Relevant bibliographies by topics / Regression analysis – Econometric models

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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 'Regression analysis – Econometric models'

Author: Grafiati
Published: 4 June 2021
Last updated: 19 February 2022

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Journal articles on the topic "Regression analysis – Econometric models"

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Chetverikov, Denis. "TESTING REGRESSION MONOTONICITY IN ECONOMETRIC MODELS." Econometric Theory 35, no. 4 (September 18, 2018): 729–76. http://dx.doi.org/10.1017/s0266466618000282.

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Monotonicity is a key qualitative prediction of a wide array of economic models derived via robust comparative statics. It is therefore important to design effective and practical econometric methods for testing this prediction in empirical analysis. This article develops a general nonparametric framework for testing monotonicity of a regression function. Using this framework, a broad class of new tests is introduced, which gives an empirical researcher a lot of flexibility to incorporate ex ante information she might have. The article also develops new methods for simulating critical values, which are based on the combination of a bootstrap procedure and new selection algorithms. These methods yield tests that have correct asymptotic size and are asymptotically nonconservative. It is also shown how to obtain an adaptive and rate optimal test that has the best attainable rate of uniform consistency against models whose regression function has Lipschitz-continuous first-order derivatives and that automatically adapts to the unknown smoothness of the regression function. Simulations show that the power of the new tests in many cases significantly exceeds that of some prior tests, e.g., that of Ghosal, Sen, and Van der Vaart (2000).
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Kuzmin, Peter, Vitaliy Kalashnikov, Natalyia Kalashnykova, and Junzo Watada. "The Great Depression: Econometric Analysis and Fuzzy Regression." Journal of Advanced Computational Intelligence and Intelligent Informatics 24, no. 6 (November 20, 2020): 785–91. http://dx.doi.org/10.20965/jaciii.2020.p0785.

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The study examines the period of the Great Depression and analyzes several measures taken by the US President Roosevelt’s government that allowed the country to get out of the crisis. An analysis and proof of the correctness of the measures chosen to exit from the crisis was conducted using econometric models and the use of statistics. Techniques for overcoming crises are relevant for all countries. This study adapts an innovative perspective in that it used the sequence of the Cobb–Douglas type function including different types of factors, and applied fuzzy regression methods.
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Phillips, P. C. B. "Partially Identified Econometric Models." Econometric Theory 5, no. 2 (August 1989): 181–240. http://dx.doi.org/10.1017/s0266466600012408.

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This paper studies a class of models where full identification is not necessarily assumed. We term such models partially identified. It is argued that partially identified systems are of practical importance since empirical investigators frequently proceed under conditions that are best described as apparent identification. One objective of the paper is to explore the properties of conventional statistical procedures in the context of identification failure. Our analysis concentrates on two major types of partially identified model: the classic simultaneous equations model under rank condition failures; and time series spurious regressions. Both types serve to illustrate the extensions that are needed to conventional asymptotic theory if the theory is to accommodate partially identified systems. In many of the cases studied, the limit distributions fall within the class of compound normal distributions. They are simply represented as covariance matrix or scalar mixtures of normals. This includes time series spurious regressions, where representations in terms of functionals of vector Brownian motion are more conventional in recent research following earlier work by the author.
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Kozinova, A. T. "An econometric analysis of retail turnover in Russia." Economic Analysis: Theory and Practice 19, no. 6 (June 29, 2020): 1133–53. http://dx.doi.org/10.24891/ea.19.6.1133.

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Subject. The article deals with econometric analysis of retail turnover in Russia and its relationship with macroeconomic indicators, like real disposable household income, consumer prices, etc. Objectives. The purpose is to create effective models to analyze the retail turnover in Russia and its relationship with other macroeconomic indicators, taking into account the existence of periods of economic instability. Methods. I apply correlation and regression methods to analyze statistics. To quantify changes in the retail turnover of Russia during the periods of economic instability, I use dummy variables. Results. The Russia’s retail trade turnover index had a reverse and moderate relationship with the consumer price index, direct and strong relationship with the indices of real disposable household income and imports, direct relationship with the manufacturing index. I offer statistically significant regression models of Russia’s retail turnover with the said macroeconomic indicators. Conclusions. The main advantage of models of retail turnover that are built using a large number of observations is a greater number of simultaneously considered factors. The quantitative assessment of retail turnover elasticity by consumer prices confirms the need for inflation targeting by the Central Bank of the Russian Federation. The higher elasticity of retail turnover in manufacturing as compared with the imports denotes the importance of import substitution policy.
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Rudzkis, R., and E. Mačiulaitytė. "Econometrical Modelling of Profit Tax Revenue." Nonlinear Analysis: Modelling and Control 12, no. 1 (January 25, 2007): 95–112. http://dx.doi.org/10.15388/na.2007.12.1.14724.

