Relevant bibliographies by topics / Effort estimation

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Effort estimation'

Author: Grafiati
Published: 4 June 2021
Last updated: 11 September 2023

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Journal articles on the topic "Effort estimation"

1

Capretz, Luiz Fernando, and Venus Marza. "Improving Effort Estimation by Voting Software Estimation Models." Advances in Software Engineering 2009 (September 1, 2009): 1–8. http://dx.doi.org/10.1155/2009/829725.

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Estimating software development effort is an important task in the management of large software projects. The task is challenging, and it has been receiving the attentions of researchers ever since software was developed for commercial purpose. A number of estimation models exist for effort prediction. However, there is a need for novel models to obtain more accurate estimations. The primary purpose of this study is to propose a precise method of estimation by selecting the most popular models in order to improve accuracy. Consequently, the final results are very precise and reliable when they are applied to a real dataset in a software project. Empirical validation of this approach uses the International Software Benchmarking Standards Group (ISBSG) Data Repository Version 10 to demonstrate the improvement in software estimation accuracy.
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Yücalar, Fatih, Deniz Kilinc, Emin Borandag, and Akin Ozcift. "Regression Analysis Based Software Effort Estimation Method." International Journal of Software Engineering and Knowledge Engineering 26, no. 05 (June 2016): 807–26. http://dx.doi.org/10.1142/s0218194016500261.

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Estimating the development effort of a software project in the early stages of the software life cycle is a significant task. Accurate estimates help project managers to overcome the problems regarding budget and time overruns. This paper proposes a new multiple linear regression analysis based effort estimation method, which has brought a different perspective to the software effort estimation methods and increased the success of software effort estimation processes. The proposed method is compared with standard Use Case Point (UCP) method, which is a well-known method in this area, and simple linear regression based effort estimation method developed by Nassif et al. In order to evaluate and compare the proposed method, the data of 10 software projects developed by four well-established software companies in Turkey were collected and datasets were created. When effort estimations obtained from datasets and actual efforts spent to complete the projects are compared with each other, it has been observed that the proposed method has higher effort estimation accuracy compared to the other methods.
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Ayyıldız, Tülin Erçelebi, and Hasan Can Terzi. "Case Study on Software Effort Estimation." International Journal of Information and Electronics Engineering 7, no. 3 (May 2017): 103–7. http://dx.doi.org/10.18178/ijiee.2017.7.3.670.

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Şengüneş, Burcu, and Nursel Öztürk. "An Artificial Neural Network Model for Project Effort Estimation." Systems 11, no. 2 (February 9, 2023): 91. http://dx.doi.org/10.3390/systems11020091.

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Estimating the project effort remains a challenge for project managers and effort estimators. In the early phases of a project, having a high level of uncertainty and lack of experience cause poor estimation of the required work. Especially for projects that produce a highly customized unique product for each customer, it is challenging to make estimations. Project effort estimation has been studied mainly for software projects in the literature. Currently, there has been no study on estimating effort in customized machine development projects to the best of our knowledge. This study aims to fill this gap in the literature regarding project effort estimation for customized machine development projects. Additionally, this study focused on a single phase of a project, the automation phase, in which the machine is automated according to customer-specific requirements. Therefore, the effort estimation of this phase is crucial. In some cases, this is the first time that the company has experienced the requirements specific to the customer. For this purpose, this study proposed a model to estimate how much work is required to automate a machine. Insufficient effort estimation is one of the main reasons behind project failures, and nowadays, researchers prefer more objective approaches such as machine learning over expert-based ones. This study also proposed an artificial neural network (ANN) model for this purpose. Data from past projects were used to train the proposed ANN model. The proposed model was tested on 11 real-life projects and showed promising results with acceptable prediction accuracy. Additionally, a desktop application was developed to make this system easier to use for project managers.
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Pagadala, Srivyshnavi, Sony Bathala, and B. Uma. "An Efficient Predictive Paradigm for Software Reliability." Asian Journal of Computer Science and Technology 8, S3 (June 5, 2019): 114–16. http://dx.doi.org/10.51983/ajcst-2019.8.s3.2051.

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Software Estimation gives solution for complex problems in the software industry which gives estimates for cost and schedule. Software Estimation provides a comprehensive set of tips and heuristics that Software Developers, Technical Leads, and Project Managers can apply to create more accurate estimates. It presents key estimation strategies and addresses particular estimation challenges. In the planning of a software development project, a major challenge faced by project managers is to predict the defects and effort. The Software defect plays critical role in software product development. The estimation of defects can be determined in the product development using many advanced statistical modelling techniques based on the empirical data obtained by the testing phases. The proposed estimation technique in this paper is a model which was developed using Rayleigh function for estimating effect of defects in Software Project Management. The present study offers to decide how many defects creep in to production and determine the effort spent in months. The estimation model was used on Software Testing Life Cycle (STLC) to complete product. The accuracy of the model explains the variation in spent efforts in months associated with number of defects. The model helps the senior management in estimating the defects, schedule, cost and effort.
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Iwata, Kazunori, Toyoshiro Nakashima, Yoshiyuki Anan, and Naohiro Ishii. "Machine Learning Classification to Effort Estimation for Embedded Software Development Projects." International Journal of Software Innovation 5, no. 4 (October 2017): 19–32. http://dx.doi.org/10.4018/ijsi.2017100102.

