A regression-with-residuals method for analyzing causal mediation: The rwrmed package

In this article, we introduce the rwrmed package, which performs mediation analysis using the methods proposed by Wodtke and Zhou (2020, Epidemiology 31: 369–375). Specifically, rwrmed estimates interventional direct and indirect effects in the presence of treatment-induced confounding by fitting models for 1) the conditional mean of the mediator given the treatment and a set of baseline confounders and 2) the conditional mean of the outcome given the treatment, mediator, baseline confounders, and a set of treatment-induced confounders that have been residualized with respect to the observed past. Interventional direct and indirect effects are simple functions of the parameters in these models when they are correctly specified and when there are no unobserved variables that confound the treatment-outcome, treatment-mediator, or mediator-outcome relationships. When no treatment-induced confounders are specified, rwrmed produces natural direct and indirect effect estimates.

Organization's Values Perspective to Financial Reporting

The purpose of this study is to obtain the truth regarding organizations’ financial statements by examining the influence of organizational culture. This is a verification research, with an explanation technique used to determine the factors estimated to affect the variables. The non-probability sampling technique was used to obtain primary data from 65 respondents working in several Indonesia organizations through questionnaires and by observing their accounting and finance divisions. Hypothesis testing in this study uses Structural Equation Modeling (SEM) with the estimation of model parameters using the PLS method (Partial Least Square). The consideration of choosing SEM analysis technique is because the variables involved are unobserved variables and there is a causal relationship between the variables. According to initial concepts, organizational culture contributes to financial reporting development due to its ability to reflect an organization's specificity and characters. The result showed that organizational culture is the property and guidelines for all individuals in an organization to carry out their duties, and it influences the presentation of financial statements.Keywords: Accounting; Financial reporting; Organizational cultureAbstrak Penelitian ini bertujuan untuk mendapatkan kebenaran melalui pengujian pengaruh budaya organisasi terhadap penyajian laporan keuangan. Jenis penelitian ini bersifat verifikatif dan bersifat penjelas atau kausalitas untuk mengetahui apa dan seberapa jauh faktor-faktor yang diperkirakan mempengaruhi suatu variabel dengan variabel lainnya. Jenis data yang digunakan adalah data primer dengan instrumen kuesioner yang dibagikan pada 65 responden dari beberapa organisasi di Indonesia. Unit observasi pada penelitian ini adalah bagian akuntansi dan keuangan pada tiap organisasi. Teknik pengambilan sampel dalam penelitian ini adalah dengan menggunakan teknik non-probability. Pengujian hipotesis dalam penelitian ini menggunakan Structural Equation Modeling (SEM) dengan penaksiran parameter-parameter model memakai metode PLS (Partial Least Square). Pertimbangan memilih teknik analisis SEM karena variable yang terlibat adalah unobserved variables serta adanya hubungan kausal antar variabelnya. Menurut konsep dikatakan bahwa budaya organisasi akan memberikan kontribusi yang berarti dalam meningkatkan pelaporan keuangan. Budaya organisasi akan mencerminkan spesifikasi dan karakter suatu organisasi. Budaya organisasi tersebut menjadi milik dan pedoman bagi seluruh lapisan individu yang ada pada suatu organisasi dalam menjalankan tugasnya. Hasil penelitian menunjukkan bahwa budaya organisasi berpengaruh terhadap penyajian laporan keuangan.Kata kunci: Akuntansi; Budaya organisasi; Pelaporan keuangan

Causal Discovery for Climate Time Series in the Presence of Unobserved Variables

<p>Scientific inquiry seeks to understand natural phenomena by understanding their underlying processes, i.e., by identifying cause and effect. In addition to mere scientific curiosity, an understanding of cause and effect relationships is necessary to predict the effect of changing dynamical regimes and for the attribution of extreme events to potential causes. It is thus an important question to ask how, in cases where controlled experiments are not feasible, causation can still be inferred from the statistical dependencies in observed time series.</p><p>A central obstacle for such an inference is the potential existence of unobserved causally relevant variables. Arguably, this is more likely to be the case than not, for example unmeasured deep oceanic variables in atmospheric processes. Unobserved variables can act as confounders (meaning they are a common cause of two or more observed variables) and thus introduce spurious, i.e., non-causal dependencies. Despite these complications, the last three decades have seen the development of so-called causal discovery algorithms (an example being FCI by Spirtes et al., 1999) that are often able to identify spurious associations and to distinguish them from genuine causation. This opens the possibility for a data-driven approach to infer cause and effect relationships among climate variables, thereby contributing to a better understanding of Earth's complex climate system.</p><p>These methods are, however, not yet well adapted to some specific challenges that climate time series often come with, e.g. strong autocorrelation, time lags and nonlinearities. To close this methodological gap, we generalize the ideas of the recent PCMCI causal discovery algorithm (Runge et al., 2019) to time series where unobserved causally relevant variables may exist (in contrast, PCMCI made the assumption of no confounding). Further, we present preliminary applications to modes of climate variability.</p>

Latent Variable Models in Clinical Psychology

Most of what clinical psychology concerns itself with is directly unobservable. Concepts like neuroticism and depression, but also learning and development, represent dispositions, states, or processes that must be inferred and cannot (currently) be directly measured. Latent variable modeling, as a statistical framework, encompasses a range of techniques that involve estimating the presence and effect of unobserved variables from observed data. This chapter provides a non-technical overview of latent variable modeling in clinical psychology. Dimensional latent variable models are emphasized, although categorical and hybrid models are touched on briefly. Challenges with specific models, such as the bifactor model are discussed. Examples draw from the psychopathology literature.

Cointegration and Adjustment in the CVAR(∞) Representation of Some Partially Observed CVAR(1) Models

A multivariate CVAR(1) model for some observed variables and some unobserved variables is analysed using its infinite order CVAR representation of the observations. Cointegration and adjustment coefficients in the infinite order CVAR are found as functions of the parameters in the CVAR(1) model. Conditions for weak exogeneity for the cointegrating vectors in the approximating finite order CVAR are derived. The results are illustrated by two simple examples of relevance for modelling causal graphs.

Corporate Governance to Prevent Financial Distress Evidence from Corporate Governance Perceptions Index of Indonesian Companies

This study aims to determine the influence of Good Corporate Governance (GCG), Company Size, Liquidity, and Rentability on Financial Distress of companies listed on Corporate Governance Perception Index (CGPI) partially and simultaneously. �The population of this research is companies listed on the Indonesian Stock Exchange (BEI) and Corporate Governance Perception ranks starting from 2013 to 2016. Based on the criteria above, 59 companies are selected. The sampling of this research is taken by using purposive sampling method from the population with a target of several considerations. The result shows that Good Corporate Governance does not significantly influence Financial Distress, Company Size negatively affects Financial Distress, Liquidity positively affects Financial Distress, and Rentability positively affects Financial Distress.� Good Corporate Governance, Company Size, Liquidity, and Rentability partially influence Financial Distress with coefficient determination is 92,25% while 2,75% is explained by other unobserved variables in outside the model.