Academic literature on the topic 'Pooled OLS regression analysis'
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Journal articles on the topic "Pooled OLS regression analysis"
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Vuko, Tina, and Marko Čular. "Finding determinants of audit delay by pooled OLS regression analysis." Croatian Operational Research Review 5, no. 1 (2014): 81–91. http://dx.doi.org/10.17535/crorr.2014.0030.
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Osobajo, Oluyomi A., Afolabi Otitoju, Martha Ajibola Otitoju, and Adekunle Oke. "The Impact of Energy Consumption and Economic Growth on Carbon Dioxide Emissions." Sustainability 12, no. 19 (2020): 7965. http://dx.doi.org/10.3390/su12197965.
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This study explored the effect of energy consumption and economic growth on CO2 emissions. The relationship between energy consumption, economic growth and CO2 emissions was assessed using regression analysis (the pooled OLS regression and fixed effects methods), Granger causality and panel cointegration tests. Data from 70 countries between 1994–2013 were analysed. The result of the Granger causality tests revealed that the study variables (population, capital stock and economic growth) have a bi-directional causal relationship with CO2 emissions, while energy consumption has a uni-directional relationship. Likewise, the outcome of the cointegration tests established that a long-run relationship exists among the study variables (energy consumption and economic growth) with CO2 emissions. However, the pooled OLS and fixed methods both showed that energy consumption and economic growth have a significant positive impact on CO2 emissions. Hence, this study supports the need for a global transition to a low carbon economy primarily through climate finance, which refers to local, national, or transnational financing, that may be drawn from public, private and alternative sources of financing. This will help foster large-scale investments in clean energy, that are required to significantly reduce CO2 emissions.
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Khairnar, Rahul Ramesh, Lyudmila DeMora, Howard M. Sandler, et al. "A methodological comparison of mapping algorithms to obtain health utilities derived using cross-sectional and longitudinal data: Secondary analysis of NRG/RTOG 0415." Journal of Clinical Oncology 38, no. 6_suppl (2020): 55. http://dx.doi.org/10.1200/jco.2020.38.6_suppl.55.
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55 Background: To compare the predictive ability of health utility mapping algorithms derived using cross-sectional and longitudinal data specific to the Expanded Prostate Cancer Index Composite (EPIC). Methods: This mapping study utilized data from an international, multicenter, randomized controlled trial of patients with low-risk prostate cancer conducted by NRG Oncology (NCT00331773). Health-related quality-of-life (HRQoL) data were collected using EPIC, and health utilities were obtained using EuroQOL-5D (EQ5D) at baseline and 6, 12 and 24 months post-intervention. Data were split into an estimation sample (70%) and a validation sample (30%). Ordinary Least Squares (OLS) regression models were estimated using baseline cross-sectional data as well as pooled data from all assessment periods. Random effects (RE) specifications that explicitly model the longitudinal nature of the data were also estimated. Candidate models were selected based on root mean square error (RMSE). Results: A total of 196 (147) patients in the estimation sample had complete EQ5D and EPIC domain (subdomain) data at all time points. OLS models using combined data outperformed the counter-part RE models as well as OLS models using baseline data in the five-fold cross-validation. Addition of covariates to the models resulted in improved predictive ability. In the external validation, when only EPIC domain/ subdomain data are available, the OLS model using combined data predicted EQ5D utilities better than the counterpart RE model and OLS model using baseline data (RMSE=0.121108 & 0.111345). OLS model using baseline data outperformed other model types for algorithms with EPIC domains and demographics (RMSE=0.121757), while RE models outperformed the other two model types for algorithms with EPIC subdomains and demographic data, (0.112782) and for algorithms with EPIC domains/ subdomains, demographics, and clinical covariates (RMSE=0.123589 & 0.163093). Conclusions: While algorithms using pooled data outperformed other model types in internal validation, RE models showed better predictive ability in external validation for algorithms with covariates. Clinical trial information: NCT00331773.
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Pertiwi, Dewi, Sautma Ronni Basana, and Marcella Grace Yasinta. "Decisions for Stock Investment among University Students." SHS Web of Conferences 76 (2020): 01005. http://dx.doi.org/10.1051/shsconf/20207601005.
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This study aimed to investigate the effect of economic value added and profitability on created shareholders value Fernandez model and market value added model in manufacturing companies listed on the Indonesia Stock Exchange period year 2013 to 2018. The samples are fifty Manufacturing companies enterprises listed on the IDX period year 2013 to 2018. Methods of data analysis was performed using panel data regression with pooled OLS model. The results of this analysis showed profitability have a significant effect on created shareholders value Fernandez model and market value added model in manufacturing companies listed on the IDX period 2013 to 2018.
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Ronni Basana, Sautma, Ricky Julio, and Christina Soehono. "Economic Value Added and Profitability on Created Shareholders Value in Manufacturing Sectors." SHS Web of Conferences 76 (2020): 01051. http://dx.doi.org/10.1051/shsconf/20207601051.
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This study aimed to investigate the effect of economic value added and profitability on created shareholders value Fernandez model and market value added model in manufacturing companies listed on the Indonesia Stock Exchange period year 2013 to 2018. The samples are fifty Manufacturing companies enterprises listed on the IDX period year 2013 to 2018. Methods of data analysis were performed using panel data regression with pooled OLS model. The results of this analysis showed profitability has a significant effect on created shareholders value Fernandez model and market value-added model in manufacturing companies listed on the IDX period 2013 to 2018.
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Ekumankama, Ogbuagu Onu. "The impact of financial structure on profitability of firms: a cross-sectional industry analysis of Nigerian quoted firms." Corporate Ownership and Control 9, no. 1 (2011): 415–27. http://dx.doi.org/10.22495/cocv9i1c4art1.
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This study empirically examines the impact of financial structure decision on the profitability of Nigerian quoted firms. Cross-sectional time series data of 72 Nigerian quoted firms were collated and analysed. Two hypotheses were proposed for the study, while the ordinary least square (OLS), fixed-effects (FE) and the gerneralised least square (GLS) regression were used on pooled and panel data to estimate the relationship between financial leverage and the different measures of profitability in Nigeria quoted firms. In determining the extent of the influence of leverage on the dependent variables, most of the industrial groups showed evidence of sizable positive influence of leverage on profitability and earnings yield. This was significant and robust with all the measures of leverage.
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PhD Candidate, Pius Ayekeh Tachang,, Dobdinga Cletus Fonchamnyo, Associate Professor, and Edokat Edward Oki Tafah, Professor. "Government Compliance with Public Procurement Policy and Performance of Construction Enterprises in Cameroon: A Micro Panel Modelling Approach." Urban Studies and Public Administration 3, no. 3 (2020): p56. http://dx.doi.org/10.22158/uspa.v3n3p56.