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The aim of this article is to present a forecast of budget revenue from the profit tax using econometric models. The set of applied models has to be reduced to very simple models due to short time series used. Therefore, the profit tax regression analysis is made in two stages. In the first stage, econometric modelling of profit tax revenue with the main profit indicators (called the profit tax base) is performed on the basis of information on profit tax regulation and its changes. In the second stage, algorithms of forecasting the profit tax base are formed when the main macroeconomic indicators of Lithuanian economy are used as regressors. Crossvalidation was applied to estimate the accuracy of these algorithms.
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Angrist, Joshua D., and Jörn-Steffen Pischke. "Undergraduate Econometrics Instruction: Through Our Classes, Darkly." Journal of Economic Perspectives 31, no. 2 (May 1, 2017): 125–44. http://dx.doi.org/10.1257/jep.31.2.125.

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The past half-century has seen economic research become increasingly empirical, while the nature of empirical economic research has also changed. In the 1960s and 1970s, an empirical economist's typical mission was to “explain” economic variables like wages or GDP growth. Applied econometrics has since evolved to prioritize the estimation of specific causal effects and empirical policy analysis over general models of outcome determination. Yet econometric instruction remains mostly abstract, focusing on the search for “true models” and technical concerns associated with classical regression assumptions. Questions of research design and causality still take a back seat in the classroom, in spite of having risen to the top of the modern empirical agenda. This essay traces the divergent development of econometric teaching and empirical practice, arguing for a pedagogical paradigm shift.
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Yao, Jia, Siqin Xiong, and Xiaoming Ma. "Comparative Analysis of National Policies for Electric Vehicle Uptake Using Econometric Models." Energies 13, no. 14 (July 13, 2020): 3604. http://dx.doi.org/10.3390/en13143604.

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As electric vehicles (EVs) have been widely discussed as a promising way to mitigate the effect of climate change, various policies have been implemented across the world to promote the uptake of EVs. Policymakers also paid attention to the density of public charging points. In this paper, we examined the impact of policies on EV markets in the post subsidy era with multiple linear regression analysis using panel data on 13 countries from 2015 to 2018. Five of the independent variables showed significantly positive effects on the 1% level in different regression models: fast/slow charger density, mandate, purchasing restriction and waiver. Subsidies showed significance only on 5% level for battery electric vehicles (BEVs). Financial stimulates have experienced a declining marginal effect, whereas a high density of fast chargers has the most significantly positive effect on EV uptake. This paper suggests policymakers can invest more in completing the public infrastructures of EVs, especially on fast charging points.
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Hausman, Jerry. "Mismeasured Variables in Econometric Analysis: Problems from the Right and Problems from the Left." Journal of Economic Perspectives 15, no. 4 (November 1, 2001): 57–67. http://dx.doi.org/10.1257/jep.15.4.57.

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The effect of mismeasured variables in the most straightforward regression analysis with a single regressor variable leads to a least squares estimate that is downward biased in magnitude toward zero. I begin by reviewing classical issues involving mismeasured variables. I then consider three recent developments for mismeasurement econometric models. The first issue involves difficulties in using instrumental variables. A second involves the consistent estimators that have recently been developed for mismeasured nonlinear regression models. Finally, I return to mismeasured left hand side variables, where I will focus on issues in binary choice models and duration models.
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Palant, Oleksii Yuriiovych, Hanna Volodymyrivna Ortina, and Marharyta Mykolaivna Kucher. "STATISTICAL ASSESSMENT OF SOCIO-ECONOMIC DETERMINATION OF CRIME IN UKRAINE." SCIENTIFIC BULLETIN OF POLISSIA, no. 4(16) (2018): 14–20. http://dx.doi.org/10.25140/2410-9576-2018-4(16)-14-20.

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The problem of analyzing the socio-economic determination of crime based on the use of econometric models has been studied, the correlation-regression models have been adapted, the applied aspects of the constructed regression equation to solve the problems of crime determination analysis have been considered
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Kumar, Ajay. "Climate Change and Sugarcane Productivity in India: An Econometric Analysis." Journal of Social and Development Sciences 5, no. 2 (June 30, 2014): 111–22. http://dx.doi.org/10.22610/jsds.v5i2.811.

