schorfheide The Metropolis-Hastings Algorithm
Convergence
• Define the Monte Carlo estimate
hN =1
N
N∑i=1
h(θi ).
• Deduce from CLT√N(hN − Eπ[h]) =⇒ N
(0,Ω(h)
),
where Ω(h) is the long-run covariance matrix
Ω(h) = limL−→∞
Vπ[h]
(1 + 2
L∑l=1
L− l
L
(q − (1− q)
π11− π1
)l).
• In turn, the asymptotic inefficiency factor is given by
InEff∞ =Ω(h)
Vπ[h]= 1 + 2 lim
L−→∞
L∑l=1
L− l
L
(q − (1− q)
π11− π1
)l
.
Frank Schorfheide The Metropolis-Hastings Algorithm
Autocorrelation Function of θi , π1 = 0.2
0 1 2 3 4 5 6 7 8 9
0.0
0.5
1.0
q = 0.00q = 0.20
q = 0.50q = 0.99
Frank Schorfheide The Metropolis-Hastings Algorithm
Asymptotic Inefficiency InEff∞, π1 = 0.2
0.0 0.2 0.4 0.6 0.8 1.010−1
100
101
102
q
Frank Schorfheide The Metropolis-Hastings Algorithm
Small Sample Variance V[hN ] across Chains versus HACEstimates of Ω(h)
10−4 10−3 10−210−5
10−4
10−3
10−2
10−1
Solid line is 45-degree line.
Frank Schorfheide The Metropolis-Hastings Algorithm
Posterior Inference
• We discussed how to solve a DSGE model;
• and how to compute the likelihood function p(Y |θ) for a DSGEmodel.
• According to Bayes Theorem
p(θ|Y ) =p(Y |θ)p(θ)∫p(Y |θ)p(θ)dθ
• We want to generate draws from posterior...
Frank Schorfheide The Metropolis-Hastings Algorithm
Benchmark Random-Walk Metropolis-Hastings (RWMH)Algorithm for DSGE Models
• Initialization:
1 Use a numerical optimization routine to maximize the log posterior,which up to a constant is given by ln p(Y |θ) + ln p(θ). Denote theposterior mode by θ.
2 Let Σ be the inverse of the (negative) Hessian computed at theposterior mode θ, which can be computed numerically.
3 Draw θ0 from N(θ, c20 Σ) or directly specify a starting value.
• Main Algorithm – For i = 1, . . . ,N:
1 Draw ϑ from the proposal distribution N(θi−1, c2Σ).2 Set θi = ϑ with probability
α(ϑ|θi−1) = min
1,
p(Y |ϑ)p(ϑ)
p(Y |θi−1)p(θi−1)
and θi = θi−1 otherwise.
Frank Schorfheide The Metropolis-Hastings Algorithm
Benchmark RWMH Algorithm for DSGE Models
• Initialization steps can be modified as needed for particularapplication.
• If numerical optimization does not work well, one could let Σ be adiagonal matrix with prior variances on the diagonal.
• Or, Σ could be based on a preliminary run of a posterior sampler.
• It is good practice to run multiple chains based on different startingvalues.
• For the subsequent illustrations we chose Σ = Vπ[h], where theposterior variance matrix is obtained from a long MCMC run.
Frank Schorfheide The Metropolis-Hastings Algorithm
Observables for Small-Scale New Keynesian Model
−2−1
012 Quarterly Output Growth
0
4
8 Quarterly Inflation
1985 1990 1995 2000048
12 Federal Funds Rate
Notes: Output growth per capita is measured in quarter-on-quarter(Q-o-Q) percentages. Inflation is CPI inflation in annualized Q-o-Qpercentages. Federal funds rate is the average annualized effective fundsrate for each quarter.
Frank Schorfheide The Metropolis-Hastings Algorithm
Convergence of Monte Carlo Average τN|N0
0.0 0.5 1.0×105
0
1
2
3
4
5
0.0 0.5 1.0×105
0.0 0.5 1.0×105
Notes: The x−axis indicates the number of draws N. N0 is set to 0,25, 000 and 50, 000, respectively.