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This paper aimed to investigate the implication of government compliance with public procurement policy for the performance of enterprises operating in the construction sector in Cameroon. To achieve its objectives, the paper made use of a micro panel collected with the help of structured questionnaires. The Pooled OLS and the random versus fixed effects models were used for the analyses. Findings revealed that the contract winning rate of construction enterprises increases monotonically over the period under study. While the performance of construction enterprises stagnated over the period 2013-2016, it only witnessed a drop in 2017 and 2018. The pooled OLS and the Random effect regression results revealed that government compliance with payment duration of construction enterprises’ bills positively and significantly affects the performance of construction enterprises in Cameroon. Based on these findings, the study recommends that the government could consider redressing the policies on the regular and prompt payment of enterprises’ bills within the timeline specified in the contracts to enable them meet up with their financial requirements, thereby contributing to their overall performance.
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Mintargo, Mintargo, Barika Barika, and Edy Rahmantyo. "Identifikasi Faktor Yang Mempengaruhi Kemiskinan Di Provinsi Bengkulu." PARETO : Jurnal Ekonomi dan Kebijakan Publik 1, no. 1 (2018): 1. http://dx.doi.org/10.32663/pareto.v1i1.350.
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The purpose of this research is to analyze the effect of unemployment with skill (X1), amount of population (X2), and minimum of wage region (X3) against proverty (Y) in Bengkulu Province. The method of analysis by an Ordinary Least Square (OLS). The data that used in this research is pooled data from BPS (Badan Pusat Statistik) of Bengkulu Province and Bapenas (period of data is 2009 � 2013 ). From the output of regression with Eviews (econometric views) aplication indicated that amount of population (X2) having significant effect against proverty (Y). unemployment with skill (X1) is not significant with proverty (Y), and the last minimum of wage region (X3) isnot significant too with proverty (Y). The coefisien regression of variables are coefisien regress of X1 aqual -0.0001200, coefisien regress of X2 equal 0.000200, and coefisien regress of X3 equal -5.18E-06.
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Bello, Mohammed Sabo, Ridzwana Mohd Said, Jalila Johari, and Fakarudin Kamarudin. "Moderating Role of Corruption Control on firm level determinants of Corporate Sustainability Disclosure Compliance in Nigeria." Asia Proceedings of Social Sciences 6, no. 2 (2020): 178–82. http://dx.doi.org/10.31580/apss.v6i2.1335.
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This paper explores the moderating effect of corruption control in strengthening the influence of firm attributes on corporate sustainability disclosure compliance in Nigeria. The study focuses on the existing discussion on mandatory disclosure compliance with corporate governance code. The extent of disclosure compliance is measured using a total unweighted disclosure index, developed from a panel dataset of 118 companies listed in the Nigerian capital market. The companies were selected using a proportionate stratified sampling technique. The dataset for the period of 2011 to 2017 were first analyzed by static panel regression analysis using pooled Ordinary Least Square (OLS), Fixed Effect (FE), and Random Effect (RE) models. The regression models were subjected to further robustness checks under dynamic GMM panel regression analysis, to test for possible endogeneity. The findings revealed significant moderating effect of corruption control, evidenced from the interaction of corruption control with selected firm attributes, namely; industry type, leverage and taxation. The research contributes to the existing literature, as it establishes the importance of control of corruption as an additional factor of corporate sustainability disclosure compliance within the context of Nigeria.
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Dao, Le Kieu Oanh, Thuy Tu Pham, and Van Chien Nguyen. "Factors Affecting the Competitive Capacity of Commercial Banks: A Critical Analysis in an Emerging Economy." International Journal of Financial Research 11, no. 4 (2020): 241. http://dx.doi.org/10.5430/ijfr.v11n4p241.
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This research was conducted to investigate the factors influencing the commercial bank’s competitive capacity in an emerging country. Data were collected from the domestic-owned commercial banks and foreign-owned commercial banks listed on Vietnam’s Stock Exchange over the period of nine years from 2010 to 2018. Three statistic approaches were employed to address econometrics issues and to improve the accuracy of the regression coefficients: Pooled Ordinary Least Square (Pooled OLS), Random Effects Model (REM), and Fixed Effects Model (FEM). To correct the diagnostics and endogeneity in the model, the study uses Generalized Least Square (GLS) and Generalized Method of Moments (GMM). In order to account for the degree of competitive capacity we use Lerner index. Results demonstrate that the impact of bank-specific characteristics on market power in banks is statistically significant, and there are substantial distinguishments of economic consideration among these factors. In addition, a bank with a higher level of competitive capacity in the previous year will outstandingly generate competitive capacity in the current year. Another possibility, a greater level foreign investment into the banks in the host country could further encourage competitive capacity in the banking system. Finally, economic growth rate has no impact on competitive capacity at a significant level of 5% while a positive effect from inflation on bank’s market power could be found.
Dissertations / Theses on the topic "Pooled OLS regression analysis"
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Sjölin, Carin. "The impact of governance on inequality : An empirical study." Thesis, Södertörns högskola, Nationalekonomi, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:sh:diva-31304.
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This paper examines the effect of governance on inequality, specifically if improvements in the World Bank’s Worldwide Governance Indicators affect inequality as measured by two Gini coefficients: Market Gini, before taxes and redistribution, and Net Gini, after taxes and redistribution. The data for the Gini measurements was taken from the Standardized World Income Inequality Database (SWIID) and the data for the Worldwide Governance Indicators was taken from the World Bank. Data for fifteen (15) years, from the start of the Worldwide Governance Indicators until 2013, was combined with data from SWIID for the same years. In all, data from one hundred fifty-six (156) countries with a full set of six (6) indicators for the years that had at least one corresponding Gini measurements were used in this study: in total one thousand seven hundred and forty-seven (1747) observations. In a pooled OLS regression, controlling for growth with the variable GDP per Capita expressed as a per cent (%) change on an annual basis, the individual indicators gave the following results, where a positive sign indicates increased inequality and vice versa: Control of Corruption and Regulatory Quality showed a positive sign for both Gini measurements. Rule of Law, Government Effectiveness, Political Stability and the Absence of Violence/Terrorism, gave a negative sign for both Gini measurements. Voice and Accountability showed a positive sign for Market Gini and a negative sign for Net Gini. The fact that an improvement in Control of Corruption increased inequality both before and after taxes and redistribution was unexpected and should be further researched.
