Search results for "Econometric"

showing 10 items of 3780 documents

Assessing nonlinear structures in real exchange rates using recurrence plot strategies

2002

Purchasing power parity (PPP) is an important theory at the basis of a large number of economic models. However, the implication derived from the theory that real exchange rates must follow stationary processes is not conclusively supported by empirical studies. In a recent paper, Serletis and Gogas [Appl. Finance Econ. 10 (2000) 615] show evidence of deterministic chaos in several OECD exchange rates. As a consequence, PPP rejections could be spurious. In this work, we follow a two-stage testing procedure to test for nonlinearities and chaos in real exchange rates, using a new set of techniques designed by Webber and Zbilut [J. Appl. Physiol. 76 (1994) 965], called recurrence quantificatio…

Nonlinear systemPurchasing power parityRecurrence quantification analysisEconometricsStatistical and Nonlinear PhysicsEconomic modelCondensed Matter PhysicsSpurious relationshipRecurrence plotMathematical economicsMathematicsPhysica D: Nonlinear Phenomena
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Impact of Stock Price Jumps on Option Values

1999

Many empirical papers document the fact that the distribution of stock returns exhibits fatter tails than would be expected from a normal distribution. This might explain some of the pricing biases of the Black/Scholes model, which is] based on a normal return distribution. Given this result, alternative option pricing models should be based on one of the following three classes of return models: (1) a stationary process, such as a paretian stable or a student’s t-distribution, (2) a mixture of stationary distributions, such as two normal distributions with different means or variances, or a mixture of a diflusion and a pure jump process, or (3) a distribution such as a normal distribution …

Normal distributionCost priceFinancial economicsValuation of optionsJump diffusionJumpEconometricsMid priceEconomicsJump processFutures contract
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How do normalization schemes affect net spillovers? A replication of the Diebold and Yilmaz (2012) study

2019

Abstract This paper replicates the Diebold and Yilmaz (2012) study on the connectedness of the commodity market and three other financial markets: the stock market, the bond market, and the FX market, based on the Generalized Forecast Error Variance Decomposition, GEFVD. We show that the net spillover indices (of directional connectedness), used to assess the net contribution of one market to overall risk in the system, are sensitive to the normalization scheme applied to the GEFVD. We show that, considering data generating processes characterized by different degrees of persistence and covariance, a scalar-based normalization of the Generalized Forecast Error Variance Decomposition is pref…

Normalization (statistics)Economics and EconometricsSocial connectedness020209 energySettore SECS-P/05 - Econometria02 engineering and technologyNormalization schemeconnectednessSpillover effect0502 economics and business0202 electrical engineering electronic engineering information engineeringEconometrics050207 economicsMathematicsspillover normalization connectednessVector autoregression models05 social sciencesFinancial marketCovarianceCausalitySpilloverGeneral EnergynormalizationGeneralized forecast error variance decompositionCommodity price fluctuations Driving forces Nonparametric additive regression modelsVariance decomposition of forecast errorsBond marketStock marketSimulationNormalization schemes
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Biproportional methods of structural change analysis: A typological survey

2004

International audience; Analysts often are interested in learning how much an exchange system has changed over time or how two different exchange systems differ. Identifying structural difference in exchange matrices can be performed using either 'directed' or 'undirected' methods. Directed methods are based on the computation and comparison of column- or row-normalizations of the matrices. The choice of row or column for the normalization implies a specific direction of the exchanges, so that the column-wise normalized results should not be compared to the row-wise normalized results. In this category fall the simple comparison of coefficient matrices and the causative method. Undirected m…