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This study provides an understanding for the relationship between climatic factors and sugarcane productivity in India. The main objective of this paper is to estimates the impact of climatic and non-climatic factors on sugarcane productivity. To check the consistency of empirical results, simple linear regression model, Ricardian productivity regression (non-linear) model and Cobb-Douglas production function models are employed. The data set incorporates 390 observations corresponding to thirteen states with panel data for 30 years during 1980 to 2009. These all models include sugarcane productivity as dependent variable. Irrigated area, agriculture labour, consumption of fertilizers, literacy rate, tractors and farm harvest price (at constant level) are considered as explanatory variables. Average rainfall, average maximum and average minimum temperature include as climatic factors to capture the effect of climatic conditions on cane productivity. These climatic factors are incorporate for three weather seasons such as rainy, winter and summer. Empirical results based on Prais Winsten models with panels corrected standard errors (PCSEs) estimation shows that climatic factors i.e. actual rainfall, average maximum and average minimum temperature have a statistically significant impact on sugarcane productivity. The climatic effect for various factors on cane productivity are varies within different seasons. Average maximum temperature in summer and average minimum temperature in rainy season have a negative and statistically significant effect on sugarcane productivity. While, sugarcane productivity positively get affect with increasing average maximum temperature in rainy season and winter seasons. The study concluded that there is non-linear relationship between climatic factors and sugarcane productivity in India.
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Dissertations / Theses on the topic "Regression analysis – Econometric models"

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Yeasmin, Mahbuba 1965. "Multiple maxima of likelihood functions and their implications for inference in the general linear regression model." Monash University, Dept. of Econometrics and Business Statistics, 2003. http://arrow.monash.edu.au/hdl/1959.1/5821.

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Pitrun, Ivet 1959. "A smoothing spline approach to nonlinear inference for time series." Monash University, Dept. of Econometrics and Business Statistics, 2001. http://arrow.monash.edu.au/hdl/1959.1/8367.

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Marchenko, Maria [Verfasser], and Enno [Akademischer Betreuer] Mammen. "Econometric analysis of quantile regression models and networks : With empirical applications / Maria Marchenko ; Betreuer: Enno Mammen." Mannheim : Universitätsbibliothek Mannheim, 2016. http://d-nb.info/1114661287/34.

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Volgina, Vera. "Postmerger financial performance: econometric analysis." Master's thesis, Vysoká škola ekonomická v Praze, 2009. http://www.nusl.cz/ntk/nusl-16850.

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There are numerous researches done in the last couple decades dedicated to the observation of impact of merges and acquisitions on the performance of the company. The topic is considered to be up-to-date, as still there is no common approach to evaluating of benefits mergers are about to bring to a new established entity. In this thesis the issue of post-merger financial performance is investigated on an example of three biggest energy companies in Europe: RWE, E.ON and Vattenfall. The aim of the thesis is to find out whether financial performance of chosen companies improves after the merger occurs. This target is elaborated with a help of the analysis of commonly used financial ratios in corporate finance and construction of two regression models, which explain the interrelations between basic indicator of the company's growth (net income), the fact of the merger and determined financial ratios. As an outcome of the research, a few findings were obtained, such as worsening of financial performance three to five years after the merger, with continuing improvement in further years, quite stable financial indicators before the merger, positive interconnection between the fact of the merger and the net income. Such outcomes might be considered as significant, though further research and elaboration of the topic can be performed in the future.
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Wesso, Gilbert R. "The econometrics of structural change: statistical analysis and forecasting in the context of the South African economy." University of the Western Cape, 1994. http://hdl.handle.net/11394/7907.

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Philosophiae Doctor - PhD
One of the assumptions of conventional regression analysis is I that the parameters are constant over all observations. It has often been suggested that this may not be a valid assumption to make, particularly if the econometric model is to be used for economic forecasting0 Apart from this it is also found that econometric models, in particular, are used to investigate the underlying interrelationships of the system under consideration in order to understand and to explain relevant phenomena in structural analysis. The pre-requisite of such use of econometrics is that the regression parameters of the model is assumed to be constant over time or across different crosssectional units.
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Huh, Ji Young. "Applications of Monte Carlo Methods in Statistical Inference Using Regression Analysis." Scholarship @ Claremont, 2015. http://scholarship.claremont.edu/cmc_theses/1160.

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This paper studies the use of Monte Carlo simulation techniques in the field of econometrics, specifically statistical inference. First, I examine several estimators by deriving properties explicitly and generate their distributions through simulations. Here, simulations are used to illustrate and support the analytical results. Then, I look at test statistics where derivations are costly because of the sensitivity of their critical values to the data generating processes. Simulations here establish significance and necessity for drawing statistical inference. Overall, the paper examines when and how simulations are needed in studying econometric theories.
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AraÃjo, Ana Maria MaurÃcio. "Analysis of practices of management environmental and its impacts on productivity of shrimp farming in Ceara State." Universidade Federal do CearÃ, 2015. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=14305.