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Sandrock, Brian Arthur. "Spatial Analysis of Foreclosures in Hillsborough County." Scholar Commons, 2014. https://scholarcommons.usf.edu/etd/5438.
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This study examines the spatial impact various socio-demographic and housing factors might have in the foreclosure lis pendens rate within various Hillsborough County, Florida tracts as well as comparing those results with past research. Hopefully the techniques used in this study can be implemented elsewhere in order to better study the foreclosure crisis. The methods used within this research were chosen carefully in order to best understand what is being observed. One method is OLS regression which helps see the impact of each variable and if that impact has a negative or positive effect on the rate of foreclosure. Bivariate Maps were created to spatially examine each variable when compared to the foreclosure rate as well as Effect plots from regression in order to see how the true relationship of a variable affects the foreclosure rate.
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Li, Yang. "An Empirical Analysis of Family Cost of Children : A Comparison of Ordinary Least Square Regression and Quantile Regression." Thesis, Uppsala University, Department of Statistics, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-126660.
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<p>Quantile regression have its advantage properties comparing to the OLS model regression which are full measurement of the effects of a covariate on response, robustness and Equivariance property. In this paper, I use a survey data in Belgium and apply a linear model to see the advantage properites of quantile regression. And I use a quantile regression model with the raw data to analyze the different cost of family on different numbers of children and apply a Wald test. The result shows that for most of the family types and living standard, from the lower quantile to the upper quantile the family cost on children increases along with the increasing number of children and the cost of each child is the same. And we found a common behavior that the cost of the second child is significantly more than the cost of the first child for a nonworking type of family and all living standard families, at the upper quantile (from 0.75 quantile to 0.9 quantile) of the conditional distribution.</p>
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Nevarez, Martinez Deyanira. "Identifying Housing Patterns in Pima County, Arizona Using the DEYA Affordability Index and Geospatial Analysis." The University of Arizona, 2015. http://hdl.handle.net/10150/576108.
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When the Fair Housing Act of 1968 was passed 47 years ago, the United States was in the midst of the civil rights movement and fair housing was identified as a pillar of equality. While, progress has been made, there is much work that needs to be done in order to achieve integration. As a country, the United States is a highly segregated country. It is important to understand the factors that contribute to this and it is important to understand the relationships that exists between them in order to attempt to solve the problem. While the legal barriers to integration have been lifted choices continue to be limited to families of color that lack the resources to live in desirable neighborhoods. The ultimate goal of this study is to examine the relationship between the impact of individual indicators and housing patterns in the greater Tucson/Pima county region. An affordability index, the DEYA index, was created to determine where affordability is at its highest. The index includes different weights for foreclosure, Pima County spending on affordable housing, the existence of Pima County general obligations bond affordable housing projects, land value and inclusion in the community land trust. Once this was determined a regression analysis was used to determine the relationship between affordability and individual factors that may be affecting integration. The indicators used were broken down into 3 categories: the categories were education, housing and neighborhoods and employment and economic health.
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Shalhoob, Hebah Shafeq. "A comparative analysis of risk-return characteristics between Sukuk (Islamic bonds) and conventional bonds." Thesis, Robert Gordon University, 2016. http://hdl.handle.net/10059/1574.
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Sukuk are an important mode of financing in the Islamic financial system. As usury (interest) is prohibited in Islam, conventional bonds are not suitable for investors in Islamic countries. Since their launch in the 1980s, Sukuk have gained recognition and popularity as a substitute for conventional bonds. However, their unique features mean that Sukuk are not always clearly understood. The aim of this study is to analyse the differences and similarities between Sukuk and conventional bonds in terms of their risk and return characteristics.
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Alves, Paula Cristina Sampaio. "Desempenho dos Bancos Alimentares Contra a Fome de Portugal Continental : caracterização e análise de eficiência." Master's thesis, Instituto Superior de Economia e Gestão, 2016. http://hdl.handle.net/10400.5/12857.
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Mestrado em Decisão Económica e Empresarial<br>Este relatório está assim dividido em três partes. Na primeira parte podemos encontrar uma breve apresentação relativa aos Bancos Alimentares, incidindo na sua história e funcionamento. Na segunda parte foi realizada uma Análise de Clusters, aos dezoito BAs de Portugal Continental, com o objetivo de classificar os BAs em grupos. Esta foi dividida em duas fases: numa primeira fase os BAs foram agrupados com base nas características internas de cada um, de modo a classificá-los de acordo com a sua dimensão - pequenos, médios ou grandes. Numa segunda fase, a classificação dos BAs não só teve em conta as características internas mas também as características socioeconómicas da região onde se encontra inserido cada BA. O objetivo desta análise foi relacionar a dimensão de um determinado BA com as necessidades da população e com a potencialidade de ofertas a seu favor. Na terceira parte foi realizada uma análise de eficiência ao comportamento de cada Banco Alimentar, usando dados em painel, recorrendo a modelos econométricos, estimados com o Pooled OLS com efeitos fixos no tempo. O objetivo deste estudo foi analisar se cada BA está a operar de forma eficiente, para o nível dos seus recursos. Os modelos empíricos foram aplicados às toneladas recolhidas anualmente nas campanhas de angariação junto do público, ao número de voluntários que participam anualmente nas campanhas, ao número de pessoas assistidas anualmente e ao total de entradas anuais em toneladas.<br>This report is divided into three sections: On the first section we can find a small introduction about the Food Bank's, focusing on their story and operation. On the second section we proceeded to a Cluster Analysis to the eighteen Food Bank's from Portugal Continental, with the purpose of classifying them by groups. This was performed in two phases: on the first phase the FB were grouped considering their intern characteristics, with the goal of classifying them by dimension - small, medium or large. On the second phase, the classification take into account not only the internal characteristics but also the socioeconomics characteristics of the region. The goal of this analysis was to relate the dimension of an determinate FB with the population necessities and the potential of offers in their favor. On the third section an analysis to the efficiency has been performed to each FB, utilizing panel data and applying econometrics models, with the Pooled OLS with fixed effects on time. The goal of this study was to analyze if each FB is performing in an efficient manner, considering their level of resources. The empirical models were applied to the Ton's gathered annually in their campaigns close to the public, the number of volunteers that participated annually in this campaigns, the number of persons assisted annually by each FB and the total of annual entries, in Ton's.<br>N/A
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Annink, Marit, and Rebecca Larsson. "Introduction of the Academic Factor Quality Minus Junk to a Commercial Factor Model and its Effect on the Explanatory Power. An OLS Regression on Stock Returns." Thesis, KTH, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254296.