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This paper discusses the effect of classification in estimating the amount of effort (in man-days) associated with code development. Estimating the effort requirements for new software projects is especially important. As outliers are harmful to the estimation, they are excluded from many estimation models. However, such outliers can be identified in practice once the projects are completed, and so they should not be excluded during the creation of models and when estimating the required effort. This paper presents classifications for embedded software development projects using an artificial neural network (ANN) and a support vector machine. After defining the classifications, effort estimation models are created for each class using linear regression, an ANN, and a form of support vector regression. Evaluation experiments are carried out to compare the estimation accuracy of the model both with and without the classifications using 10-fold cross-validation. In addition, the Games-Howell test with one-way analysis of variance is performed to consider statistically significant evidence.
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Puspaningrum, Alifia, Fachrul Pralienka Bani Muhammad, and Esti Mulyani. "Flower Pollination Algorithm for Software Effort Coefficients Optimization to Improve Effort Estimation Accuracy." JUITA: Jurnal Informatika 9, no. 2 (November 30, 2021): 139. http://dx.doi.org/10.30595/juita.v9i2.10511.

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Software effort estimation is one of important area in project management which used to predict effort for each person to develop an application. Besides, Constructive Cost Model (COCOMO) II is a common model used to estimate effort estimation. There are two coefficients in estimating effort of COCOMO II which highly affect the estimation accuracy. Several methods have been conducted to estimate those coefficients which can predict a closer value between actual effort and predicted value. In this paper, a new metaheuristic algorithm which is known as Flower Pollination Algorithm (FPA) is proposed in several scenario of iteration. Besides, FPA is also compared to several metaheuristic algorithm, namely Cuckoo Search Algorithm and Particle Swarm Optimization. After evaluated by using Mean Magnitude of Relative Error (MMRE), experimental results show that FPA obtains the best result in estimating effort compared to other algorithms by reached 52.48% of MMRE in 500 iterations.
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Deng, Jeremiah D., Martin Purvis, and Maryam Purvis. "Software Effort Estimation." International Journal of Intelligent Information Technologies 7, no. 3 (July 2011): 41–53. http://dx.doi.org/10.4018/jiit.2011070104.

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Software development effort estimation is important for quality management in the software development industry, yet its automation still remains a challenging issue. Applying machine learning algorithms alone often cannot achieve satisfactory results. This paper presents an integrated data mining framework that incorporates domain knowledge into a series of data analysis and modeling processes, including visualization, feature selection, and model validation. An empirical study on the software effort estimation problem using a benchmark dataset shows the necessity and effectiveness of the proposed approach.
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Basten, Dirk, and Thomas Hoerstrup. "Organizational Effort Estimation." Computer 47, no. 8 (August 2014): 76–79. http://dx.doi.org/10.1109/mc.2014.216.

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Ludwig, D., and C. J. Walters. "A Robust Method for Parameter Estimation from Catch and Effort Data." Canadian Journal of Fisheries and Aquatic Sciences 46, no. 1 (January 1, 1989): 137–44. http://dx.doi.org/10.1139/f89-018.

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The problem of robust estimation of optimal effort levels from surplus production models is considered. A variety of models are used to generate data, for the purpose of testing estimation schemes. The result of an estimation is an estimate of the optimal effort. These efforts are compared using the expected discounted value of a deterministic stock, which corresponds to the model used to generate the data. Such a criterion takes into account not only the loss due to bias in the estimated optimal effort, but also the loss due to the variance of the estimator. Estimation is difficult if there is a lack of informative variation in effort levels or stock sizes. In such cases, the estimation scheme which maximizes the criterion described above sacrifices realism in the representation of the stock-production relationship in order to reduce the variance of the estimate of optimal effort. We present a composite estimation scheme which performs acceptably in all the cases we have examined, and whose performance degrades slowly as the amount of information in the data decreases.
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Dissertations / Theses on the topic "Effort estimation"

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Tunalilar, Seckin. "Efes: An Effort Estimation Methodology." Phd thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12613847/index.pdf.

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The estimation of effort is at the heart of project tasks, since it is used for many purposes such as cost estimation, budgeting, monitoring, project planning, control and software investments. Researchers analyze problems of the estimation, propose new models and use new techniques to improve accuracy. However up to now, there is no comprehensive estimation methodology to guide companies in their effort estimation tasks. Effort estimation problem is not only a computational but also a managerial problem. It requires estimation goals, execution steps, applied measurement methods and updating mechanisms to be properly defined. Besides project teams should have motivation and responsibilities to build a reliable database. If such methodology is not defined, common interpretation will not be constituted among software teams of the company, and variances in measurements and divergences in collected information prevents to collect sufficient historical information for building accurate models. This thesis proposes a methodology for organizations to manage and execute effort estimation processes. The approach is based on the reported best practices, v empirical results of previous studies and solutions to problems &
conflicts described in literature. Five integrated processes: Data Collection, Size Measurement, Data Analysis, Calibration, Effort Estimation processes are developed with their artifacts, procedures, checklists and templates. The validation and applicability of the methodology is checked in a middle-size software company. During the validation of methodology we also evaluated some concepts such as Functional Similarity (FS) and usage of Base Functional Components (BFC) in effort model on a reliable dataset. By this way we evaluated whether these subjects should be a part of methodology or not. Besides in this study it is the first time that the COSMIC has been used for Artificial Neural Network models.
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Nabi, Mina. "A Software Benchmarking Methodology For Effort Estimation." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614648/index.pdf.