Normalization (statistics)JEL : C - Mathematical and Quantitative Methods/C.C6 - Mathematical Methods • Programming Models • Mathematical and Simulation Modeling/C.C6.C67 - Input–Output ModelsEconomics and EconometricsJEL: C - Mathematical and Quantitative Methods/C.C6 - Mathematical Methods • Programming Models • Mathematical and Simulation Modeling/C.C6.C67 - Input–Output Modelscausative matrixComputationJEL: D - Microeconomics/D.D5 - General Equilibrium and Disequilibrium/D.D5.D57 - Input–Output Tables and AnalysisStructural difference[SHS.ECO]Humanities and Social Sciences/Economics and Financemathematical economicsinput-output analysisJEL: C - Mathematical and Quantitative Methods/C.C6 - Mathematical Methods • Programming Models • Mathematical and Simulation Modeling/C.C6.C63 - Computational Techniques • Simulation ModelingJEL : C - Mathematical and Quantitative Methods/C.C6 - Mathematical Methods • Programming Models • Mathematical and Simulation Modeling/C.C6.C63 - Computational Techniques • Simulation ModelingbiproportionMedian filterJEL : D - Microeconomics/D.D5 - General Equilibrium and Disequilibrium/D.D5.D57 - Input–Output Tables and Analysis[ SHS.ECO ] Humanities and Social Sciences/Economies and finances[SHS.ECO] Humanities and Social Sciences/Economics and FinanceAlgorithmMathematicsRAS
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Asymmetric semi-volatility spillover effects in EMU stock markets

2018

Abstract The aim of this paper is to quantify the strength and the direction of semi-volatility spillovers between five EMU stock markets over the 2000–2016 period. We use upside and downside semi-volatilities as proxies for downside risk and upside opportunities. In this way, we aim to complement the literature, which has focused mainly on the contemporaneous correlation between positive and negative returns, with the evidence of asymmetry also in semi-volatility transmission. For this purpose, we apply the Diebold and Yilmaz (2012) methodology, based on a generalized forecast error variance decomposition, to downside and upside realized semi-volatility series. While the analysis of Diebol…

Normalization (statistics)Multivariate statisticsEconomics and Econometrics050208 financeForecast error variance decomposition05 social sciencessemi-volatility asymmetry forecast error variance decompositionVolatility spilloverDownside riskSemi-volatilitySettore SECS-P/05 - EconometriaAsymmetryFull sampleSpilloverSpillover effect0502 economics and businessVHAREconometricsVariance decomposition of forecast errorsEconomicsSemi-volatility Asymmetry Forecast error variance decomposition Spillover VHAR050207 economicsStock (geology)FinanceInternational Review of Financial Analysis
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How to standardize (if you must)

2017

In many situations we are interested in appraising the value of a certain characteristic for a given individual relative to the context in which this value is observed. In recent years this problem has become prominent in the evaluation of scientific productivity and impact. A popular approach to such relative valuations consists in using percentile ranks. This is a purely ordinal method that may sometimes lead to counterintuitive appraisals, in that it discards all information about the distance between the raw values within a given context. By contrast, this information is partly preserved by using standardization, i.e., by transforming the absolute values in such a way that, within the s…

Normalization (statistics)z-scoreLocation statisticsStandardizationMonotonic functionLibrary and Information Sciences050905 science studiesSocial Sciences (all)NOPercentile rankCitation analysisEconometricsMathematicsCitation analysis; Dispersion statistics; Location statistics; m-score; Normalization; Standardization; z-score; Social Sciences (all); Computer Science Applications1707 Computer Vision and Pattern Recognition; Library and Information Sciences05 social sciencesCounterintuitiveGeneral Social SciencesLocation statisticDispersion statisticsComputer Science Applications1707 Computer Vision and Pattern RecognitionStandardizationComputer Science Applicationsm-scoreNormalizationConceptual frameworkCitation analysisCitation analysiNormative0509 other social sciences050904 information & library sciencesDispersion statistic
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A Cautionary Note on Incremental Fit Indices Reported by LISREL

2005

Abstract. Incremental fit indices (IFIs) are regularly used when assessing the fit of structural equation models. IFIs are based on the comparison of the fit of a target model with that of a null model. For maximum-likelihood estimation, IFIs are usually computed by using the χ2 statistics of the maximum-likelihood fitting function (ML-χ2). However, LISREL recently changed the computation of IFIs. Since version 8.52, IFIs reported by LISREL are based on the χ2 statistics of the reweighted least squares fitting function (RLS-χ2). Although both functions lead to the same maximum-likelihood parameter estimates, the two χ2 statistics reach different values. Because these differences are especi…