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The ability to predict stock returns is an ability many wish to possess, and in an accurate way as possible. For many years there has been an interest in the field of factor models explaining the returns, with the aim to increase the explanatory power. This is however a complex business since the factors and their improvement of explanatory power need to be significant. Now and then, researchers come up with new significant factors that have a positive impact on models. AQR Capital Management is no exception to this, since they in 2013 presented the factor Quality Minus Junk, earning significant risk-adjusted returns. This bachelor thesis work within mathematical statistics and industrial engineering and management, aims to investigate whether or not the commercial multi-factor model used at the public pension fund Fjärde AP-fonden will be improved by adding the factor Quality Minus Junk, in the sense of explanatory power. The method used is mainly based on multiple linear regression and three three-year time periods are studied ranging from 2010 to 2018. The results from this thesis work show that the QMJ factor provides significant increases in explanatory power for one of three time periods, the most recent period 2016$-$2018. However, since the results are inconclusive further studies are needed in order to better understand how to interpret the results and whether or not to include the QMJ factor in the model.<br>Förmågan att förutsäga aktiers avkastning önskar många besitta, och på ett så precist sätt som möjligt. Under många år har forskning pågått inom området för faktormodeller som förklarar avkastningar, med målet att öka modellernas förklaringsgrad. Detta är dock en komplex verksamhet eftersom faktorerna och deras förbättring av förklaringsgraden måste vara signifikanta för modellen. Då och då kommer forskare fram med nya sådana faktorer som har positiv påverkan på modeller. AQR Capital Management är inget undantag eftersom de 2013 presenterade sin faktor Quality Minus Junk som visar signifikanta riskjusterade avkastningar. Detta kandidatexamensarbete inom matematisk statistik och industriell ekonomi, ämnar att utreda huruvida den kommersiella faktormodellen som används på Fjärde AP-fonden förbättras genom tillägget av faktorn Quality Minus Junk, i förklaringsgradsmening. Metoden som används är till största delen baserad på multipel linjär regression och tre treårsperioder studeras i tidsintervallet 2010 till 2018. Resultaten från detta projekt visar på att faktorn Quality Minus Junk bidrar med signifikanta ökningar av förklaringsgraden för en av tre perioder, den senaste perioden 2016-2018. Eftersom resultaten är inkonklusiva krävs vidare studier för att bättre förstå och konkludera vad dessa resultat faktiskt innebär samt för att inkludera QMJ-faktorn i modellen eller ej.
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Cau, Nicklasson Ronnie, and Simon Hansson. "Investment Companies’ Discount Fluctuation on the Swedish Market : A statistical analysis regarding different micro- and macroeconomic factors influence on Swedish closed-end funds’ discount." Thesis, Internationella Handelshögskolan, Högskolan i Jönköping, IHH, Economics, Finance and Statistics, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-21250.
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Closed-end funds’ (CEF) discount and discount fluctuations have been puzzling researchers for decades. Up to date, there are no multidimensional or cross-sectional variables that have been proved to influence CEFs simultaneously. Fact is that, earlier research and theories on the subject are contradictious and several suggestions on the origin of the CEF’s discount and its fluctuations have been proposed. To mention a few, investor sentiments, taxation issues, dividend policies, agency costs and agency problems are considered to influence these discounts. The purpose of this report is to examine the relationship between micro- and macroeconomic variables fluctuations, and how these can explain the discount fluctuation of the Swedish CEFs. This report focuses upon the CEFs traded at NASDAQ OMX Stockholm, which have been selected through a comprehensive multistage selection process. 10 CEFs were selected. Monthly data for calculating micro- and macroeconomic variables was collected for the period March 2003 – February 2013, which resulted in approximately 1 200 observations. OLS regression analysis, Fixed- and Random Effect Models and Hausman tests were conducted. The findings conclude that some of this report’s chosen micro- and macro variables influence on the Swedish CEFs’ discount fluctuation, although these findings are conditioned. The CEFs’ individual characteristics or traits result in a significant impact on the fluctuation of CEFs’ discount. Hence, only by controlling these characteristics, multidimensional or cross-sectional micro- and macroeconomic variables can be proved to affect the CEFs’ discount fluctuation.
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Diallo, Ibrahima Amadou. "Exchange rates policy and productivity." Thesis, Clermont-Ferrand 1, 2013. http://www.theses.fr/2013CLF10405/document.
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Cette thèse étudie comment le taux de change effectif réel (TCER) et ses mesures associées (volatilité du TCER et désalignement du TCER) affectent la croissance de la productivité totale des facteurs (CPTF). Elle analyse également les canaux par lesquels le TCER et ses mesures associées agissent sur la productivité totale des facteurs (PTF). La première partie étudie comment le TCER lui-Même, d'une part, et la volatilité du TCER, d'autre part, influencent la productivité. Une analyse du lien entre le niveau du TCER et la PTF dans le chapitre 1 indique qu'une appréciation de taux de change cause une augmentation de la PTF. Mais cet impact est également non- inéaire: en-Dessous du seuil, le TCER influence négativement la productivité tandis qu'au-Dessus du seuil il agit positivement. Les résultats du chapitre 2 illustrent que la volatilité du TCER affecte négativement la CPTF. Nous avons également constaté que la volatilité du TCER agit sur PTF selon le niveau du développement financier. Pour les pays modérément financièrement développés, la volatilité du TCER réagit négativement sur la productivité et n'a aucun effet sur la productivité pour les niveaux très bas et très élevés du développement financier. La deuxième partie examine les canaux par lesquels le TCER et ses mesures associées influencent la productivité. Les résultats du chapitre 3 illustrent que la volatilité du TCER a un impact négatif élevé sur l'investissement. Ces résultats sont robustes dans les pays à faible revenu et les pays à revenu moyens, et en employant une mesure alternative de volatilité du TCER. Le chapitre 4 montre que le désalignement du taux de change réel et la volatilité du taux de change réel affectent négativement les exportations. Il démontre également que la volatilité du taux de change réel est plus nocive aux exportations que le désalignement. Ces résultats sont corroborés par des résultats sur des sous-Échantillons de pays à bas revenu et à revenu moyen<br>This dissertation investigates how the real effective exchange rate (REER) and its associated asurements (REER volatility and REER misalignment) affect total factor productivity growth (TFPG). It also analyzes the channels through which the REER and its associated measurements act on total factor productivity (TFP). The first part studies how the REER itself, on the one hand, and the REER volatility, on the other hand, influence productivity. An analysis of the link between the level of REER and TFP in chapter 1 reveals that an exchange rate appreciation causes an increase of TFP. But this impact is also nonlinear: below the threshold, real exchange rate influences negatively productivity while above the threshold it acts positively. The results of chapter 2 illustrate that REER volatility affects negatively TFPG. We also found that REER volatility acts on TFP according to the level of financial development. For moderately financially developed countries, REER volatility reacts negatively on productivity and has no effect on productivity for very low and very high levels of financial development. The second part examines the channels through which the REER and its associated measurements influence productivity. The results of chapter 3 illustrate that the exchange rate volatility has a strong negative impact on investment. This outcome is robust in low income and middle income countries, and by using an alternative measurement of exchange rate volatility. Chapter 4 show that both real exchange rate misalignment and real exchange rate volatility affect negatively exports. It also demonstrates that real exchange rate volatility is more harmful to exports than misalignment. These outcomes are corroborated by estimations on subsamples of Low- ncome and Middle-Income countries
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Verhaeghe, Joseph Rene Stephen. "Optimal mineral taxation in Manitoba: an exploration of Monte Carlo simulation analysis." 2014. http://hdl.handle.net/1993/24021.