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Software project managers usually use benchmarking repositories to estimate effort, cost, and duration of the software development which will be used to appropriately plan, monitor and control the project activities. In addition, precision of benchmarking repositories is a critical factor in software effort estimation process which plays subsequently a critical role in the success of the software development project. In order to construct such a precise benchmarking data repository, it is important to have defined benchmarking data attributes and data characteristics and to have collected project data accordingly. On the other hand, studies show that data characteristics of benchmark data sets have impact on generalizing the studies which are based on using these datasets. Quality of data repository is not only depended on quality of collected data, but also it is related to how these data are collected. In this thesis, a benchmarking methodology is proposed for organizations to collect benchmarking data for effort estimation purposes. This methodology consists of three main components: benchmarking measures, benchmarking data collection processes, and benchmarking data collection tool. In this approach results of previous studies from the literature were used too. In order to verify and validate the methodology project data were collected in two middle size software organizations and one small size organization by using automated benchmarking data collection tool. Also, effort estimation models were constructed and evaluated for these projects data and impact of different characteristics of the projects was inspected in effort estimation models.
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Usman, Muhammad. "Supporting Effort Estimation in Agile Software Development." Licentiate thesis, Karlskrona, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-10961.

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Background: In Agile Software Development (ASD) planning is valued more than the resulting plans. Planning and estimation are carried out at multiple levels in ASD. Agile plans and estimates are frequently updated to reflect the current situation. It supports shorter release cycles and flexibility to incorporate changing market and customer needs. Many empirical studies have been conducted to investigate effort estimation in ASD. However, the evidence on effort estimation in ASD has not been aggregated and organized. Objective: This thesis has two main objectives: First, to identify and aggregate evidence, from both literature and industry, on effort estimation in ASD. Second, to support research and practice on effort estimation in ASD by organizing the identified knowledge. Method: In this thesis we conducted a Systematic Literature Review (SLR), a systematic mapping study, a questionnaire based industrial survey and an interview based survey. Results: The SLR and survey results showed that agile teams estimate effort, mostly during release and iteration planning, using techniques that are based on experts' subjective assessments. During effort estimation team related cost drivers, such as team members’ expertise, are considered important. The results also highlighted that implementation and testing are the only activities that are accounted for in effort estimates by most agile teams. Our mapping study identified that taxonomies in SE are mostly designed and presented in an ad-hoc manner. To fill this gap we updated an existing method to design taxonomies in a systematic way. The method is then used to design taxonomy on effort estimation in ASD using the evidence identified in our SLR and survey as input. Conclusions: The proposed taxonomy is evaluated by characterizing effort estimation cases of selected agile projects reported in literature. The evaluation found that the reporting of the selected studies lacks information related to the context and predictors used during effort estimation in ASD. The taxonomy can be used in consistently reporting effort estimation studies in ASD to facilitate identification, aggregation and analysis of the evidence. The proposed taxonomy was also used to characterize the effort estimation activity of agile teams in three different software companies. The proposed taxonomy was found to be useful by interviewed agile practitioners in documenting important effort estimation related knowledge, which otherwise remain tacit in most cases.
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Vukovic, Divna, and Cecilia Wester. "Staff Prediction Analysis : Effort Estimation In System Test." Thesis, Blekinge Tekniska Högskola, Institutionen för programvaruteknik och datavetenskap, 2001. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-1739.

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This master thesis is made in 2001 at Blekinge Institute of Technology and Symbian, which is a software company in Ronneby, Sweden. The purpose of the thesis is to find a suitable prediction and estimation model for the test effort. To do this, we have studied the State of the Art in cost/effort estimation and fault prediction. The conclusion of this thesis is that it is hard to make a general proposal, which is applicable for all organisations. For Symbian we have proposed a model based on use and test cases to predict the test effort.
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Sarro, Federica. "Search-based approaches for software development effort estimation." Doctoral thesis, Universita degli studi di Salerno, 2015. http://hdl.handle.net/10556/1969.

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2011 - 2012
Effort estimation is a critical activity for planning and monitoring software project development and for delivering the product on time and within budget. Significant over or under-estimates expose a software project to several risks. As a matter of fact under-estimates could lead to addition of manpower to a late software project, making the project later (Brooks’s Law), or to the cancellation of activities, such as documentation and testing, negatively impacting on software quality and maintainability. Thus, the competitiveness of a software company heavily depends on the ability of its project managers to accurately predict in advance the effort required to develop software system. However, several challenges exists in making accurate estimates, e.g., the estimation is needed early in the software lifecycle, when few information about the project are available, or several factors can impact on project effort and these factor are usually specific for different production contexts. Several techniques have been proposed in the literature to support project manager in estimating software project development effort. In the last years the use of Search-Based (SB) approaches has been suggested to be employed as an effort estimation technique. These approaches include a variety of meta-heuristics, such as local search techniques (e.g., Hill Climbing, Tabu Search, Simulated Annealing) or Evolutionary Algorithms (e.g., Genetic Algorithms, Genetic Programming). The idea underlying the use of such techniques is based on the reformulation of software engineering problems as search or optimization problems whose goal is to find the most appropriate solutions which conform to some adequacy criteria (i.e., problem goals). In particular, the use of SB approaches in the context of effort estimation is twofold: they can be exploited to build effort estimation models or to enhance the use of existing effort estimation techniques. The usage reported in the literature of SB approaches for effort estimation have provided promising results that encourage further investigations. However, they can be considered preliminary studies. As a matter of fact, the capabilities of these approaches were not fully exploited, either the employed empirical analyses did not consider the more recent recommendations on how to carry out this kind of empirical assessment in the effort estimation and in the SBSE contexts. The main aim of the PhD dissertation is to provide an insight on the use of SB techniques for the effort estimation trying to highlight strengths and weaknesses of these approaches for both the uses above mentioned. [edited by Author]
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Marshall, Ian Mitchell. "Evaluating courseware development effort estimation measures and models." Thesis, University of Abertay Dundee, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.318946.

