How Do Frictions Affect Corporate Investment? A Structural Approach

2016 ◽  
Vol 51 (6) ◽  
pp. 1863-1895 ◽  
Author(s):  
M. Cecilia Bustamante
Keyword(s):  
Active Region ◽  
Likelihood Function ◽  
Capital Intensity ◽  
Corporate Investment ◽  
Structural Approach ◽  
Log Likelihood ◽  
Financing Costs ◽  
Financing Frictions ◽  
Macro Finance

This paper provides a structural approach to testing investment equations based on the log-likelihood function of a nonlinear investment rule. The analysis integrates the predictions of theq-theory for the commonly studied active region of investment and provides new inferences on how real and financing frictions affect the probability that a firm invests. The empirical findings are consistent with the macro-finance literature suggesting thatq-theory models with nonconvex investment frictions better explain the data. I also find that both real and financing costs of investment are related to the capital intensity of the industry in which firms operate.

scholarly journals Evaluating the Observed Log-Likelihood Function in Two-Level Structural Equation Modeling with Missing Data: From Formulas to R Code

Psych ◽  
2021 ◽  
Vol 3 (2) ◽  
pp. 197-232
Author(s):  
Yves Rosseel
Keyword(s):  
Structural Equation ◽  
Structural Equation Models ◽  
Likelihood Function ◽  
Likelihood Estimation ◽  
Missing At Random ◽  
Equation Modeling ◽  
Computationally Efficient ◽  
Log Likelihood ◽  
Efficient Expression ◽  
Special Case

This paper discusses maximum likelihood estimation for two-level structural equation models when data are missing at random at both levels. Building on existing literature, a computationally efficient expression is derived to evaluate the observed log-likelihood. Unlike previous work, the expression is valid for the special case where the model implied variance–covariance matrix at the between level is singular. Next, the log-likelihood function is translated to R code. A sequence of R scripts is presented, starting from a naive implementation and ending at the final implementation as found in the lavaan package. Along the way, various computational tips and tricks are given.

Maximum Log-Likelihood Function-Based QAM Signal Classification over Fading Channels

2004 ◽  
Vol 28 (1) ◽  
pp. 77-94 ◽  
Author(s):  
Yawpo Yang ◽  
Jen-Ning Chang ◽  
Ji-Chyun Liu ◽  
Ching-Hwa Liu
Keyword(s):  
Fading Channels ◽  
Likelihood Function ◽  
Signal Classification ◽  
Log Likelihood

scholarly journals TCLUST: Trimming Approach of Robust Clustering Method

2014 ◽  
Vol 8 (4) ◽  
Author(s):  
Muhamad Alias Md. Jedi ◽  
Robiah Adnan
Keyword(s):  
Clustering Algorithm ◽  
Likelihood Function ◽  
R Package ◽  
Clustering Method ◽  
Number Of Clusters ◽  
Robust Clustering ◽  
Scatter Matrix ◽  
Group Assignment ◽  
Log Likelihood ◽  
Clustering Approach

TCLUST is a method in statistical clustering technique which is based on modification of trimmed k-means clustering algorithm. It is called “crisp” clustering approach because the observation is can be eliminated or assigned to a group. TCLUST strengthen the group assignment by putting constraint to the cluster scatter matrix. The emphasis in this paper is to restrict on the eigenvalues, λ of the scatter matrix. The idea of imposing constraints is to maximize the log-likelihood function of spurious-outlier model. A review of different robust clustering approach is presented as a comparison to TCLUST methods. This paper will discuss the nature of TCLUST algorithm and how to determine the number of cluster or group properly and measure the strength of group assignment. At the end of this paper, R-package on TCLUST implement the types of scatter restriction, making the algorithm to be more flexible for choosing the number of clusters and the trimming proportion.

A log-likelihood function-based algorithm for QAM signal classification

1998 ◽  
Vol 70 (1) ◽  
pp. 61-71 ◽  
Author(s):  
Yawpo Yang ◽  
Ching-Hwa Liu ◽  
Ta-Wei Soong
Keyword(s):  
Likelihood Function ◽  
Signal Classification ◽  
Log Likelihood
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