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Manitoba has abundant mineral deposits and the mining sector is significant for its economy, especially outside the capital region. This paper examines the Manitoba mining taxation regime, using two approaches. First, a conventional regression analysis is used to estimate the impact of the 2009 mining tax cut on Northern employment and capital investment. This approach potentially offers a general indication of how tax policy influences economic advantages. Unfortunately, data limitations impede the analysis. Second, a mining firm is modelled to directly examine the effect of various tax structures on profitability. A hypothetical underground mine is modelled using discounted cash flow and net present value methods. Monte Carlo simulation will add a further dimensionality to the analysis, evaluating the effects of taxation when making probability assumptions on metal grade, prices, and operating costs. The new mine tax holiday stands out as a significant tax benefit for the miner.
Books on the topic "Pooled OLS regression analysis"
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Baddeley, Michelle. Running regressions: A practical introduction to OLS in economics, finance and development studies. Cambridge University Press, 2009.
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Belzil, Christian. A structural analysis of the correlated random coefficient wage regression model with an application to the ols-iv puzzle. IZA, 2005.
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Halperin, Sandra, and Oliver Heath. 17. A Guide to Multivariate Analysis. Oxford University Press, 2017. http://dx.doi.org/10.1093/hepl/9780198702740.003.0017.
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This chapter extends the principles of bivariate analysis to multivariate analysis, which takes into account more than one independent variable and the dependent variable. With multivariate analysis, it is possible to investigate the impact of multiple factors on a dependent variable of interest, and to compare the explanatory power of rival hypotheses. Multivariate analysis can also be used to develop and test multi-causal explanations of political phenomena. After providing an overview of the principles of multivariate analysis, and the different types of analytical question to which they can be applied, the chapter shows how multivariate analysis is carried out for statistical control purposes. More specifically, it explains the use of OLS regression and logistic regression, the latter of which builds on cross-tabulation, to carry out multivariate analysis. It also discusses the use of multivariate analysis to debunk spurious relationships and to illustrate indirect causality.
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Li, Quan. Using R for Data Analysis in Social Sciences. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190656218.001.0001.
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This book seeks to teach undergraduate and graduate students in social sciences how to use R to manage, visualize, and analyze data in order to answer substantive questions and replicate published findings. This book distinguishes itself from other introductory R or statistics books in three ways. First, targeting an audience rarely exposed to statistical programming, it adopts a minimalist approach and covers only the most important functions and skills in R that one will need for conducting reproducible research projects. Second, it emphasizes meeting the practical needs of students using R in research projects. Specifically, it teaches students how to import, inspect, and manage data; understand the logic of statistical inference; visualize data and findings via histograms, boxplots, scatterplots, and diagnostic plots; and analyze data using one-sample t-test, difference-of-means test, covariance, correlation, ordinary least squares (OLS) regression, and model assumption diagnostics. Third, it teaches students how to replicate the findings in published journal articles and diagnose model assumption violations. The principle behind this book is to teach students to learn as little R as possible but to do as much reproducible, substance-driven data analysis at the beginner or intermediate level as possible. The minimalist approach dramatically reduces the learning cost but still proves adequate information for meeting the practical research needs of senior undergraduate and beginning graduate students. Having completed this book, students can use R and statistical analysis to answer questions regarding some substantively interesting continuous outcome variable in a cross-sectional design.
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Halperin, Sandra, and Oliver Heath. 16. Patterns of Association. Oxford University Press, 2017. http://dx.doi.org/10.1093/hepl/9780198702740.003.0016.
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This chapter discusses the principles of bivariate analysis as a tool for helping researchers get to know their data and identify patterns of association between two variables. Bivariate analysis offers a way of establishing whether or not there is a relationship between two variables, a dependent variable and an independent variable. With bivariate analysis, theoretical expectations can be compared against evidence from the real world to see if the theory is supported by what is observed. The chapter examines the pattern of association between dependent and independent variables, with particular emphasis on hypothesis testing and significance tests. It discusses ordinary least squares (OLS) regression and cross-tabulation, two of the most widely used statistical analysis techniques in political research. Finally, it explains how to state the null hypothesis, calculate the chi square, and establishing the correlation between the dependent and independent variables.
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Franzese, Robert J., and Jude C. Hays. Empirical Models of Spatial Inter‐Dependence. Edited by Janet M. Box-Steffensmeier, Henry E. Brady, and David Collier. Oxford University Press, 2009. http://dx.doi.org/10.1093/oxfordhb/9780199286546.003.0025.
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This article discusses the role of ‘spatial interdependence’ between units of analysis by using a symmetric weighting matrix for the units of observation whose elements reflect the relative connectivity between unit i and unit j. It starts by addressing spatial interdependence in political science. There are two workhorse regression models in empirical spatial analysis: spatial lag and spatial error models. The article then addresses OLS estimation and specification testing under the null hypothesis of no spatial dependence. It turns to the topic of assessing spatial lag models, and a discussion of spatial error models. Moreover, it reports the calculation of spatial multipliers. Furthermore, it presents several newer applications of spatial techniques in empirical political science research: SAR models with multiple lags, SAR models for binary dependent variables, and spatio-temporal autoregressive (STAR) models for panel data.
Book chapters on the topic "Pooled OLS regression analysis"
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Wang, Yousong, Yakun Li, and Hongyang Li. "Evaluating the Competitiveness of the Chinese Listed Construction Enterprises Based on OLS Regression Analysis Model." In Proceedings of the 24th International Symposium on Advancement of Construction Management and Real Estate. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8892-1_54.
