Agglomeration and innovation: Selection or true effect?

2019 ◽  
Vol 52 (2) ◽  
pp. 423-448
Author(s):  
Li Fang
Keyword(s):  
Cost Effectiveness ◽  
Quantile Regression ◽  
Selection Effect ◽  
Cost Benefit ◽  
Ordinary Least Squares ◽  
Least Squares Regression ◽  
Firm Innovation ◽  
Industrial Policies ◽  
Agglomeration Effect ◽  
Patent Applications

This paper separates two mechanisms through which agglomeration increases average firm innovation: selection (less innovative firms being forced out of agglomerations) and true agglomeration (firms become more innovative). I apply a quantile regression to estimate the distribution of firm innovation and separate these two mechanisms. Linking a unique establishment-level dataset with the patent dataset in the state of Maryland for the period 2004–2013, I find that a 1-mile radius area with above-median employment concentration significantly encourages firm innovation. An average establishment that files for at least one patent during the study period increases citation-weighted patent applications by 31.2% to 31.5% in such employment centers. I also find evidence of selection: non-innovators are 1.3% less likely to survive in agglomerations. The coexistence of agglomeration and selection causes the result of an ordinary least squares regression to be upwardly biased. By eliminating the selection effect, this study more precisely estimates the agglomeration effect, which can be applied to cost–benefit and cost-effectiveness analyses of urban and industrial policies.

scholarly journals Improving estimations in quantile regression model with autoregressive errors

2018 ◽  
Vol 22 (Suppl. 1) ◽  
pp. 97-107 ◽  
Author(s):  
Bahadır Yuzbasi ◽  
Yasin Asar ◽  
Samil Sik ◽  
Ahmet Demiralp
Keyword(s):  
Quantile Regression ◽  
Regression Model ◽  
Simulation Experiment ◽  
Ordinary Least Squares ◽  
Least Squares Regression ◽  
White Noise Process ◽  
Respiratory Mortality ◽  
Explanatory Variables ◽  
Quantile Regression Model ◽  
Regression Approach

An important issue is that the respiratory mortality may be a result of air pollution which can be measured by the following variables: temperature, relative humidity, carbon monoxide, sulfur dioxide, nitrogen dioxide, hydrocarbons, ozone, and particulates. The usual way is to fit a model using the ordinary least squares regression, which has some assumptions, also known as Gauss-Markov assumptions, on the error term showing white noise process of the regression model. However, in many applications, especially for this example, these assumptions are not satisfied. Therefore, in this study, a quantile regression approach is used to model the respiratory mortality using the mentioned explanatory variables. Moreover, improved estimation techniques such as preliminary testing and shrinkage strategies are also obtained when the errors are autoregressive. A Monte Carlo simulation experiment, including the quantile penalty estimators such as lasso, ridge, and elastic net, is designed to evaluate the performances of the proposed techniques. Finally, the theoretical risks of the listed estimators are given.

scholarly journals Using Quantile Regression to Estimate Intervention Effects Beyond the Mean

2019 ◽  
Vol 79 (5) ◽  
pp. 883-910 ◽  
Author(s):  
Spyros Konstantopoulos ◽  
Wei Li ◽  
Shazia Miller ◽  
Arie van der Ploeg
Keyword(s):  
Quantile Regression ◽  
Least Squares ◽  
Large Scale ◽  
Social Science Research ◽  
Empirical Work ◽  
Science Research ◽  
Ordinary Least Squares ◽  
Least Squares Regression ◽  
Intervention Effects ◽  
Education Data

This study discusses quantile regression methodology and its usefulness in education and social science research. First, quantile regression is defined and its advantages vis-à-vis vis ordinary least squares regression are illustrated. Second, specific comparisons are made between ordinary least squares and quantile regression methods. Third, the applicability of quantile regression to empirical work to estimate intervention effects is demonstrated using education data from a large-scale experiment. The estimation of quantile treatment effects at various quantiles in the presence of dropouts is also discussed. Quantile regression is especially suitable in examining predictor effects at various locations of the outcome distribution (e.g., lower and upper tails).