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Azzeh, Mohammad Y. A. "Analogy-based software project effort estimation : contributions to projects similarity measurement, attribute selection and attribute weighting algorithms for analogy-based effort estimation." Thesis, University of Bradford, 2010. http://hdl.handle.net/10454/4442.

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Software effort estimation by analogy is a viable alternative method to other estimation techniques, and in many cases, researchers found it outperformed other estimation methods in terms of accuracy and practitioners' acceptance. However, the overall performance of analogy based estimation depends on two major factors: similarity measure and attribute selection & weighting. Current similarity measures such as nearest neighborhood techniques have been criticized that have some inadequacies related to attributes relevancy, noise and uncertainty in addition to the problem of using categorical attributes. This research focuses on improving the efficiency and flexibility of analogy-based estimation to overcome the abovementioned inadequacies. Particularly, this thesis proposes two new approaches to model and handle uncertainty in similarity measurement method and most importantly to reflect the structure of dataset on similarity measurement using Fuzzy modeling based Fuzzy C-means algorithm. The first proposed approach called Fuzzy Grey Relational Analysis method employs combined techniques of Fuzzy set theory and Grey Relational Analysis to improve local and global similarity measure and tolerate imprecision associated with using different data types (Continuous and Categorical). The second proposed approach presents the use of Fuzzy numbers and its concepts to develop a practical yet efficient approach to support analogy-based systems especially at early phase of software development. Specifically, we propose a new similarity measure and adaptation technique based on Fuzzy numbers. We also propose a new attribute subset selection algorithm and attribute weighting technique based on the hypothesis of analogy-based estimation that assumes projects that are similar in terms of attribute value are also similar in terms of effort values, using row-wise Kendall rank correlation between similarity matrix based project effort values and similarity matrix based project attribute values. A literature review of related software engineering studies revealed that the existing attribute selection techniques (such as brute-force, heuristic algorithms) are restricted to the choice of performance indicators such as (Mean of Magnitude Relative Error and Prediction Performance Indicator) and computationally far more intensive. The proposed algorithms provide sound statistical basis and justification for their procedures. The performance figures of the proposed approaches have been evaluated using real industrial datasets. Results and conclusions from a series of comparative studies with conventional estimation by analogy approach using the available datasets are presented. The studies were also carried out to statistically investigate the significant differences between predictions generated by our approaches and those generated by the most popular techniques such as: conventional analogy estimation, neural network and stepwise regression. The results and conclusions indicate that the two proposed approaches have potential to deliver comparable, if not better, accuracy than the compared techniques. The results also found that Grey Relational Analysis tolerates the uncertainty associated with using different data types. As well as the original contributions within the thesis, a number of directions for further research are presented. Most chapters in this thesis have been disseminated in international journals and highly refereed conference proceedings.
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Andersson, Veronika, and Hanna Sjöstedt. "Improved effort estimation of software projects based on metrics." Thesis, Linköping University, Department of Electrical Engineering, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-5269.

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Saab Ericsson Space AB develops products for space for a predetermined price. Since the price is fixed, it is crucial to have a reliable prediction model to estimate the effort needed to develop the product. In general software effort estimation is difficult, and at the software department this is a problem.

By analyzing metrics, collected from former projects, different prediction models are developed to estimate the number of person hours a software project will require. Models for predicting the effort before a project begins is first developed. Only a few variables are known at this state of a project. The models developed are compared to a current model used at the company. Linear regression models improve the estimate error with nine percent units and nonlinear regression models improve the result even more. The model used today is also calibrated to improve its predictions. A principal component regression model is developed as well. Also a model to improve the estimate during an ongoing project is developed. This is a new approach, and comparison with the first estimate is the only evaluation.

The result is an improved prediction model. There are several models that perform better than the one used today. In the discussion, positive and negative aspects of the models are debated, leading to the choice of a model, recommended for future use.

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Schofield, Christopher. "An empirical investigation into software effort estimation by analogy." Thesis, Bournemouth University, 1998. http://eprints.bournemouth.ac.uk/411/.