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Wiersema, Margarethe F., and Harry P. Bowen. "Empirical Methods in Strategy Research: Regression Analysis and the Use of Cross-Section Versus Pooled Time-Series, Cross-Section Data." In Statistical Models for Strategic Management. Springer US, 1997. http://dx.doi.org/10.1007/978-1-4757-2614-5_9.
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Mercer, Hannah C., and Patrick S. Edwards. "An Analysis of Gender Inequality in Professional Tennis." In Applied Econometric Analysis. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-1093-3.ch006.
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This paper examines the gender wage gap in professional sports using a pooled cross-section of professional tennis players across the years 2011-2017. The dependent variable is the prize money earned by the top fifty male and top fifty female ranked tennis players throughout the world. This prize money is measured in 2017 real dollar value. The independent variables include: number of tournaments played, age, rank differentiation, gender, country and WTA/ATP score. Gender inequality is measured by determining the wage gap shown through the mean prize money earned by the professional tennis players from 2011-2017. While prize money for men and women has recently become equal in the Grand Slam tournaments, there is evidence to show that women's prize money is considerably lower in the less-publicized tournaments. Results of the ordinary least squares (OLS) regressions suggest that there is evidence for a gender-related pay disparity in professional tennis due to a number of statistically significant variables including WTA/ATP score (+), age (+), country (+) and the gender (-) and year (+) dummies.
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Şen, Süleyman, and Süreyya Kovacı. "The Impact of the COVID-19 Pandemic on the Tourism Economy." In Handbook of Research on the Impacts and Implications of COVID-19 on the Tourism Industry. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-8231-2.ch008.
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This chapter seeks to examine the impact of COVID-19 on the Turkish tourism economy. Towards this end, first of all, tourist arrivals and tourism income of Turkey were compared between pre-pandemic and within the pandemic period. Afterwards, to investigate whether COVID-19 leads to a decrease in stock prices of tourism firms, the return data of 11 firms listed in the Borsa Istanbul restaurants and hotels subsector for 10 months were examined by panel data methods. According to comparison of tourist arrivals and tourism income to the previous year, there was a vital decrease as 69% and 65% respectively in 2020. Moreover, coverage rate of foreign trade deficit of tourism income in Turkey decreased almost 80% in 2020. Overall, results of pooled OLS regression analysis revealed that COVID-19 cases and COVID-19-related deaths were decreasing monthly stock price returns. Based on these findings, it is recommended to policymakers to find better policies for a better tourism economy.
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Marcinkiewicz, Kamil, and Kai-Uwe Schnapp. "Regression Analysis." In Research Methods in the Social Sciences: An A-Z of key concepts. Oxford University Press, 2021. http://dx.doi.org/10.1093/hepl/9780198850298.003.0054.
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This chapter evaluates regression analysis, which uses quantitative and sometimes also qualitative independent variables to explain or predict change in a quantitative dependent variable. To attain this goal, it relies on the principles of covariance and correlation. Its most basic form is linear regression, also known as ordinary least squares (OLS) regression. In addition, there are many other varieties of regression methods for different research questions and data characteristics, such as time-oriented questions or data with a limited range of values. Researchers use regression analysis especially to analyse complex patterns of correlation in situations with more than one explanatory variable. Often such patterns are interpreted in the context of causal theories. The concept of regression goes back to Francis Galton’s study on human height.
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Li, Quan. "Regression Diagnostics and Sensitivity Analysis." In Using R for Data Analysis in Social Sciences. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190656218.003.0006.
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This chapter shows why the Gauss-Markov assumptions are important in ordinary least squares (OLS) regression, how to diagnose assumption violations in OLS regression, and how to conduct sensitivity analysis and correct for some assumption violations. The issues covered include linearity and model specification, perfect and high multicollinearity, constant error variance, independence of error term observations, outlier and influential observations, and normality test. A mastery of materials in this chapter is necessary for systematic data analysis of a continuous outcome variable in a cross-sectional design
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Li, Quan. "Regression Analysis." In Using R for Data Analysis in Social Sciences. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190656218.003.0005.
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This chapter teaches how to use R to conduct regression analysis to answer the question: Does trade promote economic growth? It demonstrates how to specify a statistical model from a theoretical argument, prepare data, estimate and interpret the statistical model, and use the estimated results to make inferences and answer the question of interest. More specifically, it discusses the logic of regression analysis, the relationship between population and sample regression models, how to estimate a regression model in theory and practice, the estimation of sample regression model using OLS (ordinary least squares), the interpretation of estimation results, the statistical inference in regression analysis using hypothesis testing and confidence interval, the types of sum of squares and overall model fit, and how to report the model results. The validity of regression analysis is contingent upon the assumptions of the Gauss-Markov theorem being met.
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"(A)Spatial Regression." In Spatial Analysis Techniques Using MyGeoffice®. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-3270-5.ch004.
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The global spatial lag model for stationary processes (Y=?WY+Xß+e, where W represents the neighborhood matrix while ? is the spatial autoregressive coefficient) and the spatial regimes (individual regression for each sub-region), developed within SpaceStat®, are two possibilities. An extra option is the local continuous variation framework computed by the Geographically Weighted Regression (GWR). The idea here is to adjust a regression model (the ßs of the regression model are not the same everywhere) by weighting the neighborhood observations. In this way, the estimation computation will reflect automatic adjustments, according to the distance and values of the available samples. Section “Geographically Weighted Regression” introduces this matter while the next two sections review the conventional bivariate and multivariate OLS regression, respectively. As an extension of the OLS family, non-linear relationships, regression with dummy variables, path analysis and logistic regression are briefly explained under the following section three although myGeoffice© does not cover these practices. Spearman Rank Correlation for ordinal data is presented in the last section.
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"APPENDIX 4.A. Parallel Between OLS Regression and Linear Projection." In Time Series Analysis. Princeton University Press, 1994. http://dx.doi.org/10.1515/9780691218632-034.
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Imran, Mohammed, Mohammad Ashraful Ferdous Chowdhury, and Mosharrof Hosen. "Financial Development, Technological Development, and Poverty." In Advances in Human Resources Management and Organizational Development. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-4056-4.ch008.
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This study aims to explore the impact of financial development, technological development on poverty for Asian and Non-Asian countries. In order to test the proposed model, five years' data from 80 countries over the year 2008 to 2014 have been collected. This study uses the Pooled OLS regression and found that poverty has a significant relationship with inflation and unemployment. However, technological advancement has found negatively and statistically significant with poverty. Furthermore, this study found that the Asian region has poverty reduction effect in comparing other regions when technological advancement speeds up.
