Market force data descriptive statistics

Usage

demand_descriptives(object)

supply_descriptives(object)

# S4 method for market_model
demand_descriptives(object)

# S4 method for market_model
supply_descriptives(object)

Arguments

object: A model object.

Value

A data frame containing descriptive statistics.

Details

Calculates and returns basic descriptive statistics for the model's demand or supply side data. Factor variables are excluded from the calculations. The function calculates and returns:

nobs Number of observations.
nmval Number of missing values.
min Minimum observation.
max Maximum observation.
range Observations' range.
sum Sum of observations.
median Median observation.
mean Mean observation.
mean_se Mean squared error.
mean_ce Confidence interval bound.
var Variance.
sd Standard deviation.
coef_var Coefficient of variation.

Functions

demand_descriptives(): Demand descriptive statistics.
supply_descriptives(): Supply descriptive statistics.

Examples

# initialize the basic model using the houses dataset
model <- new(
  "diseq_basic", # model type
  subject = ID, time = TREND, quantity = HS, price = RM,
  demand = RM + TREND + W + CSHS + L1RM + L2RM + MONTH,
  supply = RM + TREND + W + L1RM + MA6DSF + MA3DHF + MONTH,
  fair_houses(), # data
  correlated_shocks = FALSE # allow shocks to be correlated
)

# get descriptive statistics of demand side variables
demand_descriptives(model)
#>                    RM        TREND            W         CSHS         L1RM
#> nobs     1.300000e+02 1.300000e+02 1.300000e+02 1.300000e+02 1.300000e+02
#> nmval    0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#> min      5.780000e+02 1.500000e+01 1.800000e+01 1.334800e+03 5.770000e+02
#> max      8.350000e+02 1.440000e+02 2.300000e+01 1.643820e+04 8.300000e+02
#> range    2.570000e+02 1.290000e+02 5.000000e+00 1.510340e+04 2.530000e+02
#> sum      8.209400e+04 1.033500e+04 2.757000e+03 1.154007e+06 8.183600e+04
#> median   6.000000e+02 7.950000e+01 2.100000e+01 9.018400e+03 6.000000e+02
#> mean     6.314923e+02 7.950000e+01 2.120769e+01 8.876977e+03 6.295077e+02
#> mean_se  5.690898e+00 3.304038e+00 9.438383e-02 3.861383e+02 5.482995e+00
#> mean_ce  1.115395e+01 6.475795e+00 1.849889e-01 7.568171e+02 1.074647e+01
#> var      4.210221e+03 1.419167e+03 1.158080e+00 1.938336e+07 3.908221e+03
#> sd       6.488621e+01 3.767183e+01 1.076141e+00 4.402654e+03 6.251576e+01
#> coef_var 1.027506e-01 4.738595e-01 5.074297e-02 4.959632e-01 9.930898e-02
#>                  L2RM
#> nobs     1.300000e+02
#> nmval    0.000000e+00
#> min      5.770000e+02
#> max      8.250000e+02
#> range    2.480000e+02
#> sum      8.158300e+04
#> median   6.000000e+02
#> mean     6.275615e+02
#> mean_se  5.272695e+00
#> mean_ce  1.033429e+01
#> var      3.614171e+03
#> sd       6.011797e+01
#> coef_var 9.579614e-02

# get descriptive statistics of supply side variables
supply_descriptives(model)
#>                    RM        TREND            W         L1RM       MA6DSF
#> nobs     1.300000e+02 1.300000e+02 1.300000e+02 1.300000e+02 1.300000e+02
#> nmval    0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#> min      5.780000e+02 1.500000e+01 1.800000e+01 5.770000e+02 1.410000e+02
#> max      8.350000e+02 1.440000e+02 2.300000e+01 8.300000e+02 1.476000e+03
#> range    2.570000e+02 1.290000e+02 5.000000e+00 2.530000e+02 1.335000e+03
#> sum      8.209400e+04 1.033500e+04 2.757000e+03 8.183600e+04 1.201327e+05
#> median   6.000000e+02 7.950000e+01 2.100000e+01 6.000000e+02 9.381667e+02
#> mean     6.314923e+02 7.950000e+01 2.120769e+01 6.295077e+02 9.240974e+02
#> mean_se  5.690898e+00 3.304038e+00 9.438383e-02 5.482995e+00 2.476300e+01
#> mean_ce  1.115395e+01 6.475795e+00 1.849889e-01 1.074647e+01 4.853458e+01
#> var      4.210221e+03 1.419167e+03 1.158080e+00 3.908221e+03 7.971677e+04
#> sd       6.488621e+01 3.767183e+01 1.076141e+00 6.251576e+01 2.823416e+02
#> coef_var 1.027506e-01 4.738595e-01 5.074297e-02 9.930898e-02 3.055323e-01
#>                MA3DHF
#> nobs       130.000000
#> nmval        0.000000
#> min       -586.666667
#> max        524.333333
#> range     1111.000000
#> sum       7204.666667
#> median      61.166667
#> mean        55.420513
#> mean_se     15.849930
#> mean_ce     31.065293
#> var      32658.638336
#> sd         180.717012
#> coef_var     3.260833