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1 – 10 of over 5000Otávio Bartalotti and Quentin Brummet
Regression discontinuity designs have become popular in empirical studies due to their attractive properties for estimating causal effects under transparent assumptions…
Abstract
Regression discontinuity designs have become popular in empirical studies due to their attractive properties for estimating causal effects under transparent assumptions. Nonetheless, most popular procedures assume i.i.d. data, which is unreasonable in many common applications. To fill this gap, we derive the properties of traditional local polynomial estimators in a fixed-
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Bart J. Bronnenberg and Paulo Albuquerque
A significant portion of academic research on marketing strategy focuses on how national brands of repeat-purchase goods are managed or should be managed. Surprisingly little…
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A significant portion of academic research on marketing strategy focuses on how national brands of repeat-purchase goods are managed or should be managed. Surprisingly little consideration is given in this tradition to the extended role of geography, i.e. distance and space. For instance, manufacturers of brands in non-durable product categories are well aware of the fact that their national brands perform very different across domestic U.S. markets. This holds even for product categories with limited product differentiation. In this chapter, we outline various processes through which the influence of geography on performance of national brands materializes. We discuss a number of alternative explanations for the emergence and sustenance of spatial concentration of market shares. Several of these explanations are modeled empirically using data from the United States packaged goods industry. This chapter closes with avenues for further academic research on spatial aspects of the growth of new products.
This paper gives a selective review on some recent developments of nonparametric methods in both continuous and discrete time finance, particularly in the areas of nonparametric…
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This paper gives a selective review on some recent developments of nonparametric methods in both continuous and discrete time finance, particularly in the areas of nonparametric estimation and testing of diffusion processes, nonparametric testing of parametric diffusion models, nonparametric pricing of derivatives, nonparametric estimation and hypothesis testing for nonlinear pricing kernel, and nonparametric predictability of asset returns. For each financial context, the paper discusses the suitable statistical concepts, models, and modeling procedures, as well as some of their applications to financial data. Their relative strengths and weaknesses are discussed. Much theoretical and empirical research is needed in this area, and more importantly, the paper points to several aspects that deserve further investigation.
Kenneth Y. Chay and Dean R. Hyslop
We examine the roles of sample initial conditions and unobserved individual effects in consistent estimation of the dynamic binary response panel data model. Different…
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We examine the roles of sample initial conditions and unobserved individual effects in consistent estimation of the dynamic binary response panel data model. Different specifications of the model are estimated using female welfare and labor force participation data from the Survey of Income and Program Participation. These include alternative random effects (RE) models, in which the conditional distributions of both the unobserved heterogeneity and the initial conditions are specified, and fixed effects (FE) conditional logit models that make no assumptions on either distribution. There are several findings. First, the hypothesis that the sample initial conditions are exogenous is rejected by both samples. Misspecification of the initial conditions results in drastically overstated estimates of the state dependence and understated estimates of the short- and long-run effects of children on labor force participation. The FE conditional logit estimates are similar to the estimates from the RE model that is flexible with respect to both the initial conditions and the correlation between the unobserved heterogeneity and the covariates. For female labor force participation, there is evidence that fertility choices are correlated with both unobserved heterogeneity and pre-sample participation histories.
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James P. LeSage and R. Kelley Pace
For this discussion, assume there are n sample observations of the dependent variable y at unique locations. In spatial samples, often each observation is uniquely associated with…
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For this discussion, assume there are n sample observations of the dependent variable y at unique locations. In spatial samples, often each observation is uniquely associated with a particular location or region, so that observations and regions are equivalent. Spatial dependence arises when an observation at one location, say y i is dependent on “neighboring” observations y j, y j∈ϒi. We use ϒi to denote the set of observations that are “neighboring” to observation i, where some metric is used to define the set of observations that are spatially connected to observation i. For general definitions of the sets ϒi,i=1,…,n, typically at least one observation exhibits simultaneous dependence, so that an observation y j, also depends on y i. That is, the set ϒj contains the observation y i, creating simultaneous dependence among observations. This situation constitutes a difference between time series analysis and spatial analysis. In time series, temporal dependence relations could be such that a “one-period-behind relation” exists, ruling out simultaneous dependence among observations. The time series one-observation-behind relation could arise if spatial observations were located along a line and the dependence of each observation were strictly on the observation located to the left. However, this is not in general true of spatial samples, requiring construction of estimation and inference methods that accommodate the more plausible case of simultaneous dependence among observations.
I study state dependence in social assistance receipt in Germany using annual survey data from the German Socio-Economic Panel for the years 1995–2011. There is considerable…
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I study state dependence in social assistance receipt in Germany using annual survey data from the German Socio-Economic Panel for the years 1995–2011. There is considerable observed state dependence, with an average persistence rate in benefits of 68 per cent comparing to an average entry rate of just above 3 per cent. To identify a possible structural component, I estimate a series of dynamic random-effects probit models that control for observed and unobserved heterogeneity and endogeneity of initial conditions. I find evidence of substantial structural state dependence in benefit receipt. Estimates suggest that benefit receipt one year ago is associated with an increase in the likelihood of benefit receipt today by a factor of 3.4. This corresponds to an average partial effect of 13 percentage points. Average predicted entry and persistence rates and the absolute level of structural state dependence are higher in Eastern Germany than in Western Germany. I find only little evidence for time variation in state dependence around the years of the Hartz reforms.
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WILLIAM A. BARNETT, A. RONALD GALLANT, MELVIN J. HINICH, JOCHEN A. JUNGEILGES and DANIEL T. KAPLAN
Alessio Fusco and Nizamul Islam
This paper investigates the effect of household size, and in particular of the number of children of different age groups, on poverty, defined as being in a situation of low…
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This paper investigates the effect of household size, and in particular of the number of children of different age groups, on poverty, defined as being in a situation of low income. We apply various static and dynamic probit models to control for the endogeneity of the variables of interest and to account for unobserved heterogeneity, state dependence, and serially correlated error components. Using Luxembourg longitudinal data, we show that the number of children of different age groups significantly affects the probability of being poor. However, the magnitude of the effect varies across different specifications. In addition, we find strong evidence of true poverty persistency due to past experience, spurious poverty persistency due to individual heterogeneity, and transitory random shocks.
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Rania Hentati and Jean-Luc Prigent
Purpose – In this chapter, copula theory is used to model dependence structure between hedge fund returns series.Methodology/approach – Goodness-of-fit tests, based on the…
Abstract
Purpose – In this chapter, copula theory is used to model dependence structure between hedge fund returns series.
Methodology/approach – Goodness-of-fit tests, based on the Kendall's functions, are applied as selection criteria of the “best” copula. After estimating the parametric copula that best fits the used data, we apply previous results to construct the cumulative distribution functions of the equally weighted portfolios.
Findings – The empirical validation shows that copula clearly allows better estimation of portfolio returns including hedge funds. The three studied portfolios reject the assumption of multivariate normality of returns. The chosen structure is often of Student type when only indices are considered. In the case of portfolios composed by only hedge funds, the dependence structure is of Franck type.
Originality/value of the chapter – Introducing goodness-of-fit bootstrap method to validate the choice of the best structure of dependence is relevant for hedge fund portfolios. Copulas would be introduced to provide better estimations of performance measures.
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