scholarly journals Dependency and health utilities in stroke: Data to inform cost-effectiveness analyses

2016 ◽  
Vol 2 (1) ◽  
pp. 70-76 ◽  
Author(s):  
Myzoon Ali ◽  
Rachael MacIsaac ◽  
Terence J Quinn ◽  
Philip M Bath ◽  
David L Veenstra ◽  
...  
Keyword(s):  
Cost Effectiveness ◽  
Ordinary Least Squares ◽  
Rank Test ◽  
Future Cost ◽  
Least Squares Regression ◽  
Health States ◽  
Health Utilities ◽  
Scale Scores ◽  
Signed Rank Test ◽  
Value Sets

Introduction Health utilities (HU) assign preference weights to specific health states and are required for cost-effectiveness analyses. Existing HU for stroke inadequately reflect the spectrum of post-stroke disability. Using international stroke trial data, we calculated HU stratified by disability to improve precision in future cost-effectiveness analyses. Materials and methods We used European Quality of Life Score (EQ-5D-3L) data from the Virtual International Stroke Trials Archive (VISTA) to calculate HU, stratified by modified Rankin Scale scores (mRS) at 3 months. We applied published value sets to generate HU, and validated these using ordinary least squares regression, adjusting for age and baseline National Institutes of Health Stroke Scale (NIHSS) scores. Results We included 3858 patients with acute ischemic stroke in our analysis (mean age: 67.5 ± 12.5, baseline NIHSS: 12 ± 5). We derived HU using value sets from 13 countries and observed significant international variation in HU distributions (Wilcoxon signed-rank test p < 0.0001, compared with UK values). For mRS = 0, mean HU ranged from 0.88 to 0.95; for mRS = 5, mean HU ranged from −0.48 to 0.22. OLS regression generated comparable HU (for mRS = 0, HU ranged from 0.9 to 0.95; for mRS = 5, HU ranged from −0.33 to 0.15). Patients’ mRS scores at 3 months accounted for 65–71% of variation in the generated HU. Conclusion We have generated HU stratified by dependency level, using a common trial endpoint, and describing expected variability when applying diverse value sets to an international population. These will improve future cost-effectiveness analyses. However, care should be taken to select appropriate value sets.

Firm characteristics, innovation, financial resilience and survival of financial institutions

2019 ◽  
Vol 10 (1) ◽  
pp. 48-73
Author(s):  
Stephen Korutaro Nkundabanyanga ◽  
Elizabeth Mugumya ◽  
Irene Nalukenge ◽  
Moses Muhwezi ◽  
Grace Muganga Najjemba
Keyword(s):  
Financial Institutions ◽  
Ordinary Least Squares ◽  
Least Squares Regression ◽  
Firm Characteristics ◽  
Cross Sectional ◽  
Firm Innovation ◽  
Content Type ◽  
Going Concern ◽  
Innovation Activities ◽  
The Social

Purpose The purpose of this paper is to examine the relationship among firm characteristics, innovation, financial resilience and survival of financial institutions in Uganda. Design/methodology/approach This paper employs a cross-sectional research design, and responses from 143 officers of 40 financial institutions are analyzed using Statistical Package for the Social Sciences. The authors used ordinary least squares regression in testing the hypotheses. Findings The authors find that firm characteristics of size, age, innovation and financial resilience have a predictive force on survival of public interest firms such as financial institutions. Research limitations/implications The implication drawn here is that a combination of firm characteristics, firm innovation and financial resilience explains a significant contribution in the survival chances of financial institutions. However, as much as firm characteristics and financial resilience are significant, innovation explains more of the variances in financial institutions’ going concern appropriateness. Originality/value This paper adds to the limited financial institutions literature and provides the first empirical evidence of the efficacy of innovation and financial resilience on financial institutions survival. The auditing profession could consider more seriously the innovation activities and financial resilience of financial institutions in their test for the going concern assumption of such firms.

