scholarly journals Monitoring Parameter Constancy with Endogenous Regressors

2017 ◽  
Vol 38 (5) ◽  
pp. 791-805 ◽  
Author(s):  
Eiji Kurozumi
Keyword(s):  
Parameter Constancy ◽  
Endogenous Regressors ◽  
Monitoring Parameter

An Analysis of Office Market Rents: Parameter Constancy and Unobservable Variables

1993 ◽  
Vol 8 (4) ◽  
pp. 625-637 ◽  
Author(s):  
John Glascock ◽  
Minbo Kim ◽  
C. Sirmans
Keyword(s):  
Parameter Constancy ◽  
Office Market

scholarly journals Connectivity and growth: Financial centres in investment banking networks

2021 ◽  
pp. 0308518X2110263
Author(s):  
Vladimír Pažitka ◽  
Michael Urban ◽  
Dariusz Wójcik
Keyword(s):  
Capital Market ◽  
Financial Services ◽  
Network Connectivity ◽  
Positive Association ◽  
Equity Capital ◽  
Regression Equations ◽  
Network Ties ◽  
Urban Network ◽  
Financial Centre ◽  
Endogenous Regressors

We investigate the effect of urban network connectivity on the growth of financial centres. While existing research recognises the importance of network connectivity to firms, clusters as well as city regions, large-sample empirical evidence is currently scarce, particularly in the context of financial services. We contribute to this debate by studying underwriting of equity and debt securities, which represent some of the core activities of financial centres. We operationalise our analysis using a proprietary dataset collated from Dealogic Equity Capital Market and Debt Capital Market databases covering over 1.7 million interactions of investment banks with issuers across 540 cities globally during the 1993–2016 period. We estimate our regression equations using the system generalised method of moments estimator, which allows us to obtain consistent coefficient estimates on potentially endogenous regressors, including network connectivity variables. We identify a clear pattern of a positive association between network centrality of financial centres and their growth. We distinguish between intracity and intercity network connectivity and find that financial centres with a larger number of intercity network ties and assortative intracity networks grow faster, while intracity network density does not appear to affect financial centre growth. Our results on intercity network ties are broadly consistent with established knowledge of cluster networks. In contrast, our findings on financial centres' intracity networks contradict previous research that suggests that dense and disassortative intracluster networks aid economic performance of clusters.

scholarly journals Correction on “Towards Detecting Swath Events in TerraSAR-X Time Series to Establish NATURA 2000 Grassland Habitat Swath Management as Monitoring Parameter”

2012 ◽  
Vol 4 (8) ◽  
pp. 2455-2456 ◽  
Author(s):  
Christian Schuster ◽  
Iftikhar Ali ◽  
Peter Lohmann ◽  
Annett Frick ◽  
Michael Förster ◽  
...  
Keyword(s):  
Time Series ◽  
Natura 2000 ◽  
Monitoring Parameter

scholarly journals Quantile Regression for Panel Data: An Empirical Approach for Knowledge Spillovers Endogeneity

2017 ◽  
Vol 9 (7) ◽  
pp. 106 ◽  
Author(s):  
Luigi Aldieri ◽  
Concetto Paolo Vinci
Keyword(s):  
Theoretical Model ◽  
Panel Data ◽  
Quantile Regression ◽  
Knowledge Spillovers ◽  
Spillover Effect ◽  
Technology Spillovers ◽  
Significant Heterogeneity ◽  
The Core ◽  
Endogenous Regressors ◽  
Different Levels

The aim of this paper is to investigate the extent to which knowledge spillovers effects are sensitive to different levels of innovation. We develop a theoretical model in which the core of spillover effect is showed and then we implement the empirical model to test for the results. In particular, we run the quantile regression for panel data estimator (Baker, Powell, & Smith, 2016), to correct the bias stemming from the endogenous regressors in a panel data sample. The findings identify a significant heterogeneity of technology spillovers across quantiles: the highest value of spillovers is observed at the lowest quartile of innovation distribution. The results might be interpreted to provide some useful implications for industrial policy strategy.

scholarly journals Application of Various Machine Learning Models for Process Stability of Bio-Electrochemical Anaerobic Digestion

Processes ◽  
2022 ◽  
Vol 10 (1) ◽  
pp. 158
Author(s):  
Ain Cheon ◽  
Jwakyung Sung ◽  
Hangbae Jun ◽  
Heewon Jang ◽  
Minji Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Anaerobic Digestion ◽  
Prediction Accuracy ◽  
Input Data ◽  
Process Stability ◽  
Operational Parameters ◽  
Monitoring Parameter ◽  
Efficient Prediction ◽  
Single Input ◽  
Machine Learning Models

The application of a machine learning (ML) model to bio-electrochemical anaerobic digestion (BEAD) is a future-oriented approach for improving process stability by predicting performances that have nonlinear relationships with various operational parameters. Five ML models, which included tree-, regression-, and neural network-based algorithms, were applied to predict the methane yield in BEAD reactor. The results showed that various 1-step ahead ML models, which utilized prior data of BEAD performances, could enhance prediction accuracy. In addition, 1-step ahead with retraining algorithm could improve prediction accuracy by 37.3% compared with the conventional multi-step ahead algorithm. The improvement was particularly noteworthy in tree- and regression-based ML models. Moreover, 1-step ahead with retraining algorithm showed high potential of achieving efficient prediction using pH as a single input data, which is plausibly an easier monitoring parameter compared with the other parameters required in bioprocess models.

scholarly journals Revisiting Gaussian copulas to handle endogenous regressors

2021 ◽  
Author(s):  
Jan-Michael Becker ◽  
Dorian Proksch ◽  
Christian M. Ringle
Keyword(s):  
Regression Models ◽  
Statistical Power ◽  
Multilevel Models ◽  
Gaussian Copula ◽  
Simulation Studies ◽  
Validity Of Results ◽  
Endogenous Regressors ◽  
Copula Approach ◽  
Original Presentation ◽  
And Performance

AbstractMarketing researchers are increasingly taking advantage of the instrumental variable (IV)-free Gaussian copula approach. They use this method to identify and correct endogeneity when estimating regression models with non-experimental data. The Gaussian copula approach’s original presentation and performance demonstration via a series of simulation studies focused primarily on regression models without intercept. However, marketing and other disciplines’ researchers mainly use regression models with intercept. This research expands our knowledge of the Gaussian copula approach to regression models with intercept and to multilevel models. The results of our simulation studies reveal a fundamental bias and concerns about statistical power at smaller sample sizes and when the approach’s primary assumptions are not fully met. This key finding opposes the method’s potential advantages and raises concerns about its appropriate use in prior studies. As a remedy, we derive boundary conditions and guidelines that contribute to the Gaussian copula approach’s proper use. Thereby, this research contributes to ensuring the validity of results and conclusions of empirical research applying the Gaussian copula approach.

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