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CoordenaÃÃo de AperfeÃoamento de Pessoal de NÃvel Superior
FundaÃÃo Cearense de Apoio ao Desenvolvimento Cientifico e TecnolÃgico
Shrimp farming has been consolidated as one of the most promising economic activities of the Northeast, where it has also been shown to be responsible for high negative impacts on the coastal environment. The research problem was to see how productivity is affected by the adoption of environmental management practices by analyzing the interaction between the productivity factor and other environmental management factors. To do so, we estimated the linear type regression analysis, to obtain a mathematical equation which quantify the relationship between productivity and other variables. The survey was conducted in 60 shrimp farms located in CearÃ, on farms intended only for fattening phase. Were raised environmental management practices adopted by producers and created management indices,where these indices were aggregated into a single index that along with the variables that describe the productive characteristics and location of the farms originated econometric linlog semi-logarithmic models. Regression analysis showed that the yield is better explained by the storage density, intensive production system periodic servicing. Environmental management is not configured as a factor that influences productivity, justifying the low level of environmental management by shrimp farmers.
A carcinicultura vem se consolidando como uma das mais promissoras atividades econÃmicas da RegiÃo Nordeste, onde tambÃm tem sido apresentada como responsÃvel por elevados impactos negativos sobre o ambiente costeiro. O problema da pesquisa consistiu em verificar como a produtividade à afetada pela adoÃÃo de prÃticas de gestÃo ambiental, atravÃs da anÃlise da interaÃÃo entre o fator produtividade e os outros fatores de gestÃo ambiental. Para isto, estimou-se uma anÃlise de regressÃo do tipo linear, para obter uma equaÃÃo matemÃtica que quantificasse o relacionamento entre produtividade e outras variÃveis. A pesquisa foi realizada em 60 fazendas de carcinicultura localizadas no CearÃ, em fazendas destinadas somente à fase de engorda. Foram levantadas as prÃticas de gestÃo ambiental adotadas pelos produtores e criados Ãndices de manejo, onde estes Ãndices foram agregados em um Ãnico Ãndice que juntamente com as variÃveis que descrevem as caracterÃsticas produtivas e de localizaÃÃo das fazendas originou modelos economÃtricos semi-logarÃtmicos lin-log. A anÃlise de regressÃo mostrou que a produtividade à melhor explicada pela densidade de estocagem, sistema de produÃÃo intensivo a assistÃncia tÃcnica periÃdica. A gestÃo ambiental nÃo se configura como um fator que influencie a produtividade, justificando o baixo nÃvel de gestÃo ambiental pelos carcinicultores.
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COLAGROSSI, MARCO. "META-ANALYSIS AND META-REGRESSION ANALYSIS IN ECONOMICS: METHODOLOGY AND APPLICATIONS." Doctoral thesis, Università Cattolica del Sacro Cuore, 2017. http://hdl.handle.net/10280/19697.

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A partire dagli anni ’80, la diffusione dei metodi statistici, abbinata ai progressi nelle capacità computazionali dei personal computers, ha progressivamente facilitato i ricercatori nel testare empiricamente le proprie teorie. Gli economisti sono diventati in grado di eseguire milioni di regressioni prima di pranzo senza abbandonare le proprie scrivanie. Purtroppo, ciò ha portato ad un accumulo di evidenze spesso eterogenee, quando non contradditorie se non esplicitamente in conflitto. Per affrontare il problema, questa tesi fornirà una panoramica dei metodi meta-analitici disponibili in economia. Nella prima parte verranno introdotte le intuizioni alla base dei modelli gerarchici a fattori fissi e casuali capaci di risolvere le problematicità derivanti dalla presenza di osservazioni non indipendenti. Verrà inoltre affrontato il tema dell’errore sistematico di pubblicazione in presenza di elevata eterogeneità tra gli studi. La metodologia verrà successivamente applicata, nella seconda e terza parte, a due diverse aree della letteratura economica: l’impatto del rapporto banca-impresa sulle prestazioni aziendali e il dibattito sulla relazione fra democrazia e crescita. Mentre nel primo caso la correlazione negativa non è influenzata da fattori specifici ai singoli paesi, il contrario è vero per spiegare l’impatto (statisticamente non significativo) delle istituzioni democratiche sullo sviluppo economico. Quali siano questi fattori è però meno chiaro; gli studiosi non hanno ancora individuato le co-variate – o la corretta misurazione di esse – capaci di spiegare questa discussa relazione.
Starting in the late 1980s, improved computing performances and spread knowledge of statistical methods allowed researchers to put their theories to test. Formerly constrained economists became able [to] run millions of regressions before lunch without leaving their desks. Unfortunately, this led to an accumulation of often conflicting evidences. To address such issue, this thesis will provide an overview of the meta-analysis methods available in economics. The first paper will explain the intuitions behind fixed and random effects models in such a framework. It will then detail how multilevel modelling can help overcome hierarchical dependence issues. Finally, it will address the problem of publication bias in presence of high between-studies heterogeneity. Such methods will be then applied, in the second and third papers, to two different areas of the economics literature: the effect of relationship banking on firm performances and the democracy and growth conundrum. Results are far-reaching. While in the first case the documented negative relation is not driven by country-specific characteristics the opposite is true for the (statistically insignificant) impact of democratic institutions on economic growth. What these characteristics are is, however, less clear. Scholars have not yet found the covariates - or their suitable proxies - that matter to explain such much-debated relationship.
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Cowley, Mervyn Wellesley. "Property market forecasts and their valuation implications: a study of the Brisbane central business district office market." Queensland University of Technology, 2007. http://eprints.qut.edu.au/16563/.

