pmpp: Posterior Mean Panel Predictor for dynamic panel modelling
In pmpp: Posterior Mean Panel Predictor

Description Usage Arguments Details Value Author(s) References Examples

This function estimates parameters of the Posterior Mean Panel Predictor (PMPP) model based on an empirical-Bayes approach to obtain unit-specific fixed effects.

pmpp(dep_var, data, panel_ind = colnames(data[, 1:2]), exp_var = NULL,
  csi_var = NULL, post_mean_method = "gaussian",
  common_par_method = "QMLE", optim_method = "quadratic",
  dens_grid = 2^10, gmm_model = "twosteps", gmm_inst = 99,
  pure_data = FALSE)

`dep_var`	character string indicating name of dependent variable
`data`	data.frame or matrix with input data
`panel_ind`	vector of length 2 indicating names of variables indexing units and time periods respectively
`exp_var`	vector of character strings indicating names of exogeneous explanatory variables
`csi_var`	vector of character strings indicating names of cross-sectionally invariant explanatory variables; feature not supported yet
`post_mean_method`	method for estimating the heterogeneous intercept parameters, one of "gaussian", "kernel"
`common_par_method`	method for estimating the common parameters, one of "QMLE", "GMM_ABond", "GMM_BBond", GMM_ABover", "GMM_SSYS"
`optim_method`	which optimisation routine to use, one of "gradient", "quadratic", "annealing"
`dens_grid`	size of the grid over which data is interpolated for kernel density estimation; larger value may yield higher accuracy, but increases computation time
`gmm_model`	number of steps for computing optimal GMM matrix, one of "onestep", "twosteps", "threesteps"; "threesteps" can be used for "GMM_SSYS" only
`gmm_inst`	number of lagged values of the dependent variable to be used as GMM instruments in Arellano-Bond/Blundell-Bond setting
`pure_data`	if TRUE, removes indexing/subsetting from model's call on data, facilitating use in a loop

The PMPP model is a two-step procedure. First, the homogeneous parameters are estimated using one of the QMLE or GMM-based methods:

Arellano-Bond estimator (Difference GMM),
Arellano-Bover estimator (Level GMM),
Blundell-Bond estimator (System GMM),
Sub-optimal System GMM estimator,
Quasi-Maximum Likelihood estimator.

Parameter common_par_method can be used to select the method for common parameters estimation. All the above methods only provide estimates of the homogeneous parameters, i.e. the ones measuring impact of lagged response and external variables. The intercept is removed in the estimation process. In the second step of the PMPP modelling, the individual-specific intercept is calculated based on the formula for posterior mean (Tweedie's Formula). It involves approximating certain density function, which can be done in two ways:

Parametrically, assuming Gaussian distribution,
Using a 2D kernel density estimator.

Parameter post_mean_method can be used to select the method used for intercept estimation. For technical details on the methods, see the references.

An object of class pmpp; a list with parameter estimates, fitted values, residuals, in-sample error measures and information on the data and function call.

Michal Oleszak

Liu et al. (2016), "Forecasting with Dynamic Panel Data Models", PIER Working Paper No. 16022., https://papers.ssrn.com/sol3/Papers.cfm?abstract_id=2889000

Oleszak, M. (2018). "Forecasting sales with micro-panels: Empirical Bayes approach. Evidence from consumer goods sector.", Erasmus University Thesis Repository

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data(EmplUK, package = "plm")
EmplUK <- dplyr::filter(EmplUK, year %in% c(1978, 1979, 1980, 1981, 1982))
pmpp_model <- pmpp(dep_var = "emp", data = EmplUK)