Frank Schorfheide The Metropolis-Hastings Algorithm
Posterior Estimates of DSGE Model Parameters
Parameter Mean [0.05, 0.95] Parameter Mean [0.05,0.95]τ 2.83 [ 1.95, 3.82] ρr 0.77 [ 0.71, 0.82]κ 0.78 [ 0.51, 0.98] ρg 0.98 [ 0.96, 1.00]ψ1 1.80 [ 1.43, 2.20] ρz 0.88 [ 0.84, 0.92]ψ2 0.63 [ 0.23, 1.21] σr 0.22 [ 0.18, 0.26]r (A) 0.42 [ 0.04, 0.95] σg 0.71 [ 0.61, 0.84]π(A) 3.30 [ 2.78, 3.80] σz 0.31 [ 0.26, 0.36]γ(Q) 0.52 [ 0.28, 0.74]
Notes: We generated N = 100, 000 draws from the posterior anddiscarded the first 50,000 draws. Based on the remaining draws weapproximated the posterior mean and the 5th and 95th percentiles.
Frank Schorfheide The Metropolis-Hastings Algorithm
Effect of Scaling Constant c
0.0
0.5
1.0 Acceptance Rate α
100
102
104 InEff∞
0.0 0.5 1.0 1.5 2.0100102104106
c
InEffN
Notes: Results are based on Nrun = 50 independent Markov chains. Theacceptance rate (average across multiple chains), HAC-based estimate ofInEff∞[τ ] (average across multiple chains), and InEffN [τ ] are shown as afunction of the scaling constant c .
Frank Schorfheide The Metropolis-Hastings Algorithm
Acceptance Rate α versus Inaccuracy InEffN
0.0 0.2 0.4 0.6 0.8 1.0Acceptance Rate α
101
102
103
104
105
InE
ff N
Notes: InEffN [τ ] versus the acceptance rate α.
Frank Schorfheide The Metropolis-Hastings Algorithm
Impulse Responses of Exogenous Processes
0 2 4 6 8 100.0
0.2
0.4
0.6
0.8
1.0
t
Response of gt to εg,t
0 2 4 6 8 10t
Response of zt to εz,t
Notes: The figure depicts pointwise posterior means and 90% crediblebands. The responses are in percent relative to the initial level.
Frank Schorfheide The Metropolis-Hastings Algorithm
Parameter Transformations: Impulse Responses
0.00.20.40.60.8
Out
put
εg,t
-0.1
0.1
0.3
0.5
0.7εz,t
-0.2
-0.1
0.0εr ,t
-2.0
-1.0
0.0
1.0
Infla
tion
-0.20.20.61.01.4
-1.0
-0.6
-0.2
0 2 4 6 8 10-1.0
0.0
1.0
Fede
ralF
unds
Rat
e
0 2 4 6 8 10-0.2
0.2
0.6
1.0
1.4
0 2 4 6 8 100.0
0.4
0.8
Notes: The figure depicts pointwise posterior means and 90% crediblebands. The responses of output are in percent relative to the initial level,whereas the responses of inflation and interest rates are in annualizedpercentages.
Frank Schorfheide The Metropolis-Hastings Algorithm
Challenges Due to Irregular Posteriors
• A stylized state-space model:
yt = [1 1]st , st =
[φ1 0φ3 φ2
]st−1 +
[10
]εt , εt ∼ iidN(0, 1).
where
• Structural parameters θ = [θ1, θ2]′, domain is unit square.
• Reduced-form parameters φ = [φ1, φ2, φ3]′
φ1 = θ21, φ2 = (1− θ21), φ3 − φ2 = −θ1θ2.
Frank Schorfheide The Metropolis-Hastings Algorithm
Challenges Due to Irregular Posteriors
• s1,t looks like an exogenous technology process.
• s2,t evolves like an endogenous state variable, e.g., the capital stock.
• θ2 is not identifiable if θ1 = 0 because θ2 enters the model onlymultiplicatively.
• Law of motion of yt is restricted ARMA(2,1) process:(1− θ21L
)(1− (1− θ21)L
)yt =
(1− θ1θ2L
)εt .
