scholarly journals Impact of Full IFRS, Accounting Standards for SMES and Company Demographics on Firms’ Return on Asset and Return on Equity using Panel Data Regression

2019 ◽  
Vol 8 (6S) ◽  
pp. 589-601 ◽  
Keyword(s):  
Least Squares ◽  
Random Effects ◽  
Fixed Effects ◽  
Random Effect ◽  
Ordinary Least Squares ◽  
Lagrangian Multiplier ◽  
Random Effects Model ◽  
Fixed Effects Model ◽  
Real Property ◽  
The Impact

Due to globalization, markets are becoming more interconnected as the companies are engaged in doing cross-border offerings. Currently, competitions are intensified because Domestic organizations discover themselves competing with each nearby opposite numbers and worldwide companies. But one component that hinders SMEs is the need for reliable and similar monetary data. According to Abarca (2014), adoption of a high-quality and consistent set of accounting requirements is critical so as for the businesses to remain competitive in ASEAN member states. This paper ambitions to answer the query, what modified into the extent of the impact of compliance with full IFRS and IFRS for SMEs on profitability of agencies belong to real property enterprise? This paper moreover sought to decide whether there may be a sizeable distinction among the groups’ compliance with the overall PFRS and the PFRS for SMEs and to determine whether or now not there is a massive distinction among the companies’ financial normal overall performance earlier than and after the adoption of the PFRS for SMEs.Paired T-test have become employed in case you need to determine whether there is a big distinction between the agencies’ compliance with the entire PFRS and the PFRS for SMEs and to decide whether or not there may be a big difference some of the groups’ monetary performance earlier than and after the adoption of the PFRS for SMEs. Using STATA, the great appropriate version for every economic ratio on the subject of degree of compliance emerge as determined on. First, take a look at parm command became used to find out which most of the Least Squares Dummy Variable Regression Modes (LSDV1, LSDV2, LSDV3) underneath the Fixed Effects Model is the ideal version. Afterwards, Hausman Fixed Random Test changed into used to pick out out which is more suitable amongst Fixed Effects Model and Random Effects Model. If Fixed Effects Model modified into the more appropriate one, the Wald’s test turn out to be used to determine the best version among Fixed Effects Model and Ordinary Least Squares Model. On the alternative hand, if Random Effects Model became the more suitable one, the Breusch and Pagan Lagrangian Multiplier Test for Random Effect have become used to decide the satisfactory version amongst Random Effects Model and Ordinary Least Squares. Moreover, if Ordinary Least Squares became the splendid model, it is going to be in addition tested to check for heteroscedasticity and multicollinearity. White’s test became used to check for heterescedasticity and Variance Inflation Factor have become used to test if multicollinearity is gift. The results display that the adoption of PFRS for SMEs stepped forward the compliance of Philippine real property SMEs. However, no vast alternate became said inside the financial average performance of those companies (as measured with the resource of cross back on assets and go back on equity). This was further supported by the results of the panel regression. This means that despite having a relatively

scholarly journals FORECASTING MONEY DEMAND WITH THE INCLUSION OF FINANCIAL INNOVATION FOR THE ASSOCIATION OF SOUTHEAST ASIAN NATIONS: EVIDENCE FROM PANEL DATA ANALYSIS

2019 ◽  
Vol 1 (1) ◽  
pp. 59-74
Author(s):  
Payam Mohammad Aliha ◽  
Tamat Sarmidi ◽  
Fathin Faizah Said
Keyword(s):  
Time Series ◽  
Panel Data ◽  
Random Effects ◽  
Money Demand ◽  
Fixed Effects ◽  
Random Effects Model ◽  
Fixed Effects Model ◽  
Demand For Money ◽  
Financial Innovations ◽  
The Impact

This paper investigates the impact of financial innovations on the demand for money using a dynamic panel data for 10 ASEAN member states from 2004 to 2012 and attempt to forecast the demand for money during 2013 – 2016 to compare between forecasting performance of the fixed effects model with that of random effects model and also to compare the forecasting accuracy of dynamic forecasting and static forecasting obtained from these two models. An autoregressive model by definition is when a value from a time series is regressed on previous values from that same time series. There are two types of forecasting namely dynamic forecast and static forecast. “Dynamic forecast will take previously forecasted values while static forecast will take actual values to make next step forecast. Panel effects models assist in controlling for unobserved heterogeneity when this heterogeneity is constant over time and correlated (fixed effects) or uncorrelated (random effects) with independent variables. Hausman test indicates that the random-effects model is appropriate. We use the conventional money demand that is enriched with the number of automated teller machines (ATM) to proxy for the effect of financial innovations on money demand. By comparing the magnitude of “Root Mean Squared Error” (RMSE) as a benchmark for the two forecasts (0.1164 for dynamic forecast versus 0.0635 for static forecast) we simply find out that static forecast is superior to dynamic forecast meaning that static forecast provides more accurate forecast compared to a dynamic forecast for the fixed-effects model. Therefore, we conclude the static forecast on the basis of the random-effects model provides the most accurate forecasting. The estimation result of the chosen random-effects regression also indicates the estimated coefficient of ATM is not significant meaning that ATM does not impact money demand in ASEAN countries.