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Property market forecasts play a crucial role in modern real estate valuation methodologies and, consequently, flawed forecasts can have adverse impacts on the accuracy of valuations. This thesis identifies property industry inconsistencies in the formulation and application of office rent forecasts adopted in discounted cash flow (DCF) studies used to assess the value of commercial properties and the viability of proposed projects. Existing research on commercial property cycles and office property market modelling is examined in order to identify the dominant market drivers adopted by researchers. Forecasting techniques are also explored towards specifying space and rent models for the Brisbane CBD office market using the perceived dominant drivers as explanatory variables. Surveys of property valuers and developers are undertaken to underpin the selection of these variables. The implications of varying rent forecasts applied in DCF based valuation assessments are tested through the use of a case study involving four Brisbane office buildings. Innovative research is conducted through adopting geographic information system supported land use and historical valuation studies to delineate market precincts within the Brisbane CBD. The rent model is then re-estimated using precinct based office rent data to allow the generation of forecasts for the individual precincts. Out-of-sample accuracy test results for the precinct forecasts are compared with the results produced by the model specified using whole-of-city data. The literature reviews, surveys and model testing determine a relatively consistent range of dominant explanatory variables applicable to office markets. The case study, in a local context, confirms that varying forecasts do have a significant impact on property valuations. Tests of the forecast results generated by the Brisbane CBD model provide some evidence that more plausible office rent forecasts stem from the use of market models as compared with solely applying professional judgement based forecasts. Subject to data availability limitations, the precinct based rent model is found to produce rent forecasts superior to those generated by the whole-of-city model. Finally, the thesis makes a range of industry recommendations towards enhancing forecasts and recommendations are also made for potential future research projects.
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Miskolczi, Martina. "Vícestavová analýza nezaměstnanosti a další statistické metody pro modelování nezaměstnanosti." Doctoral thesis, Vysoká škola ekonomická v Praze, 2010. http://www.nusl.cz/ntk/nusl-201115.

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Unemployment modelling covers both view of the labour market such as is, economy and knowledge of mathematics, statistics and, thus, econometrics. The importance of unemployment seems to be even more significant after the period of crisis; high unemployment is not only economic burden bud serious social risk and psychological problem as well. In the dissertation thesis, selected models used for unemployment modelling and -- in some cases for its prediction -- are introduced. To be able to predict the future trend of labour market reliably means to be able to plan tools of active and passive employment policies effectively. Alternatively, it means to search programs and supports that help in reduction of unemployment. Specific applications of models for the Czech labour market involve model of multistate life tables, simultaneous econometric models and Phillips curve. Phillips curve of mutual "trade-off" of unemployment and inflation is confirmed in short periods, in longer and long period of time rather fails, it is not reliable. It is not possible to use it for prediction at all; it would be needed to predict inflation. Analogous characteristics has the Beveridge curve. Simultaneous econometric models for number of economically active persons and for unemployment and inflation de facto fail, even though they demonstrate the range of opportunities including point and interval forecasts. Period of economic crisis when changes in labour market principles occur means usually problem for such the models, which work well in periods of stable growth or decline. More, it is difficult to specify these models correctly with regard to threat of multikolinearity. Multistate models aiming at calculation of multistate life tables, or even multistate projection are extremely demanding for input data. But they enable to understand relations or transitions among states, respectively. It is very beneficial tool for comprehension and policy planning in the area of labour market and social affairs in the process of lowering unemployment. Forecasts in such a type of model are possible but difficult because it is necessary to predict probability of transition among states.
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Books on the topic "Regression analysis – Econometric models"

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Bootstrap tests for regression models. Basingstoke, Hampshire: Palgrave Macmillan, 2009.