• Given θ1 and θ2, we obtain an observationally equivalent process byswitching the values of the two roots of the autoregressive lagpolynomial.
• Choose θ1 and θ2 such that
θ1 =√
1− θ21, θ2 = θ1θ2/θ1.
Frank Schorfheide The Metropolis-Hastings Algorithm
Posteriors for Stylized State-Space Model
Local Identification Problem Global Identification Problem
0.0 0.1 0.2 0.3 0.4 0.5
0.2
0.4
0.6
0.8
θ2
θ10.2 0.4 0.6 0.8
0.2
0.4
0.6
0.8
θ2
θ1
Notes: Intersections of the solid lines indicate parameter values that wereused to generate the data from which the posteriors are constructed. Leftpanel: θ1 = 0.1 and θ2 = 0.5. Right panel: θ1 = 0.8, θ2 = 0.3.
Frank Schorfheide The Metropolis-Hastings Algorithm
Improvements to MCMC: Blocking
• In high-dimensional parameter spaces the RWMH algorithmgenerates highly persistent Markov chains.
• What’s bad about persistence?√N(hN − E[hN ])
=⇒ N
(0,
1
N
n∑i=1
V[h(θi )] +1
N
N∑i=1
∑j 6=i
COV[h(θi ), h(θj)
]).
• Potential Remedy:• Partition θ = [θ1, . . . , θK ].• Iterate over conditional posteriors p(θk |Y , θ<−k>).
• To reduce persistence of the chain, try to find partitions such thatparameters are strongly correlated within blocks and weaklycorrelated across blocks or use random blocking.
Frank Schorfheide The Metropolis-Hastings Algorithm
Block MH Algorithm
Draw θ0 ∈ Θ and then for i = 1 to N:
1 Create a partition B i of the parameter vector into Nblocks blocksθ1, . . . , θNblocks
via some rule (perhaps probabilistic), unrelated to thecurrent state of the Markov chain.
2 For b = 1, . . . ,Nblocks :
1 Draw ϑb ∼ q(·|[θi<b, θ
i−1b , θi−1
≥b
]).
2 With probability,
α = max
p([θi<b, ϑb, θ
i−1>b
]|Y )q(θi−1
b , |θi<b, ϑb, θi−1>b )
p(θi<b, θi−1b , θi−1
>b |Y )q(ϑb|θi<b, θi−1b , θi−1
>b ), 1
,
set θib = ϑb, otherwise set θib = θi−1b .
Frank Schorfheide The Metropolis-Hastings Algorithm
Random-Block MH Algorithm
1 Generate a sequence of random partitions B iNi=1 of the parametervector θ into Nblocks equally sized blocks, denoted by θb,b = 1, . . . ,Nblocks as follows:
1 assign an iidU[0, 1] draw to each element of θ;
2 sort the parameters according to the assigned random number;
3 let the b’th block consist of parameters (b − 1)Nblocks , . . . , bNblocks .1
2 Execute Algorithm Block MH Algorithm.
1If the number of parameters is not divisible by Nblocks , then the size of a subset ofthe blocks has to be adjusted.
Frank Schorfheide The Metropolis-Hastings Algorithm
Metropolis-Adjusted Langevin Algorithm
• The proposal distribution of Metropolis-Adjusted Langevin (MAL)algorithm is given by
µ(θi−1) = θi−1 +c12M1
∂
∂θln p(θi−1|Y )
∣∣∣∣θ=θi−1
,
Σ(θi−1) = c22M2.
that is θi−1 is adjusted by a step in the direction of the gradient ofthe log posterior density function.
• One standard practice is to set M1 = M2 = M, with
M = −[
∂
∂θ∂θ′ln p(θ|Y )
∣∣∣∣θ=θ
]−1,
where θ is the mode of the posterior distribution obtained using anumerical optimization routine.
Frank Schorfheide The Metropolis-Hastings Algorithm
Newton MH Algorithm
• Newton MH Algorithm replaces the Hessian evaluated at theposterior mode θ by the Hessian evaluated at θi−1.