Conference papers on the topic "Pooled OLS regression analysis"
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XUE, Yuhao, Shixuan WANG, Bijun ZHU, and Bin LIU. "The Impact of Firm Informationization on Audit Fee: Based on the Analysis of OLS Regression Model." In 2020 2nd International Conference on Economic Management and Model Engineering (ICEMME). IEEE, 2020. http://dx.doi.org/10.1109/icemme51517.2020.00009.
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Yining, Deng, Niu Mandi, and Zhang Qingyu. "The Empirical Analysis of the Influence of Urban Innovation Ability on the Economic growth Based on the Least Square Regression Model (OLS)." In 2021 International Conference on Financial Management and Economic Transition (FMET 2021). Atlantis Press, 2021. http://dx.doi.org/10.2991/aebmr.k.210917.083.
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Philip, G., C. Le Bailly De Tilleghem, AS Swern, T. Loeys, and SS Smugar. "Baseline Predictors of Placebo Response in Exercise-Induced Bronchoconstriction (EIB): Pooled Regression Analysis of 3 Studies of Montelukast in EIB." In American Thoracic Society 2009 International Conference, May 15-20, 2009 • San Diego, California. American Thoracic Society, 2009. http://dx.doi.org/10.1164/ajrccm-conference.2009.179.1_meetingabstracts.a2802.
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Zhang, Lingang, and Chao Zhou. "The influence of OFDI model difference on the brand value of Chinese enterprises: -- OLS regression and analysis based on A-share listed company data." In 2020 16th Dahe Fortune China Forum and Chinese High-educational Management Annual Academic Conference (DFHMC). IEEE, 2020. http://dx.doi.org/10.1109/dfhmc52214.2020.00056.
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Blazheska, Angela, and Igor Ivanovski. "QUANTITATIVE ANALYSIS OF THE OPERATIONAL PERFORMANCE OF THE SELECTED NON-LIFE INSURANCE COMPANIES IN THE INSURANCE MARKET OF REPUBLIC OF NORTH MACEDONIA." In Economic and Business Trends Shaping the Future. Ss Cyril and Methodius University, Faculty of Economics-Skopje, 2020. http://dx.doi.org/10.47063/ebtsf.2020.0030.
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The aim of this paper is to analyze the operational performance of the 5 dominant companies on the non-life insurance market in Republic of North Macedonia. As input in the analysis, the quarterly data for the 2009-2019 period is included for the key indicators such as the gross written premium (GWP), the gross liquidated damages, the number of insurance contracts and settled claims as well as the operating costs of the companies. These variables are observed through OLS (Ordinary Least Squares) regression analysis and VAR (Vector Autoregressive) model which demonstrates the dependence of the GWP to the rest of the indicators and their responsiveness to shocks. The findings of the study offers valuable insight and opportunities for short term recommendations and further exploration. The companies are missing the sustainability and viability of their management models and define the “shortcism” as more important for the market and operational performance. In these regard, the business models must introduce contemporary and comprehensive tools and techniques, dominantly based on IT solutions and adequate HCM changes, for risk identification and actions for lowering the claims ratio and their volume. Moreover, all the companies should evaluate the elements of the operating costs, both for sales as well as of the administrative ones, as critical components for the companies’ profitability. Very importantly, significant changes at the ALM models and higher rate of returns should inevitably create additional advantage for dynamic and sustainable models for consumer acquisition and new products and services development.
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Brown, Jeffrey M., Emily B. Carper, Joseph A. Beck, and Alexander A. Kaszynski. "Emulation of As-Manufactured Transonic Rotor Airfoil Modal Behavior and the Significance of Frequency Veering." In ASME Turbo Expo 2019: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/gt2019-91670.
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Abstract This effort investigates the ability to efficiently emulate the modal characteristics of a transonic compressor rotor as a function of measured geometric variations. The transonic rotor is measured with a structured blue light scanner that provides a dense geometric representation of the as-manufactured part. Principal component analysis (PCA) is applied to create a reduced order, orthogonal basis of parameters used as independent emulator parameters. Ordinary least squares (OLS) and Gaussian stochastic process (GSP) regression models are employed as emulators for airfoil frequency and mode shape across the first twenty resonant modes. While many modes are emulated with high accuracy, some challenge conventional emulation. It is shown that geometric variations cause significant variation in modal behavior for closely spaced frequencies. This work identifies that as-manufactured geometry deviations can lead to frequency veering and associated chaotic modal behavior. Effective emulation of frequncy and mode shape is demonstrated, but the physics-based understanding of as-manufactured geometry frequeny veering defines the way for future improvements in emulation accuracy and computational requirements.
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Birškytė, Liucija. "The impact of government debt on public finance stability in Lithuania." In Contemporary Issues in Business, Management and Economics Engineering. Vilnius Gediminas Technical University, 2019. http://dx.doi.org/10.3846/cibmee.2019.030.
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Purpose – though the global financial crisis is well behind us several EU countries continue to experience problems with public finance stability and need to cope with the consequences of high public debt. The purpose of the article is to find the relationship between government debt and of public finance stability in Lithuania. Research methodology – in order to achieve the aim of the article Financial Stability Index (FSI) for Lithuania has been created. It is based on theory and previous research. To find the determinants of FSI the multiple regression analysis model was specified and tested using Ordinary Least Squares (OLS). Findings – the results of multiple regression analysis indicate the government debt has a statistically significant impact on FSI, ceteris paribus. Other findings of the research show that profit or loss of the non-financial sector, foreign trade balance as well as a foreign direct investment are significant determinants of public finance stability. Research limitations – one of the limitations of this research is the small sample size that has an impact on the validity and generalizability of the results. Having a longer time-series data or panel data for more countries would improve the robustness and applicability of research results. Practical implications – the results of the research provide guidance to policymakers in the public finance area. Originality/Value − this paper contributes to the scarce literature on government debt and other determinants of financial stability in Lithuania
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Makrevska Disoska, Elena, and Katerina Shapkova Kocevska. "THE IMPACT OF HUMAN FREEDOMS ON ECONOMIC GROWTH." In Economic and Business Trends Shaping the Future. Ss Cyril and Methodius University, Faculty of Economics-Skopje, 2020. http://dx.doi.org/10.47063/ebtsf.2020.0016.