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Most practitioners recognise the important part accurate estimates of development effort play in the successful management of major software projects. However, it is widely recognised that current estimation techniques are often very inaccurate, while studies (Heemstra 1992; Lederer and Prasad 1993) have shown that effort estimation research is not being effectively transferred from the research domain into practical application. Traditionally, research has been almost exclusively focused on the advancement of algorithmic models (e.g. COCOMO (Boehm 1981) and SLIM (Putnam 1978)), where effort is commonly expressed as a function of system size. However, in recent years there has been a discernible movement away from algorithmic models with non-algorithmic systems (often encompassing machine learning facets) being actively researched. This is potentially a very exciting and important time in this field, with new approaches regularly being proposed. One such technique, estimation by analogy, is the focus of this thesis. The principle behind estimation by analogy is that past experience can often provide insights and solutions to present problems. Software projects are characterised in terms of collectable features (such as the number of screens or the size of the functional requirements) and stored in a historical case base as they are completed. Once a case base of sufficient size has been cultivated, new projects can be estimated by finding similar historical projects and re-using the recorded effort. To make estimation by analogy feasible it became necessary to construct a software tool, dubbed ANGEL, which allowed the collection of historical project data and the generation of estimates for new software projects. A substantial empirical validation of the approach was made encompassing approximately 250 real historical software projects across eight industrial data sets, using stepwise regression as a benchmark. Significance tests on the results accepted the hypothesis (at the 1% confidence level) that estimation by analogy is a superior prediction system to stepwise regression in terms of accuracy. A study was also made of the sensitivity of the analogy approach. By growing project data sets in a pseudo time-series fashion it was possible to answer pertinent questions about the approach, such as, what are the effects of outlying projects and what is the minimum data set size? The main conclusions of this work are that estimation by analogy is a viable estimation technique that would seem to offer some advantages over algorithmic approaches including, improved accuracy, easier use of categorical features and an ability to operate even where no statistical relationships can be found.
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Kanneganti, Alekhya. "Using Ensemble Machine Learning Methods in Estimating Software Development Effort." Thesis, Blekinge Tekniska Högskola, Institutionen för datavetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-20691.

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Background: Software Development Effort Estimation is a process that focuses on estimating the required effort to develop a software project with a minimal budget. Estimating effort includes interpretation of required manpower, resources, time and schedule. Project managers are responsible for estimating the required effort. A model that can predict software development effort efficiently comes in hand and acts as a decision support system for the project managers to enhance the precision in estimating effort. Therefore, the context of this study is to increase the efficiency in estimating software development effort. Objective: The main objective of this thesis is to identify an effective ensemble method to build and implement it, in estimating software development effort. Apart from this, parameter tuning is also implemented to improve the performance of the model. Finally, we compare the results of the developed model with the existing models. Method: In this thesis, we have adopted two research methods. Initially, a Literature Review was conducted to gain knowledge on the existing studies, machine learning techniques, datasets, ensemble methods that were previously used in estimating Software Development Effort. Then a controlled Experiment was conducted in order to build an ensemble model and to evaluate the performance of the ensemble model for determining if the developed model has a better performance when compared to the existing models.   Results: After conducting literature review and collecting evidence, we have decided to build and implement stacked generalization ensemble method in this thesis, with the help of individual machine learning techniques like Support vector regressor (SVR), K-Nearest Neighbors regressor (KNN), Decision Tree Regressor (DTR), Linear Regressor (LR), Multi-Layer Perceptron Regressor (MLP) Random Forest Regressor (RFR), Gradient Boosting Regressor (GBR), AdaBoost Regressor (ABR), XGBoost Regressor (XGB). Likewise, we have decided to implement Randomized Parameter Optimization and SelectKbest function to implement feature section. Datasets like COCOMO81, MAXWELL, ALBERCHT, DESHARNAIS were used. Results of the experiment show that the developed ensemble model performs at its best, for three out of four datasets. Conclusion: After evaluating and analyzing the results obtained, we can conclude that the developed model works well with the datasets that have continuous, numeric type of values. We can also conclude that the developed ensemble model outperforms other existing models when implemented with COCOMO81, MAXWELL, ALBERCHT datasets.
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Books on the topic "Effort estimation"

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Trendowicz, Adam, and Ross Jeffery. Software Project Effort Estimation. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-03629-8.

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Vesterinen, Petri. On effort estimation in software projects. Lappeenranta, Finland: Lappeenranta University of Technology, 2000.

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Schofield, Christopher. An Empirical investigation into software effort estimation by analogy. Poole: Bournemouth University, 1998.

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Singh, Jasveer. Functional Software Size Measurement Methodology with Effort Estimation and Performance Indication. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2017. http://dx.doi.org/10.1002/9781119238126.

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Cahalan, Jennifer A. Catch sampling and estimation in the federal groundfish fisheries off Alaska. Seattle, Wash.]: U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, National Marine Fisheries Service, Alaska Fisheries Science Center, 2010.

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Gilhousen, Philip. Estimation of Fraser River sockeye escapements from commercial harvest data, 1892-1944. Vancouver, B.C., Canada: International Pacific Salmon Fisheries Commission, 1992.

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Mayo, R. K. Estimation of standardized otter trawl effort, landings per unit effort, and landings at age for Gulf of Maine and Georges Bank cod. Woods Hole, MA: NOAA/National Marine Fisheries Service, Northeast Fisheries Science Center, Conservation and Utilization Division, 1994.

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Office, General Accounting. Credit reform: Greater effort needed to overcome persistent cost estimation problems : report to the Chairman, Committee on the Budget, U.S. Senate. Washington, D.C. (P.O. Box 37050, Washington, D.C. 20013): The Office, 1998.

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Office, General Accounting. Credit reform: Greater effort needed to overcome persistent cost estimation problems : report to the Chairman, Committee on the Budget, U.S. Senate. Washington, D.C. (P.O. Box 37050, Washington 20013): The Office, 1998.

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Smith, Gretchen Cole. Ozone bioindicator sampling and estimation. Newtown Square, PA: U.S. Dept. of Agriculture, Forest Service, Northern Research Station, 2007.