• The proposal distribution is given by
µ(θi−1) = θi−1 − s
[∂
∂θ∂θ′ln p(θ|Y )
∣∣∣∣θ=θi−1
]−1× ∂
∂θln p(θi−1|Y )
∣∣∣∣θ=θi−1
Σ(θi−1) = −c22[
∂
∂θ∂θ′ln p(θ|Y )
∣∣∣∣θ=θi−1
]−1.
• It is useful to let s be independently of θi−1:
c1 = 2s, s ∼ iidU[0, s],
where s is a tuning parameter.
Frank Schorfheide The Metropolis-Hastings Algorithm
Run Times and Tuning Constants for MH Algorithms
Algorithm Run Time Acceptance Tuning[hh:mm:ss] Rate Constants
1-Block RWMH-I 00:01:13 0.28 c = 0.0151-Block RWMH-V 00:01:13 0.37 c = 0.4003-Block RWMH-I 00:03:38 0.40 c = 0.0703-Block RWMH-V 00:03:36 0.43 c = 1.2003-Block MAL 00:54:12 0.43 c1 = 0.400, c2 = 0.7503-Block Newton MH 03:01:40 0.53 s = 0.700, c2 = 0.600
Notes: In each run we generate N = 100, 000 draws. We report thefastest run time and the average acceptance rate across Nrun = 50independent Markov chains.
Frank Schorfheide The Metropolis-Hastings Algorithm
Autocorrelation Function of τ i
0 5 10 15 20 25 30 35 40−0.2
0.00.20.40.60.81.01.2
1-Block RWMH-V1-Block RWMH-I
3-Block RWMH-V3-Block RWMH-I
3-Block MAL3-Block Newton MH
Notes: The autocorrelation functions are computed based on a single runof each algorithm.
Frank Schorfheide The Metropolis-Hastings Algorithm
Inefficiency Factor InEffN [τ ]
3-BlockMAL
3-BlockNewton MH
3-BlockRWMH-V
1-BlockRWMH-V
3-BlockRWMH-I
1-BlockRWMH-I
100
101
102
103
104
105
Notes: The small-sample inefficiency factors are computed based onNrun = 50 independent runs of each algorithm.
Frank Schorfheide The Metropolis-Hastings Algorithm
IID Equivalent Draws Per Second
iid-equivalent draws per second =N
Run Time [seconds]· 1
InEffN.
Algorithm Draws Per Second1-Block RWMH-V 7.763-Block RWMH-V 5.653-Block MAL 1.243-Block RWMH-I 0.143-Block Newton MH 0.131-Block RWMH-I 0.04
Frank Schorfheide The Metropolis-Hastings Algorithm
Performance of Different MH Algorithms
RWMH-V (1 Block) RWMH-V (3 Blocks)
10−10 10−8 10−6 10−4 10−2 10010−10
10−8
10−6
10−4
10−2
100
10−10 10−8 10−6 10−4 10−2 10010−10
10−8
10−6
10−4
10−2
100
MAL Newton
10−10 10−8 10−6 10−4 10−2 10010−10
10−8
10−6
10−4
10−2
100
10−10 10−8 10−6 10−4 10−2 10010−10
10−8
10−6
10−4
10−2
100
Notes: Each panel contains scatter plots of the small sample varianceV[θ] computed across multiple chains (x-axis) versus the HAC[h]estimates of Ω(θ)/N (y -axis).
Frank Schorfheide The Metropolis-Hastings Algorithm
Recall: Posterior Odds and Marginal Data Densities
• Posterior model probabilities can be computed as follows:
πi,T =πi,0p(Y |Mi )∑j πj,0p(Y |Mj)
, j = 1, . . . , 2, (1)
• where
p(Y |M) =
∫p(Y |θ,M)p(θ|M)dθ (2)
• Note:
ln p(Y1:T |M) =T∑t=1
ln
∫p(yt |θ,Y1:t−1,M)p(θ|Y1:t−1,M)dθ
• Posterior odds and Bayes Factor
π1,Tπ2,T
=π1,0π2,0︸︷︷︸
Prior Odds
× p(Y |M1)
p(Y |M2)︸ ︷︷ ︸Bayes Factor
(3)
Frank Schorfheide The Metropolis-Hastings Algorithm
Computation of Marginal Data Densities
• Reciprocal importance sampling:
• Geweke’s modified harmonic mean estimator
• Sims, Waggoner, and Zha’s estimator
• Chib and Jeliazkov’s estimator
• For a survey, see Ardia, Hoogerheide, and van Dijk (2009).