Quantile Regression Analysis and Other Alternatives to Ordinary Least Squares Regression

Methodology ◽  
2014 ◽  
Vol 10 (3) ◽  
pp. 81-91 ◽  
Author(s):  
Harry Haupt ◽  
Friedrich Lösel ◽  
Mark Stemmler
Keyword(s):  
Regression Analysis ◽  
Quantile Regression ◽  
Least Squares ◽  
Corporal Punishment ◽  
Ordinary Least Squares ◽  
Study Data ◽  
Physical Punishment ◽  
Least Squares Regression ◽  
Prevention Project ◽  
Quantile Regression Analysis

Data analyses by classical ordinary least squares (OLS) regression techniques often employ unrealistic assumptions, fail to recognize the source and nature of heterogeneity, and are vulnerable to extreme observations. Therefore, this article compares robust and non-robust M-estimator regressions in a statistical demonstration study. Data from the Erlangen-Nuremberg Development and Prevention Project are used to model risk factors for physical punishment by fathers of 485 elementary school children. The Corporal Punishment Scale of the Alabama Parenting Questionnaire was the dependent variable. Fathers’ aggressiveness, dysfunctional parent-child relations, various other parenting characteristics, and socio-demographic variables served as predictors. Robustness diagnostics suggested the use of trimming procedures and outlier diagnostics suggested the use of robust estimators as an alternative to OLS. However, a quantile regression analysis provided more detailed insights beyond the measures of central tendency and detected sources of considerable heterogeneity in the risk structure of father’s corporal punishment. Advantages of this method are discussed with regard to methodological and content issues.

Quantile regression: an alternative approach to modelling forest area burned by individual fires

2018 ◽  
Vol 27 (8) ◽  
pp. 538 ◽  
Author(s):  
Baburam Rijal
Keyword(s):  
Quantile Regression ◽  
Forest Ecosystems ◽  
Fire Regime ◽  
Model Fitting ◽  
Ordinary Least Squares ◽  
Mean Value ◽  
Mixed Effects ◽  
Least Squares Regression ◽  
Fire Size ◽  
Large Fires

Components of a fire regime have long been estimated using mean-value-based ordinary least-squares regression. But, forest and fire managers require predictions beyond the mean because impacts of small and large fires on forest ecosystems and wildland–urban interfaces are different. Therefore, different action plans are required to manage potential fires of varying sizes that demand size-based modelling tools. The objective of this study was to compare two model-fitting techniques, namely quantile mixed-effects (QME) model and ordinary linear mixed-effects (LME) model for constructing distributions of model-predicted small and large fires. I examined these techniques by modelling the fire size of individual escaped wildfires. Results showed that the LME-predicted fire size approximately coincided to the 0.75 quantile. The LME model produced more biased predictions at the two extremes, both of which manifest great importance in forest ecosystems and fire management. Modelling the distributions for small and large fires using quantile regression can reduce such biases along with giving unbiased mean estimates. This study concludes that quantile modelling is an effective approach to complement ordinary regression that helps predict the size-based risks of individual fires more precisely, and that could allow managers to better plan resources when managing fires.

scholarly journals Harvesting reflective knowledge exchange for inbound open innovation in complex collaborative networks: an empirical verification in Europe

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Armando Papa ◽  
Roberto Chierici ◽  
Luca Vincenzo Ballestra ◽  
Dirk Meissner ◽  
Mehmet A. Orhan
Keyword(s):  
Open Innovation ◽  
Knowledge Exchange ◽  
External Environment ◽  
Innovation Process ◽  
Ordinary Least Squares ◽  
Collaborative Networks ◽  
Reflective Knowledge ◽  
Least Squares Regression ◽  
Content Type ◽  
Patent Applications