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Godfrey, L. G. Bootstrap tests for regression models. Basingstoke, Hampshire: Palgrave Macmillan, 2009.

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Hylleberg, Svend. Seasonality in regression. Orlando: Academic Press, 1986.

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Elbers, Chris. Imputed welfare estimates in regression analysis. Washington, D.C: World Bank, 2004.

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Seasonality in regression. Orlando, Fla: Academic Press, 1986.

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Bährens, Henning. Partielle und simultane Prüfung auf Autokorrelation und Heteroskedastizität der Störvariablen im linearen Regressionsmodell. Göttingen: Unitext, 1992.

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John, DiNardo, ed. Econometric methods. 4th ed. New Yorki: McGraw Hil, 2007.

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Short-memory linear processes and econometric applications. Hoboken, N.J: Wiley, 2011.

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Empirical vector autoregressive modeling. Berlin: Springer-Verlag, 1994.

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Bhatti, M. Ishaq. Non-Nested Regression Models: UK ed. edition. Hauppauge, New York, USA: Nova Science Publishers Inc, 2013.

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Book chapters on the topic "Regression analysis – Econometric models"

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Allen, David E., and Singh Robert Powell. "Asset Pricing, the Fama—French Factor Model and the Implications of Quantile-Regression Analysis." In Financial Econometrics Modeling: Market Microstructure, Factor Models and Financial Risk Measures, 176–93. London: Palgrave Macmillan UK, 2011. http://dx.doi.org/10.1057/9780230298101_7.

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Florax, Raymond J. G. M., and Thomas de Graaff. "The Performance of Diagnostic Tests for Spatial Dependence in Linear Regression Models: A Meta-Analysis of Simulation Studies." In Advances in Spatial Econometrics, 29–65. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-662-05617-2_2.

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LeSage, James P., and R. Kelley Pace. "Spatial Econometric Models." In Handbook of Applied Spatial Analysis, 355–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03647-7_18.

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Härdle, Wolfgang Karl, and Léopold Simar. "Regression Models." In Applied Multivariate Statistical Analysis, 233–59. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-26006-4_8.

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Härdle, Wolfgang Karl, and Léopold Simar. "Regression Models." In Applied Multivariate Statistical Analysis, 253–80. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-45171-7_8.

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Jöreskog, Karl G., Ulf H. Olsson, and Fan Y. Wallentin. "Regression Models." In Multivariate Analysis with LISREL, 35–133. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-33153-9_2.

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Härdle, Wolfgang Karl, and Léopold Simar. "Regression Models." In Applied Multivariate Statistical Analysis, 229–53. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-17229-8_8.

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Karim, Md Rezaul, and M. Ataharul Islam. "Regression Models." In Reliability and Survival Analysis, 115–41. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9776-9_7.

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Chen, Jie, and Arjun K. Gupta. "Regression Models." In Parametric Statistical Change Point Analysis, 139–54. Boston: Birkhäuser Boston, 2011. http://dx.doi.org/10.1007/978-0-8176-4801-5_4.

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Chen, Jie, and A. K. Gupta. "Regression Models." In Parametric Statistical Change Point Analysis, 111–25. Boston, MA: Birkhäuser Boston, 2000. http://dx.doi.org/10.1007/978-1-4757-3131-6_4.

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Conference papers on the topic "Regression analysis – Econometric models"

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Aydın, Eren Gül. "A Study on Informal Employment in Turkey from Theorical and Emprical Perspectives." In International Conference on Eurasian Economies. Eurasian Economists Association, 2013. http://dx.doi.org/10.36880/c04.00824.

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This paper elaborates both theorical and empirical evolution of the informal employment as an part of the informal economy in Turkey. First of all, economic dualism and migration is emphasized as the main reasons of informal employment and examine other reasons of the informal employment such as social,fiscal and economic.Later of this study, an econometric model is employed by using Multiple Linear Regression Analysis. Informal employment is used as independent variables of industrial production(IPI). The first result of econometrics model, informal employment was seen statistically significiant determinator of IPI. Secondly, some production and employment variables and Gross National Product are used for estimating decisive factor of the informal employment in Turkey between 2005 and 2012. Results show that GDP and all other variables used in regression positively corelated to informal employment except import and export in Turkey.
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Медведева, Анастасия, Anastasiya Medvedeva, Анастасия Рыжкова, and Anastasia Ryzhkova. "Econometric analysis of the efficiency of investments in the main capital of the southern federal district." In Mathematics in Economics. AUS PUBLISHERS, 2018. http://dx.doi.org/10.26526/conferencearticle_5c24b1d0845065.42036660.

