Contingency fee is one of the common institutions of common law legal system particularly in the united state. In this type of fees, the payment of fee is conditional to the results of lawsuit. So if the lawsuit fails, no fee would be paid to the lawyer and if the lawsuit is successful, more than usual amount or a pecent of condemned amount will be paid. Despite accepting theses agreement in some legal systems, there are some limitations in order to make conformity with their own legal system which make different, the importance and functions of it in compare with the U. S. In some legal systems, other institutions with the same function are predicted too. In Iran legal system, despite of freedom of contract, according to article 80 of the law of attorney regulations, a type of contingency fees which is a part of a demand are prohibited. This paper with a comparative attitude and with insisting on U. S legal system and clarifiying the conditional and contingency fees and its advantages and disadvantages, defend the accepting of it in Iran legal system.

This study examines the relation of conditional and non-conditional persistence of earning components to abnormal returns. The conditional persistence has been calculated using the concept of investors’ over-reaction towards accruals and their under-reaction towards sales revenue. The research uses multivariate regression method and the statistical sample consists of 66 firms listed in the Tehran Stock Exchange over the period from 2003 to 2016. To test the hypotheses, the panel data method and t-student test is used. The findings show a significant relation of the “the difference of conditional and non-conditional persistence of unexpected revenues” and “unexpected revenues” to “abnormal stock returns”; however, the findings indicate no relation of “differences of conditional and non-conditional persistence of unexpected revenues” and “unexpected earnings” to “abnormal stock returns”. Also, the relation between “the difference of conditional and non-conditional persistence of accruals" and "accrual anomaly" is not confirmed.

Mixed models are widely used to analyze longitudinal data. In their conventional formulation as linear mixed models (LMMs) and generalized LMMs (GLMMs), a commonly indispensable assumption in settings involving longitudinal non-Gaussian data is that the longitudinal observations from subjects are conditionally independent, given subject-specific random effects. Although conventional Gaussian LMMs are able to incorporate conditional dependence of longitudinal observations, they require that the data are, or some transformation of them is, Gaussian, a serious limitation in a wide variety of practical applications. Here, we introduce the class of Gaussian copula conditional regression models (GCCRMs) as flexible alternatives to conventional LMMs and GLMMs. One advantage of GCCRMs is that they extend conventional LMMs and GLMMs in a way that reduces to conventional LMMs, when the data are Gaussian, and to conventional GLMMs, when conditional independence is assumed. We implement likelihood analysis of GCCRMs using existing software and statistical packages and evaluate the finite-sample performance of maximum likelihood estimates for GCCRM empirically via simulations vis-a-vis the `naive' likelihood analys is that incorrectly assumes conditionally independent longitudinal data. Our results show that the `naive' analysis yields estimates with possibly severe bias and incorrect standard errors, leading to misleading inferences. We use bolus count data on patients' controlled analgesia comparing dosing regimes and data on serum creatinine from a renal graft study to illustrate the applications of GCCRMs.