Frank Schorfheide The Metropolis-Hastings Algorithm
Modified Harmonic Mean
• Reciprocal importance samplers are based on the following identity:
1
p(Y )=
∫f (θ)
p(Y |θ)p(θ)p(θ|Y )dθ, (4)
where∫f (θ)dθ = 1.
• Conditional on the choice of f (θ) an obvious estimator is
pG (Y ) =
[1
N
N∑i=1
f (θi )
p(Y |θi )p(θi )
]−1, (5)
where θi is drawn from the posterior p(θ|Y ).
• Geweke (1999):
f (θ) = τ−1(2π)−d/2|Vθ|−1/2 exp[−0.5(θ − θ)′V−1θ (θ − θ)
]×
(θ − θ)′V−1θ (θ − θ) ≤ F−1χ2d
(τ). (6)
Frank Schorfheide The Metropolis-Hastings Algorithm
Chib and Jeliazkov
• Rewrite Bayes Theorem:
p(Y ) =p(Y |θ)p(θ)
p(θ|Y ). (7)
• Thus,
pCS(Y ) =p(Y |θ)p(θ)
p(θ|Y ), (8)
where we replaced the generic θ in (7) by the posterior mode θ.
Frank Schorfheide The Metropolis-Hastings Algorithm
Chib and Jeliazkov
• Use output of Metropolis-Hastings Algorithm.
• Proposal density for transition θ 7→ θ: q(θ, θ|Y ).
• Probability of accepting proposed draw:
α(θ, θ|Y ) = min
1,
p(θ|Y )/q(θ, θ|Y )
p(θ|Y )/q(θ, θ|Y )
.
• Note that∫α(θ, θ|Y )q(θ, θ|Y )p(θ|Y )dθ
=
∫min
1,
p(θ|Y )/q(θ, θ|Y )
p(θ|Y )/q(θ, θ|Y )
q(θ, θ|Y )p(θ|Y )dθ
= p(θ|Y )
∫min
p(θ|Y )/q(θ, θ|Y )
p(θ|Y )/q(θ, θ|Y ), 1
q(θ, θ|Y )dθ
= p(θ|Y )
∫α(θ, θ|Y )q(θ, θ|Y )dθ
Frank Schorfheide The Metropolis-Hastings Algorithm
Chib and Jeliazkov
• Posterior density at the mode can be approximated as follows
p(θ|Y ) =1N
∑Ni=1 α(θi , θ|Y )q(θi , θ|Y )1J
∑Jj=1 α(θ, θj |Y )
, (9)
• θi are posterior draws obtained with the the M-H Algorithm;
• θj are additional draws from q(θ, θ|Y ) given the fixed value θ.
Frank Schorfheide The Metropolis-Hastings Algorithm
MH-Based Marginal Data Density Estimates
Model Mean(ln p(Y )) Std. Dev.(ln p(Y ))Geweke (τ = 0.5) -346.17 0.03Geweke (τ = 0.9) -346.10 0.04SWZ (q = 0.5) -346.29 0.03SWZ (q = 0.9) -346.31 0.02Chib and Jeliazkov -346.20 0.40
Notes: Table shows mean and standard deviation of log marginal datadensity estimators, computed over Nrun = 50 runs of the RWMH-Vsampler using N = 100, 000 draws, discarding a burn-in sample ofN0 = 50, 000 draws. The SWZ estimator uses J = 100, 000 draws tocompute τ , while the CJ estimators uses J = 100, 000 to compute thedenominator of p(θ|Y ).
Frank Schorfheide The Metropolis-Hastings Algorithm