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Book chapters on the topic "Effort estimation"

1

Trendowicz, Adam, and Ross Jeffery. "Estimation Under Uncertainty." In Software Project Effort Estimation, 81–124. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-03629-8_4.

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Trendowicz, Adam, and Ross Jeffery. "Basic Estimation Strategies." In Software Project Effort Estimation, 125–52. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-03629-8_5.

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Trendowicz, Adam, and Ross Jeffery. "Continuously Improving Effort Estimation." In Software Project Effort Estimation, 367–99. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-03629-8_16.

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Trendowicz, Adam, and Ross Jeffery. "Effort Estimation Best Practices." In Software Project Effort Estimation, 401–32. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-03629-8_17.

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Trendowicz, Adam, and Ross Jeffery. "Challenges of Predictable Software Development." In Software Project Effort Estimation, 3–10. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-03629-8_1.

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Trendowicz, Adam, and Ross Jeffery. "Classification and Regression Trees." In Software Project Effort Estimation, 295–304. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-03629-8_10.

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Trendowicz, Adam, and Ross Jeffery. "Case-Based Reasoning." In Software Project Effort Estimation, 305–13. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-03629-8_11.

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Trendowicz, Adam, and Ross Jeffery. "Wideband Delphi." In Software Project Effort Estimation, 315–26. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-03629-8_12.

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Trendowicz, Adam, and Ross Jeffery. "Planning Poker." In Software Project Effort Estimation, 327–38. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-03629-8_13.

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Trendowicz, Adam, and Ross Jeffery. "Bayesian Belief Networks (BBN)." In Software Project Effort Estimation, 339–48. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-03629-8_14.

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Conference papers on the topic "Effort estimation"

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Mendes, Emilia, Sergio Di Martino, Filomena Ferrucci, and Carmine Gravino. "Effort estimation." In the 16th international conference. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1242572.1242702.

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Ferrucci, Filomena, Emilia Mendes, and Federica Sarro. "Web effort estimation." In the 8th International Conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2365324.2365330.

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Matos, Olavo, Luiz Fortaleza, Tayana Conte, and Emilia Mendes. "Realising web effort estimation." In the 17th International Conference. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2460999.2461002.

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Costagliola, Gennaro, Sergio Di Martino, Filomena Ferrucci, Carmine Gravino, Genoveffa Tortora, and Giuliana Vitiello. "Effort estimation modeling techniques." In the 6th international conference. New York, New York, USA: ACM Press, 2006. http://dx.doi.org/10.1145/1145581.1145584.

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Qi, Kan, and Barry W. Boehm. "Process-Driven Incremental Effort Estimation." In 2019 IEEE/ACM International Conference on Software and System Processes (ICSSP). IEEE, 2019. http://dx.doi.org/10.1109/icssp.2019.00030.

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Tunalilar, Seckin, and Onur Demirors. "EFES: An Effort Estimation Methodology." In 2012 Joint Conf of 22nd Int'l Workshop on Software Measurement and the 7th Int'l Conference on Software Process and Product Measurement (IWSM-MENSURA). IEEE, 2012. http://dx.doi.org/10.1109/iwsm-mensura.2012.37.

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Sarro, Federica, Alessio Petrozziello, and Mark Harman. "Multi-objective software effort estimation." In ICSE '16: 38th International Conference on Software Engineering. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2884781.2884830.

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Tariq, Sidra, Muhammad Usman, Raymond Wong, Yan Zhuang, and Simon Fong. "On Learning Software Effort Estimation." In 2015 3rd International Symposium on Computational and Business Intelligence (ISCBI). IEEE, 2015. http://dx.doi.org/10.1109/iscbi.2015.21.

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Wienke, Sandra, Julian Miller, Martin Schulz, and Matthias S. Muller. "Development Effort Estimation in HPC." In SC16: International Conference for High Performance Computing, Networking, Storage and Analysis. IEEE, 2016. http://dx.doi.org/10.1109/sc.2016.9.

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Jørgensen, Magne. "Measurement of Software Development Effort Estimation Bias: Avoiding Biased Measures of Estimation Bias." In 11th International Conference on Embedded Systems and Applications (EMSA 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.120607.

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In this paper, we propose improvements in how estimation bias, e.g., the tendency towards under-estimating the effort, is measured. The proposed approach emphasizes the need to know what the estimates are meant to represent, i.e., the type of estimate we evaluate and the need for a match between the type of estimate given and the bias measure used. We show that even perfect estimates of the mean effort will not lead to an expectation of zero estimation bias when applying the frequently used bias measure: (actual effort – estimated effort)/actual effort. This measure will instead reward under-estimates of the mean effort. We also provide examples of bias measures that match estimates of the mean and the median effort, and argue that there are, in general, no practical bias measures for estimates of the most likely effort. The paper concludes with implications for the evaluation of bias of software development effort estimates.
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Reports on the topic "Effort estimation"

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Qamer, Faisal M., Sravan Shrestha, Kiran Shakya, Birendra Bajracharya, Shib Nandan Shah, Ram Krishna Regmi, Salik Paudel, et al. Operational in-season rice area estimation through Earth observation data in Nepal - working paper. International Centre for Integrated Mountain Development (ICIMOD), March 2023. http://dx.doi.org/10.53055/icimod.1017.