scholarly journals A review and meta-analysis of red and processed meat consumption and breast cancer

2010 ◽  
Vol 23 (2) ◽  
pp. 349-365 ◽  
Author(s):  
Dominik D. Alexander ◽  
Libby M. Morimoto ◽  
Pamela J. Mink ◽  
Colleen A. Cushing
Keyword(s):  
Breast Cancer ◽  
Random Effects ◽  
Fixed Effects ◽  
Meta Analysis ◽  
Random Effect ◽  
Red Meat ◽  
Processed Meat ◽  
Random Effects Model ◽  
Fixed Effects Model ◽  
Meat Intake

The relationship between meat consumption and breast cancer has been the focus of several epidemiological investigations, yet there has been no clear scientific consensus as to whether red or processed meat intake increases the risk of breast cancer. We conducted a comprehensive meta-analysis incorporating data from several recently published prospective studies of red or processed meat intake and breast cancer. In the meta-analysis utilising data from the Pooling Project publication (includes data from eight cohorts) combined with data from nine studies published between 2004 and 2009 and one study published in 1996, the fixed-effect summary relative risk estimate (SRRE) for red meat intake (high v. low) and breast cancer was 1·02 (95 % CI 0·98, 1·07; P value for heterogeneity = 0·001) and the random-effects SRRE was 1·07 (95 % CI 0·98, 1·17). The SRRE for each 100 g increment of red meat was 1·04 (95 % CI 1·00, 1·07), based on a fixed-effects model, and 1·12 (95 % CI 1·03, 1·23) based on a random-effects model. No association was observed for each 100 g increment of red meat among premenopausal women (SRRE 1·01; 95 % CI 0·92, 1·11) but a statistically significant SRRE of 1·22 (95 % CI 1·04, 1·44) was observed among postmenopausal women using a random-effects model. However, the association for postmenopausal women was attenuated and non-significant when using a fixed-effects model (SRRE 1·03; 95 % CI 0·98, 1·08). The fixed- and random-effect SRRE for high (v. low) processed meat intake and breast cancer were 1·00 (95 % CI 0·98, 1·01; P value for heterogeneity = 0·005) and 1·08 (95 % CI 1·01, 1·16), respectively. The fixed- and random-effect SRRE for each 30 g increment of processed meat were 1·03 (95 % CI 1·00, 1·06) and 1·06 (95 % CI 0·99, 1·14), respectively. Overall, weak positive summary associations were observed across all meta-analysis models, with the majority being non-statistically significant. Heterogeneity was evident in most analyses, summary associations were sensitive to the choice of analytical model (fixed v. random effects), and publication bias appeared to have produced slightly elevated summary associations. On the basis of this quantitative assessment, red meat and processed meat intake does not appear to be independently associated with increasing the risk of breast cancer, although further investigations of potential effect modifiers, such as analyses by hormone receptor status, may provide valuable insight to potential patterns of associations.

The relationship between migraine and infant colic: A systematic review and meta-analysis

Cephalalgia ◽  
2014 ◽  
Vol 35 (1) ◽  
pp. 63-72 ◽  
Author(s):  
Amy A Gelfand ◽  
Peter J Goadsby ◽  
I Elaine Allen
Keyword(s):  
Systematic Review ◽  
Random Effects ◽  
Odds Ratio ◽  
Fixed Effects ◽  
Meta Analysis ◽  
Random Effects Model ◽  
Fixed Effects Model ◽  
Primary Analysis ◽  
Infant Colic ◽  
The Relationship

