Compute Test Statistic and Critical Value
g_restriction(
theta,
w_data,
a_matrix,
j0_vector,
v_bar,
alpha,
grid0 = "all",
iv_matrix = NULL,
test0 = "CCK",
cvalue = "SN",
account_uncertainty = FALSE,
bootstrap_replications = NULL,
rng_seed = NULL,
bootstrap_indices = NULL,
an_vec = NULL,
hat_r_inf = NULL,
dist_data = NULL
)
theta
a vector containing the parameters of interest.
w_data
an n x k matrix of product portfolio data.
a_matrix
an n x (J0 + 1) matrix of estimated revenue differentials.
j0_vector
a J0 x 2 matrix of ownership by the two firms.
v_bar
a tuning parameter as in Assumption 4.2.
alpha
the significance level.
grid0
optional vector of length J0 containing the indices of the products in the market.
iv_matrix
optional n x d_iv matrix of instruments.
test0
optional test statistic to use. Either “CCK” or “RC-CCK”.
cvalue
optional critical value to use. Either “SPUR1”, “SN”, “SN2S”, or “EB2S”.
account_uncertainty
Whether to account for additional uncertainty (as in Equations 49 and 50). If TRUE, the last two elements of theta are assumed to be mu.
bootstrap_replications
the number of bootstrap replications. Required if bootstrap_indices is not specified.
rng_seed
the seed for replication purposes. If not specified, the seed is not set.
bootstrap_indices
an integer vector of indices to use for the bootstrap. If this is specified, bootstrap_replications and rng_seed will be ignored. If this is not specified, bootstrap_replications is required.
an_vec
if using SPUR1, an n-dimensional vector of An values.
hat_r_inf
if using RC-CCK, the lower value of the test statistic.
dist_data
an n x (J + 1) matrix of distances from the product factories to the cities.
a vector containing the test statistic and critical value.