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In an effort to adopt emerging technologies in food security assessment through a codevelopment approach, the Government of Nepal’s Ministry of Agriculture and Livestock Development (MoALD) and the International Centre for Integrated Mountain Development’s (ICIMOD) SERVIR-HKH Initiative undertook a pilot study in Chitwan District in 2019 to jointly develop methods for satellite remote sensing and machine learning-based in-season crop assessment. MoALD experts and relevant stakeholders thoroughly reviewed the approach before the honourable minister approved it for formal use in the national-level assessment for 2020 and onwards. For wider adoption of the advanced data science methods established in the pilot study, we customised the technology by developing a digital suite of software, including GeoFairy (a mobile app to facilitate field data collection by field extension professionals at the district level) and RiceMapEngine (a simplified platform for machine learning-based crop classification to facilitate crop area map production by MoALD’s GIS Section). In the current federal governance structure of Nepal, high-quality crop maps and yield estimates will not only bridge information needs among the federal and subnational institutions but also provide a means for consistent cross-country crop status assessments and communication.
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Benavente, José Miguel, and Pluvia Zuñiga. The Effectiveness of Innovation Policy and the Moderating Role of Market Competition: Evidence from Latin American Firms. Inter-American Development Bank, September 2021. http://dx.doi.org/10.18235/0003655.

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This paper evaluates whether market competition matters for the effectiveness of innovation policies. Using data for manufacturing firms in Chile and Peru, we implement propensity matching techniques combined with differences-in-differences estimation to evaluate the impact of innovation subsidies on the post-treatment innovation investment effort of firms and test whether such impact differs according to the intensity of competition. We corroborate the existence of crowding-in effects in beneficiaries when compared to a control group of untreated firms. The subsidy impact is found either only significant in highly competitive sectors or larger in more competition-intensive industries -compared to low competition ones. Thus, we confirm that market competition plays a moderating role in the effectiveness of innovation policies to stimulate firm innovation investment. The results are robust to different matching and estimation methods and suggest, therefore, the importance of considering market contexts in the design of innovation policies.
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Tarko, Andrew P., Mario A. Romero, Vamsi Krishna Bandaru, and Xueqian Shi. Guidelines for Evaluating Safety Using Traffic Encounters: Proactive Crash Estimation on Roadways with Conventional and Autonomous Vehicle Scenarios. Purdue University, 2023. http://dx.doi.org/10.5703/1288284317587.

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With the expected arrival of autonomous vehicles, and the ever-increasing levels of automation in today’s human driven vehicles, road safety is changing at a rapid pace. This project aimed to address the need for an efficient and rapid method of safety evaluation and countermeasure identification via traffic encounters, specifically traffic conflicts that are considered useful surrogates of crashes. Recent research-delivered methods for estimating crash frequencies based on these events were observed in the field. In this project we developed a method for observing traffic encounters with two LiDAR-based traffic monitoring units, called TScan, which were recently developed in JTRP-funded projects SPR-3831 and SPR-4102. The TScan units were deployed in the field for several hours to collect data at selected intersections. These large data sets were used to improve object detection and tracking algorithms in order to better assist in detecting traffic encounters and conflicts. Consequently, the software of the TScan trailer-based units was improved and the results generated with the upgraded system include a list of potential encounters for further analysis. We developed an engineering application for analyzing the trajectories of vehicles involved in the pre-selected encounters to identify final traffic encounters and conflicts. Another module of the engineering application visualized the traffic encounters and conflicts to inspect the spatial patterns of these events and to estimate the number of crashes for the observation period. Furthermore, a significant modeling effort resulted in a method of producing factors that expand the conflict-based crash estimates in short observation periods to an entire year. This report provides guidelines for traffic encounters and conflicts, the user manuals for setting up and operating the TScan research unit. and manuals for the engineering applications mentioned above.
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Hall, Peter, and R. J. Carroll. Variance Function Estimation in Regression: The Effect of Estimating the Mean. Fort Belvoir, VA: Defense Technical Information Center, August 1988. http://dx.doi.org/10.21236/ada198228.

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Neuert, Mark, and Smitha Koduru. PR-244-173856-R01 In-line Inspection Crack Tool Reliability and Performance Evaluation. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), June 2019. http://dx.doi.org/10.55274/r0011599.

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The ability for operators to make operational and maintenance decisions based on in-line inspection (ILI) data depends on the performance of ILI tools with respect to sizing and detection of crack and stress corrosion cracking (SCC) features. A series of previous Pipeline Research Council International, Inc. (PRCI) projects created a database of ILI tool and pipe excavation data that can be used to evaluate the detection and sizing capabilities of ultrasonic (UT) (NDE-4-E Phase 1, PR-244-133731) and electromagnetic acoustic (EMAT) (NDE-4-E Phase 2, PR-244-153719) ILI technologies. This current project, NDE-4-7 (PR-244-173856), was carried out by C-FER Technologies (1999) Inc. (C-FER) for PRCI. It is Phase 3 of an ongoing industry-wide effort to understand and characterize ILI tool performance. In addition to adding a new data set to the database developed in Phases 1 and 2, estimates of ILI tool performance with respect to rate of detection (ROD), probability of identification (POI), false discovery rate (FDR), and sizing accuracy (SA) were calculated. Two further analyses were performed, namely an investigation of the effect of crack profile data on tool performance and burst pressure estimation, and an evaluation of the reduction in sizing uncertainty attained through multiple measurements of crack features. This document has a related webinar.
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Vail, Kylin, Bret Lizundia, David Welch, and Evan Reis. Earthquake Damage Workshop (PEER-CEA Project). Pacific Earthquake Engineering Research Center, University of California, Berkeley, CA, November 2020. http://dx.doi.org/10.55461/plbd5536.