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The impact of formal institutions, including rule of law, human rights, and civil liberties on economic growth has been in the focus of the latest research agenda of the new institutional economics due to the current pandemic of the Corona-19 virus. Some limitations are necessary to be imposed to address a pandemic, but this is a real risk of lasting deterioration in basic human freedoms. Increased surveillance, restrictions on free expression and information, and limits on public participation are becoming increasingly common. The present fear is that the authorities worldwide are using the current situation to repress human rights for political purposes. This paper aims to explore the effect of the overall institutional environment, understood as the concept of human freedom, on economic prosperity in different jurisdictions around the world. Human freedom is a general term for personal, civil, and economic freedom and therefore the interconnection with economic growth can be seen in both directions. In our analysis, we use the Human Freedom Index published by the Fraser Institute as a proxy for human freedom. Here, human freedom is understood as the absence of coercive constraint. The index is calculated based on 79 distinct indicators representing different aspects of personal and economic freedom. This analysis seeks to answer several questions. First, we are interested in examining whether there is empirical evidence about the causality between human freedoms and economic growth. Second, we are interested in whether human freedom has a positive impact on growth rates. And third, we are interested in examining the influence of other determinants on economic growth. To test the causality between human freedom and economic growth, we have conducted a Granger causality analysis. The empirical strategy for identification of the possible influence of human freedom to growth rates includes the development of ordinary least squares (OLS) panel regression models for selected economies of the world, or around 174 cross-section units (countries) in the period between 2008 and 2017.
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Koşan, Naime İrem, and Sudi Apak. "Trade Openness and Macroeconomic Policy in OECD Countries." In International Conference on Eurasian Economies. Eurasian Economists Association, 2015. http://dx.doi.org/10.36880/c06.01373.
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Trade openness has been subject to an important issue many studies in literature. It allows us to analyze potential trade as a percentage of gross domestic product. Total value of international trade in goods and services shows the countries’ integration into the world economy. Generally, small countries are more integrated because of their dependency on imports. On the other hand, there many variables which effects trade integration. Our study focuses on to analyze the effects on trade openness and make inferences for OECD countries.
 In this paper we aim to examine the relationship between trade openness and macro-economic indicators in OECD countries. To analyze the relationship, we used panel data regression analysis. Data obtained from World Bank, The Heritage Foundation and United Nations Conference on Trade and Development (UNCTAD). The panel data covers 2000-2013 periods and 33 countries. The analysis made through the Stata econometric packet program. We predicted pooled, fixed effects and random effects panel data models and analyzed them.
 It has been found that gross domestic savings, investment freedom, and unemployment rate are statistically significant. The results found in this paper show that investment freedom and gross domestic savings have positive effect on trade openness as we expected. On the other hand, unemployment rate has positive effect on trade openness. These findings have important policy implications for OECD countries. Our interpretation of these findings is that, integration to world economy has generally positive effects for macroeconomic factors in OECD countries, but it should be limited.
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Koşan, Naime İrem, Sudi Apak, and Selahattin Sarı. "International Trade and Macro-Economic Policy in Eurasian Economies." In International Conference on Eurasian Economies. Eurasian Economists Association, 2016. http://dx.doi.org/10.36880/c07.01494.
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International trade is defined the exchange of goods, services, and capital among various countries and regions. Also the potential of imports and exports account for an important part of growth. On the other hand, total value of international trade in goods and services shows the countries’ integration into the world economy. In this paper we focused on to analyze the effects on imports and make inferences for Eurasian Countries.
 In this paper we aim to examine the relationship between imports and macro-economic indicators in 6 Eurasian economies. To analyze the relationship, we used panel data regression analysis. Data obtained from World Bank. The panel data covers 1996-2012 periods and 6 countries which named Kazakhstan, Russian Federation, Uzbekistan, Kyrgyz Republic, Azerbaijan and Turkmenistan. We predicted pooled, fixed effects and random effects panel data models using the Stata and analyzed them. The dependent variable is defined the imports in our model. It has been found that gross domestic savings, foreign direct investments and, and exports are statistically significant for this countries.
 The results found in this paper show that gross domestic savings has negative effects on imports. On the other hand, for this 6 countries foreign direct investments (inflow) and exports have positive effects on imports as we expected. It shows us the economic positions of Eurasian countries still depend on Russian Federation. Also, these findings have important policy implications for Eurasian Countries. Our interpretation of these findings is that, integration to world economy has generally positive effects on foreign direct investments for this countries.
 
Reports on the topic "Pooled OLS regression analysis"
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Villamizar-Villegas, Mauricio, and Yasin Kursat Onder. Uncovering Time-Specific Heterogeneity in Regression Discontinuity Designs. Banco de la República de Colombia, 2020. http://dx.doi.org/10.32468/be.1141.
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The literature that employs Regression Discontinuity Designs (RDD) typically stacks data across time periods and cutoff values. While practical, this procedure omits useful time heterogeneity. In this paper we decompose the RDD treatment effect into its weighted time-value parts. This analysis adds richness to the RDD estimand, where each time-specific component can be different and informative in a manner that is not expressed by the single cutoff or pooled regressions. To illustrate our methodology, we present two empirical examples: one using repeated cross-sectional data and another using time-series. Overall, we show a significant heterogeneity in both cutoff and time-specific effects. From a policy standpoint, this heterogeneity can pick up key differences in treatment across economically relevant episodes. Finally, we propose a new estimator that uses all observations from the original design and which captures the incremental effect of policy given a state variable. We show that this estimator is generally more precise compared to those that exclude observations exposed to other cutoffs or time periods. Our proposed framework is simple and easily replicable and can be applied to any RDD application that carries an explicitly traceable time dimension.
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Stark, Sasha, Heather Wardle, and Isabel Burdett. Examining lottery play and risk among young people in Great Britain. GREO, 2021. http://dx.doi.org/10.33684/2021.002.
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Purpose & Significance: Despite the popularity of lottery and scratchcards and some evidence of gambling problems among players, limited research focuses on the risks of lottery and scratchcard play and predictors of problems, especially among young people. The purpose of this project is to examine whether lottery and scratchcard participation is related to gambling problems among 16-24 year olds in Great Britain and whether general and mental health and gambling behaviours explain this relationship. Methodology: Samples of 16-24 year olds were pooled from the 2012, 2015, and 2016 Gambling in England and Scotland: Combined Data from the Health Survey for England and the Scottish Health Survey (n=3,454). Bivariate analyses and Firth method logistic regression were used to examine the relationship between past-year lottery and scratchcard participation and gambling problems, assessing the attenuating role of mental wellbeing, mental health disorders, self-assessed general health, and playing other games in past year. Results: There is a significant association between scratchcard play and gambling problems. The association somewhat attenuated but remained significant after taking into account wellbeing, mental health disorders, general health, and engagement in other gambling activities. Findings also show that gambling problems are further predicted by age (20-24 years), gender (male), lower wellbeing, and playing any other gambling games. Implications: Results are valuable for informing youth-focused education, decisions around the legal age for National Lottery products, and the development of safer gambling initiatives for high risk groups and behaviours, such as scratchcard play.