Context Infant colic is a common and distressing disorder of early infancy. Its etiology is unknown, making treatment challenging. Several articles have suggested a link to migraine. Objective The objective of this article was to perform a systematic review and, if appropriate, a meta-analysis of the studies on the relationship between infant colic and migraine. Data sources Studies were identified by searching PubMed and ScienceDirect and by hand-searching references and conference proceedings. Study selection For the primary analysis, studies specifically designed to measure the association between colic and migraine were included. For the secondary analysis, studies that collected data on colic and migraine but were designed for another primary research question were also included. Data extraction Data were abstracted from the original studies, through communication with study authors, or both. Two authors independently abstracted data. Main outcomes and measures The main outcome measure was the association between infant colic and migraine using both a fixed-effects model and a more conservative random-effects model. Results Three studies were included in the primary analysis; the odds ratio for the association between migraine and infant colic was 6.5 (4.6–8.9, p < 0.001) for the fixed-effects model and 5.6 (3.3–9.5, p = 0.004) for the random-effects model. In a sensitivity analysis wherein the study with the largest effect size was removed, the odds ratio was 3.6 (95% CI 1.7–7.6, p = 0.001) for both the fixed-effects model and random-effects model. Conclusions In this meta-analysis, infant colic was associated with increased odds of migraine. If infant colic is a migrainous disorder, this would have important implications for treatment. The main limitation of this meta-analysis was the relatively small number of studies included.

scholarly journals IMPACT OF HUMAN CAPITAL DEVELOPMENT ON PRODUCTIVITY GROWTH IN EU MEMBER STATES

2018 ◽  
Vol 16 (0) ◽  
pp. 1-12 ◽  
Author(s):  
Alma Mačiulytė-Šniukienė ◽  
Kristina Matuzevičiūtė
Keyword(s):  
Human Capital ◽  
Fixed Effects ◽  
Panel Data Analysis ◽  
Labour Productivity ◽  
Ordinary Least Squares ◽  
Fixed Effects Model ◽  
Education Expenditure ◽  
Member States ◽  
Eu Member States ◽  
The Impact

In this research, we investigate the impact of human capital on labour productivity in European Union member states using panel data analysis. Results of the paper are estimated using the Pooled ordinary least squares (OLS) and Fixed effects model (FEM). The results show that human capital is positively significant in improving the growth of labour productivity in the EU. Our estimates also suggest that the impact occurs after three times lags in case of education expenditure.

scholarly journals The Impact of Misspecified Random Effect Distribution in a Weibull Regression Mixed Model

Stats ◽  
2018 ◽  
Vol 1 (1) ◽  
pp. 48-76
Author(s):  
Freddy Hernández ◽  
Viviana Giampaoli
Keyword(s):  
Weibull Distribution ◽  
Random Effects ◽  
Mixed Models ◽  
Fixed Effects ◽  
Mixed Model ◽  
Random Effect ◽  
Estimation Procedure ◽  
Weibull Regression ◽  
Two Parameters ◽  
The Impact

Mixed models are useful tools for analyzing clustered and longitudinal data. These models assume that random effects are normally distributed. However, this may be unrealistic or restrictive when representing information of the data. Several papers have been published to quantify the impacts of misspecification of the shape of the random effects in mixed models. Notably, these studies primarily concentrated their efforts on models with response variables that have normal, logistic and Poisson distributions, and the results were not conclusive. As such, we investigated the misspecification of the shape of the random effects in a Weibull regression mixed model with random intercepts in the two parameters of the Weibull distribution. Through an extensive simulation study considering six random effect distributions and assuming normality for the random effects in the estimation procedure, we found an impact of misspecification on the estimations of the fixed effects associated with the second parameter σ of the Weibull distribution. Additionally, the variance components of the model were also affected by the misspecification.

Intellectual capital efficiency and market-based financial performance: Evidence from listed conglomerate companies

2021 ◽  
Vol 2 (2) ◽  
pp. 31-42
Author(s):  
Eniola Ayisat Sulaiman ◽  
Abubakar Sadiq Kasum ◽  
Wasiu Ajani Musa
Keyword(s):  
Financial Performance ◽  
Intellectual Capital ◽  
Fixed Effects ◽  
Value Added ◽  
Secondary Data ◽  
Lagrangian Multiplier ◽  
Fixed Effects Model ◽  
Analysis Technique ◽  
Capital Efficiency ◽  
The Impact

Having observed the rate at which dissimilarity occurs between market and book value, and management ignorance concerning the impact intellectual capital disclosure has on companies’ values spurred the interest to probe the association between the efficiency of value-added intellectual coefficient (VAIC) and market-based financial performance of listed Nigerian conglomerate companies. To accomplish the purpose of this study, secondary data were employed and extracted from annual audited reports of listed conglomerate companies in Nigeria from the period of 2010–2018. The data obtained were subjected to static panel data regression analysis technique. The random-effects model was adopted because the empirical result from Breusch and Pagan Lagrangian multiplier (BP-LM) and Hausman tests chose it over the fixed-effects model to produce better results. This study revealed that the value-added efficiency of capital employed (VACA), value-added efficiency of human capital (VAHU), and value-added efficiency of structural capital (STVA) are the drivers of intellectual capital in the conglomerate sector. This study concluded that elements of intellectual capital have a strong power on market-based financial performance. This study recommends that information on intellectual capital components should be reported in ways they deem fit by developing a model of intellectual capital disclosure that complies with the International Accounting Standard Board (IASB)

