14,007 research outputs found
Panel Smooth Transition Regression Models
We develop a non-dynamic panel smooth transition regression model with fixed individual effects. The model is useful for describing heterogenous panels, with regression coefficients that vary across individuals and over time. Heterogeneity is allowed for by assuming that these coefficients are continuous functions of an observable variable through a bounded function of this variable and fluctuate between a limited number (often two) of âextreme regimesâ. The model can be viewed as a generalization of the threshold panel model of Hansen (1999). We extend the modelling strategy for univariate smooth transition regression models to the panel context. This comprises of model specification based on homogeneity tests, parameter estimation, and diagnostic checking, including tests for parameter constancy and no remaining nonlinearity. The new model is applied to describe firms' investment decisions in the presence of capital market imperfections.financial constraints; heterogeneous panel; invesatment; misspecification test; nonlinear modelling panel data; smooth transition model
Panel Smooth Transition Regression Models
We develop a non-dynamic panel smooth transition regression model with fixed individual effects. The model is useful for describing heterogenous panels, with regression coefficients that vary across individuals and over time. Heterogeneity is allowed for by assuming that these coefficients are continuous functions of an observable variable through a bounded function of this variable and fluctuate between a limited number (often two) of âextreme regimesâ. The model can be viewed as a generalization of the threshold panel model of Hansen (1999). We extend the modelling strategy for univariate smooth transition regression models to the panel context. This comprises of model specification based on homogeneity tests, parameter estimation, and diagnostic checking, including tests for parameter constancy and no remaining nonlinearity. The new model is applied to describe firmsâ investment decisions in the presence of capital market imperfections.financial constraints; heterogenous panel; investment; misspecification test; nonlinear modelling panel data; smooth transition models
Performance Bounds for Parameter Estimation under Misspecified Models: Fundamental findings and applications
Inferring information from a set of acquired data is the main objective of
any signal processing (SP) method. In particular, the common problem of
estimating the value of a vector of parameters from a set of noisy measurements
is at the core of a plethora of scientific and technological advances in the
last decades; for example, wireless communications, radar and sonar,
biomedicine, image processing, and seismology, just to name a few. Developing
an estimation algorithm often begins by assuming a statistical model for the
measured data, i.e. a probability density function (pdf) which if correct,
fully characterizes the behaviour of the collected data/measurements.
Experience with real data, however, often exposes the limitations of any
assumed data model since modelling errors at some level are always present.
Consequently, the true data model and the model assumed to derive the
estimation algorithm could differ. When this happens, the model is said to be
mismatched or misspecified. Therefore, understanding the possible performance
loss or regret that an estimation algorithm could experience under model
misspecification is of crucial importance for any SP practitioner. Further,
understanding the limits on the performance of any estimator subject to model
misspecification is of practical interest. Motivated by the widespread and
practical need to assess the performance of a mismatched estimator, the goal of
this paper is to help to bring attention to the main theoretical findings on
estimation theory, and in particular on lower bounds under model
misspecification, that have been published in the statistical and econometrical
literature in the last fifty years. Secondly, some applications are discussed
to illustrate the broad range of areas and problems to which this framework
extends, and consequently the numerous opportunities available for SP
researchers.Comment: To appear in the IEEE Signal Processing Magazin
Point estimation with exponentially tilted empirical likelihood
Parameters defined via general estimating equations (GEE) can be estimated by
maximizing the empirical likelihood (EL). Newey and Smith [Econometrica 72
(2004) 219--255] have recently shown that this EL estimator exhibits desirable
higher-order asymptotic properties, namely, that its bias is small
and that bias-corrected EL is higher-order efficient. Although EL possesses
these properties when the model is correctly specified, this paper shows that,
in the presence of model misspecification, EL may cease to be root n convergent
when the functions defining the moment conditions are unbounded (even when
their expectations are bounded). In contrast, the related exponential tilting
(ET) estimator avoids this problem. This paper shows that the ET and EL
estimators can be naturally combined to yield an estimator called exponentially
tilted empirical likelihood (ETEL) exhibiting the same bias and the
same variance as EL, while maintaining root n convergence under
model misspecification.Comment: Published at http://dx.doi.org/10.1214/009053606000001208 in the
Annals of Statistics (http://www.imstat.org/aos/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Aversion to ambiguity and model misspecification in dynamic stochastic environments
Preferences that accommodate aversion to subjective uncertainty and its potential misspecification in dynamic settings are a valuable tool of analysis in many disciplines. By generalizing previous analyses, we propose a tractable approach to incorporating broadly conceived responses to uncertainty. We illustrate our approach on some stylized stochastic environments. By design, these discrete time environments have revealing continuous time limits. Drawing on these illustrations, we construct recursive representations of intertemporal preferences that allow for penalized and smooth ambiguity aversion to subjective uncertainty. These recursive representations imply continuous time limiting HamiltonâJacobiâBellman equations for solving control problems in the presence of uncertainty.Published versio
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