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The article discusses the phased construction of a regression model for the main sources of financing investments in fixed capital of the Southern Federal District. The authors have compiled an assessment of the significance of the regression model obtained and also analyzed the results in terms of investment returns and effectiveness within the framework of the regional testing
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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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Dobrina, Maria V., Yana A. Yurova, and Galina V. Shurshikova. "Econometric Models with Discrete Dependent Variable in Portfolio Analysis." In Proceedings of the 2nd International Conference on Economy, Management and Entrepreneurship (ICOEME 2019). Paris, France: Atlantis Press, 2019. http://dx.doi.org/10.2991/icoeme-19.2019.18.

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Sedlak, Otilija, Jelena Birovljev, Zoran Ciric, Jelica Eremic, and Ivana Ciric. "ANALYSIS OF COMPETITIVENESS OF HIGHER EDUCATION WITH ECONOMETRIC MODELS." In International Conference on Education and New Learning Technologies. IATED, 2016. http://dx.doi.org/10.21125/edulearn.2016.1121.

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Kollias, Spyros K., Vasileios Vlachos, Alexandros Papanikolaou, and Vassilis Assimakopoulos. "Adapting Econometric Models, Technical Analysis and Correlation Data to Computer Security Data." In 2011 First SysSec Workshop. IEEE, 2011. http://dx.doi.org/10.1109/syssec.2011.18.

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Duller, Christine. "Model selection for logistic regression models." In NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2012: International Conference of Numerical Analysis and Applied Mathematics. AIP, 2012. http://dx.doi.org/10.1063/1.4756152.

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Xu, Easton Li, Xiaoning Qian, Tie Liu, and Shuguang Cui. "Pairwise interaction analysis of logistic regression models." In 2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP). IEEE, 2016. http://dx.doi.org/10.1109/globalsip.2016.7905829.

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Reka, R. "Computing dissimilar types of regression analysis models." In PROCEEDINGS OF INTERNATIONAL CONFERENCE ON ADVANCES IN MATERIALS RESEARCH (ICAMR - 2019). AIP Publishing, 2020. http://dx.doi.org/10.1063/5.0017636.

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Singla, Chaitanya, Pradeepta Kumar Sarangi, Sunny Singh, Ashok Kumar Sahoo, and Neetu Mittal. "Predictive Analytics: A Comparative Study of Expert Modeler Vs ANN Models in Econometric Analysis." In 2020 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO). IEEE, 2020. http://dx.doi.org/10.1109/icrito48877.2020.9197969.

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Reports on the topic "Regression analysis – Econometric models"

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Chetverikov, Denis. Testing regression monotonicity in econometric models. Institute for Fiscal Studies, November 2012. http://dx.doi.org/10.1920/wp.cem.2012.3512.

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Mullahy, John. Multivariate Fractional Regression Estimation of Econometric Share Models. Cambridge, MA: National Bureau of Economic Research, September 2010. http://dx.doi.org/10.3386/w16354.

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Heckman, James, and Christopher Taber. Econometric Mixture Models and More General Models for Unobservables in Duration Analysis. Cambridge, MA: National Bureau of Economic Research, June 1994. http://dx.doi.org/10.3386/t0157.

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Harris, J. M., S. D. Frans, P. E. Poston, and A. L. Wong. Advances in Regression: Use of Models in Spectroscopic Data Analysis. Fort Belvoir, VA: Defense Technical Information Center, July 1989. http://dx.doi.org/10.21236/ada210541.

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West, Kenneth. Econometric Analysis of Present Value Models When the Discount Factor Is near One. Cambridge, MA: National Bureau of Economic Research, July 2012. http://dx.doi.org/10.3386/w18247.

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Mauchant, Diane, Kirk D. Rice, Michael A. Riley, Dennis Lebber, Daniel Samarov, and Amanda L. Forster. Analysis of three different regression models to estimate the ballistic performance of new and environmentally conditioned body armor. Gaithersburg, MD: National Institute of Standards and Technology, February 2011. http://dx.doi.org/10.6028/nist.ir.7760.

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Kim, Changmo, Ghazan Khan, Brent Nguyen, and Emily L. Hoang. Development of a Statistical Model to Predict Materials’ Unit Prices for Future Maintenance and Rehabilitation in Highway Life Cycle Cost Analysis. Mineta Transportation Institute, December 2020. http://dx.doi.org/10.31979/mti.2020.1806.