Purpose This study aims to investigate the effects of open innovation (OI) and big data analytics (BDA) on reflective knowledge exchange (RKE) within the context of complex collaborative networks. Specifically, it considers the relationships between sourcing knowledge from an external environment, transferring knowledge to an external environment and adopting solutions that are useful to appropriate returns from innovation. Design/methodology/approach This study analyzes the connection between the number of patent applications and the amount of OI, as well as the association between the number of patent applications and the use of BDA. Data from firms in the 27 European Union countries were retrieved from the Eurostat database for the period 2014–2019 and were investigated using an ordinary least squares regression analysis. Findings Because of its twofold lens based on both knowledge management and OI, this study sheds light on OI collaboration modes and highlights the crucial role they could play in innovation. In particular, the results suggest that OI collaboration modes have a strong effect on innovation performance, stimulating the search for RKE. Originality/value This study furthers a deeper understanding of RKE, which is shown to be an important mechanism that incentivizes firms to increase their efforts in the innovation process. Further, RKE supports firms in taking full advantage of the innovative knowledge they generate within their inter-organizational network.

Large shareholder participation behaviors, managers’ risk-taking and firm innovation performance

2018 ◽  
Vol 9 (1) ◽  
pp. 99-115 ◽  
Author(s):  
Feng Zhang ◽  
Jianjun Yang ◽  
Zhi Xu ◽  
Guilong Zhu
Keyword(s):  
Risk Taking ◽  
Ordinary Least Squares ◽  
Innovation Performance ◽  
Least Squares Regression ◽  
Firm Innovation ◽  
Content Type ◽  
Chinese Firm ◽  
Large Shareholder ◽  
Strategic Consensus ◽  
Large Shareholders

Purpose Focusing on internal corporate governance, the purpose of this paper is to apply the shareholder activism perspective to consider how large shareholder participation behaviors might influence firm innovation performance. Specifically, “confrontationally strategic intervention” and “cooperatively strategic consensus” participation behaviors are examined and hypothesized to have different effects on managers’ risk-taking and firm innovation performance. Design/methodology/approach Drawing on 182 Chinese firm samples, this paper applies hierarchical ordinary least-squares regression analysis to test the proposed hypotheses. Findings The results show that strategic intervention was negatively associated with managers’ risk-taking and firm innovation performance, while strategic consensus positively affected managers’ risk-taking and firm innovation performance. Moreover, managers’ risk-taking fully mediated the influence of strategic intervention on firm innovation performance, whereas it partially mediated the influence of strategic consensus on firm innovation performance. Originality/value The study extends research on shareholder participation by construing that large shareholders’ participation behaviors can significantly influence managers’ risk-taking and corporate innovation performance, further deepening the understanding of the influences of large shareholders on the firm-level outcomes. The theoretical and practical implications of this finding are also discussed.

scholarly journals Insights from analysing tourist expenditure using quantile regression

2016 ◽  
Vol 23 (5) ◽  
pp. 1138-1145 ◽  
Author(s):  
António Almeida ◽  
Brian Garrod
Keyword(s):  
Quantile Regression ◽  
Least Squares ◽  
Empirical Studies ◽  
Ordinary Least Squares ◽  
Estimation Technique ◽  
Least Squares Regression ◽  
Data Set ◽  
Detailed Understanding ◽  
Tourism Destinations ◽  
Single Data

Mature tourism destinations are increasingly needing to diversify their products and markets. To be successful, such strategies require a very detailed understanding of potential tourists’ levels and patterns of spending. Empirical studies of tourist expenditure have tended to employ ordinary least squares regression for this purpose. There are, however, a number of important limitations to this technique, chief among which is its inability to distinguish between tourists who have higher- and lower-than-average levels of spending. As such, some researchers recommend the use of an alternative estimation technique, known as quantile regression, which does allow such distinctions to be made. This study uses a single data set, collected among rural tourists in Madeira, to analyse the determinants of tourist expenditure using both techniques. This enables direct comparison to be made and illustrates the additional insights to be gained using quantile regression.

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