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This report is one of a series of reports documenting the methods and findings of a multi-year, multi-disciplinary project coordinated by the Pacific Earthquake Engineering Research Center (PEER and funded by the California Earthquake Authority (CEA). The overall project is titled “Quantifying the Performance of Retrofit of Cripple Walls and Sill Anchorage in Single-Family Wood-Frame Buildings,” henceforth referred to as the “PEER–CEA Project.” The overall objective of the PEER–CEA Project is to provide scientifically based information (e.g., testing, analysis, and resulting loss models) that measure and assess the effectiveness of seismic retrofit to reduce the risk of damage and associated losses (repair costs) of wood-frame houses with cripple wall and sill anchorage deficiencies as well as retrofitted conditions that address those deficiencies. Tasks that support and inform the loss-modeling effort are: (1) collecting and summarizing existing information and results of previous research on the performance of wood-frame houses; (2) identifying construction features to characterize alternative variants of wood-frame houses; (3) characterizing earthquake hazard and ground motions at representative sites in California; (4) developing cyclic loading protocols and conducting laboratory tests of cripple wall panels, wood-frame wall subassemblies, and sill anchorages to measure and document their response (strength and stiffness) under cyclic loading; and (5) the computer modeling, simulations, and the development of loss models as informed by a workshop with claims adjustors. This report is a product of Working Group 6 (WG6): Interaction with Claims Adjustors & Catastrophe Modelers and focuses on a damage workshop effort undertaken to provide repair estimates of representative damaged single-family wood-frame case study buildings to compare the differences in costs between houses with and without retrofits to cripple walls and sill anchorage. At the request of the CEA, 11 experienced claims adjustors from insurance companies volunteered to provide the estimates. Electronic cost estimation files for each case study building were developed by the PEER–CEA Project Team using the Verisk Xactware Xactimate X1 platform and provided to the claims adjustors to complete their estimates. These adjustor estimates served as the baseline for comparison against the FEMA P-58 [FEMA 2012] methodology used on the project for loss estimation. The term “damage workshop effort” is used to emphasize that the scope of work included not just a successful workshop meeting, but the broader development of a damage description package describing case studies and associated Xactimate descriptions before the workshop meeting and revisions after it, two rounds of estimates and survey question responses by adjustors, interpretation and clarification of the estimates for consistency, and synthesizing of estimate findings and survey responses into conclusions and recommendations. Three building types were investigated, each with an unretrofitted and a retrofitted condition. These were then assessed at four levels of damage, resulting in a total of 24 potential scenarios. Because of similarities, only 17 scenarios needed unique Xactimate estimates. Each scenario was typically estimated by three to five adjustors, resulting in a final total of 74 different estimates.
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Gamboa-Estrada, Fredy, and Jose Vicente Romero. Common and idiosyncratic movements in Latin-American Exchange Rates. Banco de la República, April 2021. http://dx.doi.org/10.32468/be.1158.

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We propose a simple theoretical and empirical approach to differentiate between common and idiosyncratic exchange rate movements in 5 Latin-American economies: Brazil, Chile, Colombia, Mexico, and Peru. Our approach allows us to distinguish the effects on exchange rates of a regional exchange rate common factor and macroeconomic fundamentals differentials. The methodology and estimation strategy are suitable for both low and high frequency settings. We provide evidence that the regional common factor has a significant effect on the dynamics of the Latin-American exchange rates. In our estimations the relation between exchange rates and the common factor is contemporaneous and stable during the studied period.
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Julio-Román, Juan Manuel, and Javier Guillermo Gómez-Pineda. Output gap estimation, estimation uncertainty and its effect on policy rules. Bogotá, Colombia: Banco de la República de Colombia, June 1999. http://dx.doi.org/10.32468/be.125.

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Battistin, Erich, and Andrew Chesher. Treatment effect estimation with covariate measurement error. Institute for Fiscal Studies, September 2009. http://dx.doi.org/10.1920/wp.cem.2009.2509.

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Benavente, José Miguel, and Pluvia Zuñiga. How Does Market Competition Affect Firm Innovation Incentives in Emerging Countries? Evidence from Chile and Colombia. Inter-American Development Bank, May 2022. http://dx.doi.org/10.18235/0004235.

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The effect of market competition on firm innovation remains controversial, especially in the context of developing countries. This paper presents new empirical evidence about the causal impact of competition on firm innovation for Chilean and Colombian manufacturing firms. Using instrumental-variable estimation, our results show that market competition increases firm propensity to invest in innovation, but this relationship manifests differently in the two countries. While this relationship is linear in Chilean firms, an inversed-U shaped relation prevails in Colombian firms. In both countries, however, innovation incentives are mostly concentrated in the medium range of the firm productivity distribution. These findings are robust to including past innovation engagement, import competition, and business dynamics. In addition, first- stage estimations show that competition law interventions improved market competition in sanctioned sectors while business entry reforms significantly leveraged competition across industries. These findings stress the importance of pro-competition regulations and competition policy, not only to benefit consumers welfare but also to support firm innovation.
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