testpanelSubjectBreak {MCMCpack} | R Documentation

## A Test for the Subject-level Break using a Unitivariate Linear Regression Model with Breaks

### Description

testpanelSubjectBreak fits a unitivariate linear regression model with parametric breaks using panel residuals to test the existence of subject-level breaks in panel residuals. The details are discussed in Park (2011).

### Usage

    testpanelSubjectBreak(
      subject.id,
      time.id,
      resid,
      max.break = 2,
      minimum = 10,
      mcmc = 1000,
      burnin = 1000,
      thin = 1,
      verbose = 0,
      b0,
      B0,
      c0,
      d0,
      a = NULL,
      b = NULL,
      seed = NA,
      Time = NULL,
      ps.out = FALSE,
      ...
    )

### Arguments

`subject.id` | A numeric vector indicating the group number. It should start from 1.
`time.id` | A numeric vector indicating the time unit. It should start from 1.
`resid` | A vector of panel residuals.
`max.break` | An upper bound of break numbers for the test.
`minimum` | A minimum length of time series for the test. The test will skip a subject with a time series shorter than this.
`mcmc` | The number of MCMC iterations after burn-in.
`burnin` | The number of burn-in iterations for the sampler.
`thin` | The thinning interval used in the simulation. The number of MCMC iterations must be divisible by this value.
`verbose` | A switch which determines whether or not the progress of the sampler is printed to the screen. If `verbose` is greater than 0, the iteration number and the posterior density samples are printed to the screen every `verbose`th iteration.
`b0` | The prior mean of the residual mean.
`B0` | The prior precision of the residual variance
`c0` | c_0/2 is the shape parameter for the inverse Gamma prior on σ^2. The amount of information in the inverse Gamma prior is something like that from c_0 pseudo-observations.
`d0` | d_0/2 is the scale parameter for the inverse Gamma prior on σ^2.
`a` | a is the shape1 beta prior for transition probabilities. By default, the expected duration is computed and corresponding a and b values are assigned. The expected duration is the sample period divided by the number of states.
`b` | b is the shape2 beta prior for transition probabilities. By default, the expected duration is computed and corresponding a and b values are assigned. The expected duration is the sample period divided by the number of states.
`seed` | The seed for the random number generator. If NA, current R system seed is used.
`Time` | Times of the observations. This will be used to find the time of the first observations in panel residuals.
`ps.out` | If ps.out == TRUE, state probabilities are exported. If the number of panel subjects is huge, users can turn it off to save memory.
`...` | further arguments to be passed

### Details

`testpanelSubjectBreak` fits a univariate linear regression model for subject-level residuals from a panel model. The details are discussed in Park (2011).

The model takes the following form:

e_{it} = α_{im} + \varepsilon_{it}\;\; m = 1, …, M

The errors are assumed to be time-varying at the subject level:

\varepsilon_{it} \sim \mathcal{N}(0, σ^2_{im})

We assume standard, semi-conjugate priors:

β \sim \mathcal{N}(b_0,B_0^{-1})

And:

σ^{-2} \sim \mathcal{G}amma(c_0/2, d_0/2)

Where β and σ^{-2} are assumed a priori independent.

And:

p_{mm} \sim \mathcal{B}eta(a, b),\;\; m = 1, …, M

Where M is the number of states.

OLS estimates are used for starting values.

### Value

The returned object is a matrix containing log marginal likelihoods for all HMMs. The dimension of the returned object is the number of panel subjects by max.break + 1. If psout == TRUE, the returned object has an array attribute `psout` containing state probabilities for all HMMs.

### References

Jong Hee Park, 2012. “Unified Method for Dynamic and Cross-Sectional Heterogeneity: Introducing Hidden Markov Panel Models.” American Journal of Political Science.56: 1040-1054. <doi: 10.1111/j.1540-5907.2012.00590.x>

Siddhartha Chib. 1998. “Estimation and comparison of multiple change-point models.” Journal of Econometrics. 86: 221-241. <doi: 10.1080/01621459.1995.10476635>

### Examples

    ## Not run:
      set.seed(1974)
      N <- 30
      T <- 80
      NT <- N*T

      ## true parameter values
      true.beta <- c(1, 1)
      true.sigma <- 3
      x1 <- rnorm(NT)
      x2 <- runif(NT, 2, 4)

      ## group-specific breaks
      break.point = rep(T/2, N); break.sigma=c(rep(1, N));
      break.list <- rep(1, N)

      X <- as.matrix(cbind(x1, x2), NT, );
      y <- rep(NA, NT)
      id  <-  rep(1:N, each=NT/N)
      K <-  ncol(X);
      true.beta <- as.matrix(true.beta, K, 1)

      ## compute the break probability
      ruler <- c(1:T)
      W.mat <- matrix(NA, T, N)
      for (i in 1:N){
        W.mat[, i] <- pnorm((ruler-break.point[i])/break.sigma[i])
      }
      Weight <- as.vector(W.mat)

      ## draw time-varying individual effects and sample y
      j = 1
      true.sigma.alpha <- 30
      true.alpha1 <- true.alpha2 <- rep(NA, N)
      for (i in 1:N){
        Xi <- X[j:(j+T-1), ]
        true.mean <- Xi  %*% true.beta
        weight <- Weight[j:(j+T-1)]
        true.alpha1[i] <- rnorm(1, 0, true.sigma.alpha)
        true.alpha2[i] <- -1*true.alpha1[i]
        y[j:(j+T-1)] <- ((1-weight)*true.mean + (1-weight)*rnorm(T, 0, true.sigma) +
        		    (1-weight)*true.alpha1[i]) +
        		    (weight*true.mean + weight*rnorm(T, 0, true.sigma) + weight*true.alpha2[i])
        j <- j + T
      }

      ## extract the standardized residuals from the OLS with fixed-effects
      FEols <- lm(y ~ X + as.factor(id) -1 )
      resid.all <- rstandard(FEols)
      time.id <- rep(1:80, N)

      ## model fitting
      G <- 1000
      BF <- testpanelSubjectBreak(subject.id=id, time.id=time.id,
             resid= resid.all, max.break=3, minimum = 10,
             mcmc=G, burnin = G, thin=1, verbose=G,
             b0=0, B0=1/100, c0=2, d0=2, Time = time.id)

      ## estimated break numbers
      ## thresho
      estimated.breaks <- make.breaklist(BF, threshold=3)

      ## print all posterior model probabilities
      print(attr(BF, "model.prob"))

    ## End(Not run)

[Package MCMCpack version 1.5-0 Index]