finding best fit model using AIC in Panel Data using R

Question

I am currently trying to find a best model using R for Panel Data. I have a project on Corporate Governance in which I collected data of various companies from 2009-2014. I found the best fit using Backward Elimination and Forward Selection using T values in R which concur with themselves (I think its expected as well). But, after reading various articles in academic community I realize T values can give dubious results and to gain any sort of credibility for my results it will be best to use AIC or BIC criteria (even though its not a big improvement by the looks of things in terms of credibility but still its some improvement). As I understand, the definition of AIC is as follows:

AIC = -2log-likelihood+2p (for BIC it will be plogn)

For normal data with no time series, R has a pre-defined function stepAIC which selects variables according to AIC values till AIC value cannot decrease.

But for panel data I havent found any way to find AIC value. I have tried searching for this but in vain.

Stepwise regression should be avoided, for reasons that are well documented on this site. It is not only not good for prediction; it distorts all aspects of statistical inference. — Frank Harrell, Apr 28 '19 at 12:09

Alexandre Ludolf · Answer 1 · 2016-08-04T17:50:59.390

1

Hi I know it is an old Question but i had a similar problem:

I ended up using the lmer/lme4 packages and I was able to force option="ML" to force the use of the conventional Maximum likelihood function, instead of using the REML (restricted maximum likelihood) default option. so stepAIC from mass package could optimize by AIC.

edited Aug 04 '16 at 17:50

answered Aug 04 '16 at 15:33

Alexandre Ludolf

11
3

What do you mean by force a ML function? – mdewey Aug 04 '16 at 15:58
mdewey - rlme functions by defaust uses “restricted maximum likelihood” method (REML) and I couldnt find a way to optimize by AIC using this REML default option. but I was able to run the optimization by AIC if setting the option="ML", using the standar Maximum likelihood function. please refer to: http://stats.stackexchange.com/questions/48671/what-is-restricted-maximum-likelihood-and-when-should-it-be-used – Alexandre Ludolf Aug 04 '16 at 17:49

score 0 · Answer 2 · answered Apr 29 '21 at 13:56

I found this some years ago (I don't remember where):

aicbic_plm <- function(object, criterion) {
  
  
  # object is "plm", "panelmodel" 
  # Lets panel data has index :index = c("Country", "Time")
  
  sp = summary(object)
  
  if(class(object)[1]=="plm"){
    u.hat <- residuals(sp) # extract residuals
    df <- cbind(as.vector(u.hat), attr(u.hat, "index"))
    names(df) <- c("resid", "Country", "Time")
    c = length(levels(df$Country)) # extract country dimension 
t = length(levels(df$Time)) # extract time dimension 
    np = length(sp$coefficients[,1]) # number of parameters
n.N = nrow(sp$model) # number of data
    s.sq  <- log( (sum(u.hat^2)/(n.N))) # log sum of squares
    
    # effect = c("individual", "time", "twoways", "nested"),
    # model = c("within", "random", "ht", "between", "pooling", "fd")
    
    # I am making example only with some of the versions:
    
    if (sp$args$model == "within" & sp$args$effect == "individual"){
      n = c
      np = np+n+1 # update number of parameters
    }
    
    if (sp$args$model == "within" & sp$args$effect == "time"){
      T = t
      np = np+T+1 # update number of parameters
    }
    
    if (sp$args$model == "within" & sp$args$effect == "twoways"){
      n = c
      T = t
      np = np+n+T # update number of parameters
    }
    aic <- round(       2*np  +  n.N * (  log(2*pi) + s.sq  + 1 ),1)
    bic <- round(log(n.N)*np  +  n.N * (  log(2*pi) + s.sq  + 1 ),1)
    
    if(criterion=="AIC"){
      names(aic) = "AIC"
      return(aic)
    }
    if(criterion=="BIC"){
      names(bic) = "BIC"
      return(bic)
    }
  }
}
```

finding best fit model using AIC in Panel Data using R

2 Answers2