Product diversification, business structure, and firm performance in Taiwanese property and liability insurance sector

2017 ◽  
Vol 18 (5) ◽  
pp. 486-499 ◽  
Author(s):  
Chen-Ying Lee
Keyword(s):  
Firm Performance ◽  
Financial Services ◽  
Fixed Effects ◽  
Ordinary Least Squares ◽  
Product Diversification ◽  
Least Squares Regression ◽  
Fixed Effects Model ◽  
Content Type ◽  
The Impact ◽  
Business Structure

Purpose The purpose of this study is to analyze product diversification, business structure and insurer performance with a comprehensive look at the property-liability (P/L) insurance operations. Design/methodology/approach Using a panel data, this study employs an ordinary least squares regression model, fixed effects model and random effects model to examine the impact of product diversification and business structure on the performance of P/L insurers. The study assesses insurer performance using both risk-adjusted return on assets and risk-adjusted return on equity. Findings The study finds that product diversification is significantly negatively related to the performance of P/L insurers. The results are consistent with the diversification discount theory. The empirical results reveal that business lines have significant impacts on firm performance, particularly on the lines of fire and marine insurances. Furthermore, the interaction between product diversification and firm size implies that product diversification significantly increases the performance of large-sized insurance firms. Originality/value The study provides some valuable insights into the effects of diversification and business structure on the performance of P/L insurers in a developing country. The study’s findings suggest that management of P/L insurers should clarify their objectives and carefully assess the company’s resources when dealing with product diversification and business structure. The results have practical implications for the financial services industry in Taiwan.

scholarly journals Comparison of Competing Approaches to Analyzing Cross-Classified Data: Random Effects Models, Ordinary Least Squares, or Fixed Effects with Cluster Robust Standard Errors

2021 ◽  
Author(s):  
Young Ri Lee ◽  
James E Pustejovsky
Keyword(s):  
Least Squares ◽  
Random Effects ◽  
Fixed Effects ◽  
Good Alternative ◽  
Ordinary Least Squares ◽  
Alternative Methods ◽  
Least Squares Regression ◽  
Monte Carlo Simulation Study ◽  
Random Effects Models ◽  
Fixed Effects Regression

Cross-classified random effects modeling (CCREM) is a common approach for analyzing cross-classified data in education. However, when the focus of a study is on the regression coefficients at level one rather than on the random effects, ordinary least squares regression with cluster robust variance estimators (OLS-CRVE) or fixed effects regression with CRVE (FE-CRVE) could be appropriate approaches. These alternative methods may be advantageous because they rely on weaker assumptions than what is required by CCREM. We conducted a Monte Carlo Simulation study to compare the performance of CCREM, OLS-CRVE, and FE-CRVE in models with crossed random effects, including conditions where homoscedasticity assumptions and exogeneity assumptions held and conditions where they were violated. We found that CCREM performed the best when its assumptions are all met. However, when homoscedasticity assumptions are violated, OLS-CRVE and FE-CRVE provided similar or better performance than CCREM. FE-CRVE showed the best performance when the exogeneity assumption is violated. Thus, we recommend two-way FE-CRVE as a good alternative to CCREM, particularly if the homoscedasticity or exogeneity assumptions of the CCREM might be in doubt.

scholarly journals The impact of financial variables on systematic risk – An empirical study in manufacturing industry on Ho Chi Minh stock exchange

2017 ◽  
Vol 1 (Q4) ◽  
pp. 88-94
Author(s):  
Minh Tien Pham ◽  
Bich Huy Hai Bui ◽  
Thao Thi Thu Nguyen
Keyword(s):  
Fixed Effects ◽  
Stock Exchange ◽  
Random Effect ◽  
Random Effect Model ◽  
Systematic Risk ◽  
Ordinary Least Squares ◽  
Operating Efficiency ◽  
Manufacturing Companies ◽  
Financial Variables ◽  
The Impact

The aim of this study is to examine the effect of financial variables on systematic risk, using the panel data of 64 manufacturing companies listed in Ho Chi Minh City Stock Exchange (HOSE) during the period of 2011-2015. The three models employed are pooled Ordinary Least Squares (OLS), Random Effect Model (REM), and Fixed Effects Model (FEM). The results of model tests show that FEM is the most suitable to carry out the analysis. In order to increase the efficiency of the model, the tests for model problems are conducted. The results point to the presence of heteroskedasticity problem in the model; therefore, the modified FEM is used to deal with this issue. Empirical evidence from HOSE indicates that leverage has a significantly positive impact while operating efficiency and profitability show significantly negative impact on systematic risk (beta).

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