The effects of Tier-1 capital to risk management, and profitability on performance using multiple fixed effect panel data model

2020 ◽  
Vol 39 (1) ◽  
pp. 117-128 ◽  
Author(s):  
Zulkifli Rangkuti ◽  
Ernasari ◽  
Suhadak ◽  
Sri Mangesti Rahayu ◽  
Solimun
Keyword(s):  
Risk Management ◽  
Panel Data ◽  
Data Model ◽  
Fixed Effect ◽  
Panel Data Model ◽  
Tier 1 ◽  
Tier 1 Capital

scholarly journals The Effect of ASEAN-China Free Trade Area (ACFTA) on Indonesia Export

ETIKONOMI ◽  
2016 ◽  
Vol 15 (2) ◽  
pp. 125-138
Author(s):  
Indriyani Indriyani
Keyword(s):  
Panel Data ◽  
Free Trade ◽  
Cross Section ◽  
Data Model ◽  
Free Trade Area ◽  
Fixed Effect ◽  
Panel Data Model ◽  
Phnom Penh ◽  
Trade Area ◽  
Asean Countries

ASEAN-China Free Trade Area (ACFTA) is an agreement between the members of ASEAN and China to create a free trade area by removing tariff and non-tariff barriers. This agreement begins with the signing of the agreement on November 5, 2002 in Phnom Penh. Implementation is done in phases beginning January 1, 2004. The purpose of this study determines the effect of the implementation of ACFTA on Indonesia's exports to the ASEAN countries and China. This study complements previous research regarding the ACFTA. The data used in this study are the data of Indonesian exports to ASEAN countries and China for 15 years from 2000 until 2014. The tests were conducted with a fixed effect panel data model with cross section SUR. The results of this study indicate that the ACFTA increase Indonesian exports to the ASEAN countries and China.DOI: 10.15408/etk.v15i2.3331

Of Fixed-Effects and Time-Invariant Variables

2011 ◽  
Vol 19 (2) ◽  
pp. 119-122 ◽  
Author(s):  
Nathaniel Beck
Keyword(s):  
Panel Data ◽  
Data Model ◽  
Fixed Effects ◽  
Fixed Effect ◽  
Panel Data Model ◽  
Vector Decomposition ◽  
Time Invariant

What follows is a longish controversy (two critiques, a reply and two rejoinders) over the quality of the estimates and associated SEs provided by Plümper and Troeger's (2007) “fixed-effect vector decomposition” (FEVD) procedure; Plümper and Troeger (PT) will refer to that article and not any persons. My role is to lay out some issues that separate the authors rather than to adjudicate between them. As with many controversies, a bit of heat is generated along with some light. Readers care a bit less than the authors about what was said when, but they do care a lot about what appropriate method to use when a panel data model has both unit-specific intercepts and variables that are invariant over a unit. Thus, I also take it upon myself to discuss some things that I gleaned from this controversy; this discussion has a bit less heat than what follows, but of course readers should judge the evidence for themselves.

The effects of Tier-1 capital to risk management and profitability on performance using multiple fixed effect panel data model

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Zulkifli Rangkuti
Keyword(s):  
Risk Management ◽  
Panel Data ◽  
Indirect Effect ◽  
Commercial Banks ◽  
Stock Exchange ◽  
Content Type ◽  
Tier 1 ◽  
Research Findings ◽  
Positive Direct ◽  
Tier 1 Capital

Purpose This paper aims to examine the effects of Tier-1 capital toward risk management and profitability on the performance of Indonesian Commercial Banks. Design/methodology/approach The research population consisted of all commercial banks listed on the Indonesia Stock Exchange. The data were in the form of financial statements of commercial banks for the periods of 2012 to 2016 with a total of 42 companies (bank). From a total of 42 commercial banks listed in the Indonesia Stock Exchange, not all of them met the criteria. Commercial banks that meet these criteria are as many as 28 banks are sampled research. Findings Tier-1 capital has a positive direct effect on risk management, Tier-1 capital has a positive indirect effect on profitability with risk management as a mediation variable, risk management has a positive direct effect on profitability, Tier-1 capital has a positive indirect effect on performance with risk management and profitability as mediation variables, risk management has a positive indirect effect on performance with as mediation variable and profitability has a positive impact on performance. Originality/value The originality of this research can be seen from the causal relationship between the effects of Tier-1 capital, risk management and profitability on the performance of commercial banks in the context of stock performance among Indonesia commercial banks. Also, the analysis tools using multiple fixed effect panel data models in this research as a novelty in this research. In addition, previous research findings remain inconsistent with one another. By conducting this research, it is expected that more consistent research findings than the previous ones can be generated. Sluggish global economic conditions, which result in declined bank performance are an interesting topic to investigate. The paper uses an original sample, 28 Indonesian banks in 2012-2016. Also, it links Tier 1 capital with risk management and performance in a novel theoretical framework.

scholarly journals Robust Multicollinearity Diagnostic Measure For Fixed Effect Panel Data Model

2021 ◽  
Vol 17 (5) ◽  
pp. 636-646
Author(s):  
Shelan Saied Ismaeel ◽  
Habshah Midi ◽  
Muhammed Sani
Keyword(s):  
Panel Data ◽  
Data Model ◽  
Ordinary Least Squares ◽  
Fixed Effect ◽  
Diagnostic Methods ◽  
Panel Data Model ◽  
Variance Inflation Factor ◽  
Leverage Points ◽  
Inflation Factor ◽  
Erroneous Interpretation

It is now evident that high leverage points (HLPs) can induce the multicollinearity pattern of a data in fixed effect panel data model. Those observations that are responsible for this phenomenon are called high leverage collinearity-enhancing observations (HLCEO). The commonly used within group ordinary least squares (WOLS) estimator for estimating the parameters of fixed effect panel data model is easily affected by HLCEOs. In their presence, the WOLS estimates may produce large variances and this would lead to erroneous interpretation. Therefore, it is imperative to detect the multicollinearity which is caused by HLCEOs. The classical Variance Inflation Factor (CVIF) is the commonly used diagnostic method for detecting multicollinearity in panel data. However, it is not correctly diagnosed multicollinearity in the presence of HLCEOs. Hence, in this paper three new robust diagnostic methods of diagnosing multicollinearity in panel data are proposed, namely the RVIF (WGM-FIMGT), RVIF (WGM-DRGP) and RVIF (WMM) and compared their performances with the CVIF. The numerical evidences show that the CVIF incorrectly diagnosed multicollinearity but our proposed methods correctly diagnosed no multicollinearity in the presence of HLCEOs where RVIF (WGM-FIMGT) being the best method as it has the least computational running time.

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