Exact Tests in Panel Data Using Generalizedp-Values

Inferences on the regression coefficients in panel data models: parametric bootstrap approach

“…In this section, we present the results of our simulation study to compare the size and powers of our PB approach with generalized p values by [23] and approximated methods based on a Monte Carlo simulation study. we use the abbreviation PB, GPV and AP to refer these three methods.…”

“…This panel consists of annual observations across 18 OECD countries, covering the period 1960-1978. We take a part of the panel as well as reported in Table 3 by [23]. At first, let = ( , 1 , 2 , 3 ) � , then a computed GLSE of is given as ̃ = (0.765, 0.323, −0.469, −0.578) � and unbiased estimators of 2 and 2 are 0.0552 and 0.0012, respectively.…”

“…Our procedure is based on a new parametric bootstrap pivot variable. The performance of our PB method is compared with generalized p value approaches introduced by [23]. The numerical results in section "Simulation study" show that in terms of the type I error rate and power, the performance of our method is better than generalized p value (GPV) inferences and approximate (AP) method.…”

“…Precious inferences in panel data are difficult when these models have nuisance parameters. Zhao [23] suggested generalized p value inferences in panel data models of generalized p values. When nuisance parameters are present in models, generalized p values are effective to solve the testing problems [8,11,[16][17][18][19]24].…”

A Parametric Bootstrap Approach for a One-Way Error Component Regression Model with Measurement Errors

“…If the measurement errors do not exist, model (1) has been discussed by refs. [7,8], etc. When the individual effect and the time index of j = 1, .…”