Null modelStatisticsNull (mathematics)EconometricsGeneral Social SciencesSample (statistics)Function (mathematics)General PsychologyStructural equation modelingConfirmatory factor analysisLISRELMathematicsMethodology
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Learning from foreign operation modes: The virtuous path for innovation

2020

In this article, we analyze the impact of learning from internationalization on small and medium enterprises’ (SMEs) performance along different development paths. Drawing on the exploitation versus exploration logic, we use an alternative view of foreign operation modes (the learning perspective) to provide insights into the impact of such learning on technological and organizational innovation as well as overall performance. Our results, which are derived from a sample of 132 SMEs active in traditional manufacturing industries, point to a path to superior performance that entails resource-augmenting operation modes and organizational innovation. JEL CLASSIFICATION: O31; F23; L25; M10; M1…

O31Economics and EconometricsL25Strategy and Management05 social sciencesM10operation modeExploitationexplorationGeneral Business Management and AccountingM16innovationInternationalizationorganizational learning0502 economics and businessPath (graph theory)ddc:650050211 marketingSmall and medium-sized enterprisesBusinessF23Business and International Management050203 business & managementIndustrial organization
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Upstream Product Market Regulations, ICT, R&D and Productivity

2017

Our study aims to assess the actual importance of the two main channels via which upstream anti-competitive sector regulations are usually considered to impact productivity growth, i.e. by acting as a disincentive to business investments in R&D and in ICT. We estimate the specific impacts of these two channels and their shares in the total impact as opposed to alternative channels of investments in other forms of intangible capital that we cannot explicitly consider for lack of appropriate data such as improvements in skills, management and organization. To achieve this, we specify an extended production function explicitly relating productivity to R&D and ICT capital as well as to upstream…

O43INNOVATIONo47 - "Measurement of Economic Growth; Aggregate Productivity; Cross-Country Output Convergence"jel:C23[SHS]Humanities and Social SciencesC50Economics[ SHS.ECO ] Humanities and Social Sciences/Economies and finances050207 economicsIndustrial organization050205 econometrics CointegrationR&D05 social sciencesEconomic Growth and Aggregate Productivity: OtherHETEROGENEOUS PANELS[SHS.ECO]Humanities and Social Sciences/Economics and Finance047MANUFACTURING FIRMSjel:L5jel:O57Capital (economics)8. Economic growthTESTSENTRYo49 - Economic Growth and Aggregate Productivity: OtherEconomics and EconometricsproductivityProduct marketCOINTEGRATIONgrowthCOMPETITIONMeasurement of Economic Growth; Aggregate Productivity; Cross-Country Output Convergenceregulations0502 economics and business[ SHS ] Humanities and Social Sciencesparasitic diseasesjel:O43Production (economics)jel:O47ProductivityTotal factor productivityUpstream (petroleum industry)MarketProductivity Growth Regulations Competition Catch-up R&D ICTjel:L16ICTjel:O33Panel dataPANEL-DATAReview of Income and Wealth
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HUMAN CAPITAL IN GROWTH REGRESSIONS: HOW MUCH DIFFERENCE DOES DATA QUALITY MAKE?.

2000

We construct a revised version of the Barro and Lee (1996) data set for a sample of OECD countries using previously unexploited sources and following a heuristic approach to obtain plausible time profiles for attainment levels by removing sharp breaks in the data that seem to reflect changes in classification criteria. It is then shown that these revised data perform much better than the Barro and Lee (1996) or Nehru et al (1995) series in a number of growth specifications. We interpret these results as an indication that poor data quality may be behind counterintuitive findings in the recent literature on the (lack of) relationship between educational investment and growth. Using our prefe…

Observational errorAggregate (data warehouse)Growth; Human CapitalSample (statistics)Human capitaljel:I20jel:O30jel:O40Data qualityEconometricsProduction (economics)Errors-in-variables modelsConstruct (philosophy)General Economics Econometrics and FinanceMathematics
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