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Market fit coefficients

Source: R/market_fit.R
coef.Rd

Returns the coefficients of the fitted market model.

Usage

# S4 method for market_fit
coef(object)

# S4 method for market_fit
coefficients(object)

Arguments

object

A fitted model object.

Value

A named vector of estimated model coefficients.

Methods (by class)

  • coef(market_fit): Estimated coefficients.

  • coefficients(market_fit): Estimated coefficients alias.

Examples

# \donttest{
# estimate a model using the houses dataset
fit <- diseq_deterministic_adjustment(
  HS | RM | ID | TREND ~
    RM + TREND + W + CSHS + L1RM + L2RM + MONTH |
      RM + TREND + W + L1RM + MA6DSF + MA3DHF + MONTH,
  fair_houses(),
  correlated_shocks = FALSE,
  estimation_options = list(control = list(maxit = 1e+6))
)

# access the estimated coefficients
coef(fit)
#>          D_RM       D_CONST       D_TREND           D_W        D_CSHS 
#>   -6.39287048  -20.16533253   -3.09530903    2.77909898    0.02878931 
#>        D_L1RM        D_L2RM     D_MONTH02     D_MONTH03     D_MONTH04 
#>    8.92812266   -2.48883659   17.94359360   28.55310947   78.00303909 
#>     D_MONTH05     D_MONTH06     D_MONTH07     D_MONTH08     D_MONTH09 
#>   84.17025404   82.50861671   88.22174395   52.40940637   49.40057986 
#>     D_MONTH10     D_MONTH11     D_MONTH12          S_RM       S_CONST 
#>   67.05086057   24.50611800   30.86396923    0.46442865  -37.46456496 
#>       S_TREND           S_W        S_L1RM      S_MA6DSF      S_MA3DHF 
#>   -0.14554797    2.08578681   -0.40511192    0.04693627    0.03455421 
#>     S_MONTH02     S_MONTH03     S_MONTH04     S_MONTH05     S_MONTH06 
#>    3.81910684   34.38727665   65.75523098   67.60421681   57.39161807 
#>     S_MONTH07     S_MONTH08     S_MONTH09     S_MONTH10     S_MONTH11 
#>   43.90383859   49.34401811   44.93630427   49.43637822   32.80682646 
#>     S_MONTH12       RM_DIFF    D_VARIANCE    S_VARIANCE 
#>   11.27185537    1.53371422 1295.13136810  107.76446554 
coefficients(fit)
#>          D_RM       D_CONST       D_TREND           D_W        D_CSHS 
#>   -6.39287048  -20.16533253   -3.09530903    2.77909898    0.02878931 
#>        D_L1RM        D_L2RM     D_MONTH02     D_MONTH03     D_MONTH04 
#>    8.92812266   -2.48883659   17.94359360   28.55310947   78.00303909 
#>     D_MONTH05     D_MONTH06     D_MONTH07     D_MONTH08     D_MONTH09 
#>   84.17025404   82.50861671   88.22174395   52.40940637   49.40057986 
#>     D_MONTH10     D_MONTH11     D_MONTH12          S_RM       S_CONST 
#>   67.05086057   24.50611800   30.86396923    0.46442865  -37.46456496 
#>       S_TREND           S_W        S_L1RM      S_MA6DSF      S_MA3DHF 
#>   -0.14554797    2.08578681   -0.40511192    0.04693627    0.03455421 
#>     S_MONTH02     S_MONTH03     S_MONTH04     S_MONTH05     S_MONTH06 
#>    3.81910684   34.38727665   65.75523098   67.60421681   57.39161807 
#>     S_MONTH07     S_MONTH08     S_MONTH09     S_MONTH10     S_MONTH11 
#>   43.90383859   49.34401811   44.93630427   49.43637822   32.80682646 
#>     S_MONTH12       RM_DIFF    D_VARIANCE    S_VARIANCE 
#>   11.27185537    1.53371422 1295.13136810  107.76446554 
# }