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The main objectives of this study are to investigate the trends in primary pavement materials’ unit price over time and to develop statistical models and guidelines for using predictive unit prices of pavement materials instead of uniform unit prices in life cycle cost analysis (LCCA) for future maintenance and rehabilitation (M&R) projects. Various socio-economic data were collected for the past 20 years (1997–2018) in California, including oil price, population, government expenditure in transportation, vehicle registration, and other key variables, in order to identify factors affecting pavement materials’ unit price. Additionally, the unit price records of the popular pavement materials were categorized by project size (small, medium, large, and extra-large). The critical variables were chosen after identifying their correlations, and the future values of each variable were predicted through time-series analysis. Multiple regression models using selected socio-economic variables were developed to predict the future values of pavement materials’ unit price. A case study was used to compare the results between the uniform unit prices in the current LCCA procedures and the unit prices predicted in this study. In LCCA, long-term prediction involves uncertainties due to unexpected economic trends and industrial demand and supply conditions. Economic recessions and a global pandemic are examples of unexpected events which can have a significant influence on variations in material unit prices and project costs. Nevertheless, the data-driven scientific approach as described in this research reduces risk caused by such uncertainties and enables reasonable predictions for the future. The statistical models developed to predict the future unit prices of the pavement materials through this research can be implemented to enhance the current LCCA procedure and predict more realistic unit prices and project costs for the future M&R activities, thus promoting the most cost-effective alternative in LCCA.
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Khadan, Jeetendra, Nekeisha Spencer, Eric Strobl, and Theophiline Bose-Duker. Socioeconomic Factors Associated with Being Overweight or Obese in Suriname. Inter-American Development Bank, June 2021. http://dx.doi.org/10.18235/0003348.

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This paper applies probit regression models to a nationally representative household survey dataset collected in 2016-2017 to analyze the relationships between various socio-demographic variables and adult Body Mass Index (BMI) in Suriname. Our results indicate that women, the elderly, and couples either married and/or living together are more likely to be obese or overweight. As expected, this is also true for individuals who have chronic illnesses. The analysis also finds that individuals who engage in a sport or in other forms of exercise, even if modest, have lower odds of being overweight or obese. Interestingly, the findings indicate that individuals who benefit from government social safety net programs are less likely to be associated with being overweight or obese. The results of this study have implications for the adjustment of current Surinamese nutritional guidelines as well as the design and implementation of targeted obesity-reduction policies that recognize that being overweight is influenced by various characteristics. Although the results are country-specific, they have the potential to influence action in all countries in the Caribbean that lack policies to address obesity.
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Arhin, Stephen, Babin Manandhar, Hamdiat Baba Adam, and Adam Gatiba. Predicting Bus Travel Times in Washington, DC Using Artificial Neural Networks (ANNs). Mineta Transportation Institute, April 2021. http://dx.doi.org/10.31979/mti.2021.1943.

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Washington, DC is ranked second among cities in terms of highest public transit commuters in the United States, with approximately 9% of the working population using the Washington Metropolitan Area Transit Authority (WMATA) Metrobuses to commute. Deducing accurate travel times of these metrobuses is an important task for transit authorities to provide reliable service to its patrons. This study, using Artificial Neural Networks (ANN), developed prediction models for transit buses to assist decision-makers to improve service quality and patronage. For this study, we used six months of Automatic Vehicle Location (AVL) and Automatic Passenger Counting (APC) data for six Washington Metropolitan Area Transit Authority (WMATA) bus routes operating in Washington, DC. We developed regression models and Artificial Neural Network (ANN) models for predicting travel times of buses for different peak periods (AM, Mid-Day and PM). Our analysis included variables such as number of served bus stops, length of route between bus stops, average number of passengers in the bus, average dwell time of buses, and number of intersections between bus stops. We obtained ANN models for travel times by using approximation technique incorporating two separate algorithms: Quasi-Newton and Levenberg-Marquardt. The training strategy for neural network models involved feed forward and errorback processes that minimized the generated errors. We also evaluated the models with a Comparison of the Normalized Squared Errors (NSE). From the results, we observed that the travel times of buses and the dwell times at bus stops generally increased over time of the day. We gathered travel time equations for buses for the AM, Mid-Day and PM Peaks. The lowest NSE for the AM, Mid-Day and PM Peak periods corresponded to training processes using Quasi-Newton algorithm, which had 3, 2 and 5 perceptron layers, respectively. These prediction models could be adapted by transit agencies to provide the patrons with accurate travel time information at bus stops or online.
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