5,818 research outputs found
Nonparametric IV estimation of shape-invariant Engel curves
This paper concerns the identification and estimation of a shape-invariant Engel
curve system with endogenous total expenditure. The shape-invariant specification
involves a common shift parameter for each demographic group in a pooled
system of Engel curves. Our focus is on the identification and estimation of both
the nonparametric shape of the Engel curve and the parametric specification of the
demographic scaling parameters. We present a new identification condition, closely
related to the concept of bounded completeness in statistics. The estimation procedure
applies the sieve minimum distance estimation of conditional moment restrictions
allowing for endogeneity. We establish a new root mean squared convergence
rate for the nonparametric IV regression when the endogenous regressor has unbounded
support. Root-n asymptotic normality and semiparametric efficiency of
the parametric components are also given under a set of ‘low-level’ sufficient conditions.
Monte Carlo simulations shed lights on the choice of smoothing parameters
and demonstrate that the sieve IV estimator performs well. An application is made
to the estimation of Engel curves using the UK Family Expenditure Survey and
shows the importance of adjusting for endogeneity in terms of both the curvature
and demographic parameters of systems of Engel curves
Semi-nonparametric IV estimation of shape-invariant Engel curves
This paper studies a shape-invariant Engel curve system with endogenous total expenditure, in which the shape-invariant specification involves a common shift parameter for each demographic group in a pooled system of nonparametric Engel curves. We focus on the identification and estimation of both the nonparametric shapes of the Engel curves and the parametric specification of the demographic scaling parameters. The identification condition relates to the bounded completeness and the estimation procedure applies the sieve minimum distance estimation of conditional moment restrictions, allowing for endogeneity. We establish a new root mean squared convergence rate for the nonparametric instrumental variable regression when the endogenous regressor could have unbounded support. Root-n asymptotic normality and semiparametric efficiency of the parametric components are also given under a set of "low-level" sufficient conditions. Our empirical application using the U.K. Family Expenditure Survey shows the importance of adjusting for endogeneity in terms of both the nonparametric curvatures and the demographic parameters of systems of Engel curves
Non-parametric detection and estimation of structural change
SUMMARY: We propose a semi-non-parametric approach to the estimation and testing of structural change in time series regression models. Under the null of a given set of the coefficients being constant, we develop estimators of both the time-varying (non-parametric) and constant (parametric) components. Given the estimators under null and alternative, generalized F and Wald tests are developed. The asymptotic distributions of the estimators and test statistics are derived. A simulation study examines the finite-sample performance of the estimators and tests. The techniques are employed in the analysis of structural change in the US productivity and the Eurodollar term structure
Testing conditional factor models
Using nonparametric techniques, we develop a methodology for estimating and testing conditional alphas and betas and long-run alphas and betas, which are the averages of conditional alphas and betas, respectively, across time. The estimators and tests can be implemented for a single asset or jointly across portfolios. The traditional Gibbons, Ross, and Shanken (1989) test arises as a special case of no time variation in the alphas and factor loadings and homoskedasticity. As applications of the methodology, we estimate conditional CAPM and multifactor models on book-to-market and momentum decile portfolios. We reject the null that long-run alphas are equal to zero even though there is substantial variation in the conditional factor loadings of these portfolios
Estimation of dynamic models with nonparametric simulated maximum likelihood
We propose an easy-to-implement simulated maximum likelihood estimator for dynamic models where no closed-form representation of the likelihood function is available. Our method can handle any simulable model without latent dynamics. Using simulated observations, we nonparametrically estimate the unknown density by kernel methods, and then construct a likelihood function that can be maximized. We prove that this nonparametric simulated maximum likelihood (NPSML) estimator is consistent and asymptotically efficient. The higher-order impact of simulations and kernel smoothing on the resulting estimator is also analyzed; in particular, it is shown that the NPSML does not suffer from the usual curse of dimensionality associated with kernel estimators. A simulation study shows good performance of the method when employed in the estimation of jump diffusion models
Modelling diverse root density dynamics and deep nitrogen uptake — a simple approach
We present a 2-D model for simulation of root density and plant nitrogen (N) uptake for crops grown in agricultural systems, based on a modification of the root density equation originally proposed by Gerwitz and Page in J Appl Ecol 11:773–781, (1974). A root system form parameter was introduced to describe the distribution of root length vertically and horizontally in the soil profile. The form parameter can vary from 0 where root density is evenly distributed through the soil profile, to 8 where practically all roots are found near the surface. The root model has other components describing root features, such as specific root length and plant N uptake kinetics. The same approach is used to distribute root length horizontally, allowing simulation of root growth and plant N uptake in row crops. The rooting depth penetration rate and depth distribution of root density were found to be the most important parameters controlling crop N uptake from deeper soil layers. The validity of the root distribution model was tested with field data for white cabbage, red beet, and leek. The model was able to simulate very different root distributions, but it was not able to simulate increasing root density with depth as seen in the experimental results for white cabbage. The model was able to simulate N depletion in different soil layers in two field studies. One included vegetable crops with very different rooting depths and the other compared effects of spring wheat and winter wheat. In both experiments variation in spring soil N availability and depth distribution was varied by the use of cover crops. This shows the model sensitivity to the form parameter value and the ability of the model to reproduce N depletion in soil layers. This work shows that the relatively simple root model developed, driven by degree days and simulated crop growth, can be used to simulate crop soil N uptake and depletion appropriately in low N input crop production systems, with a requirement of few measured parameters
Identification of a Class of Index Models: A Topological Approach
We establish nonparametric identification in a class of so-called index models by
using a novel approach that relies on general topological results. Our proof strategy requires
substantially weaker conditions on the functions and distributions characterising the model
than those required by existing strategies; in particular, it does not require any large-support
conditions on the regressors of our model. We apply the general identification result to additive
random utility and competing risk model
Development and critical evaluation of a generic 2-D agro-hydrological model (SMCR_N) for the responses of crop yield and nitrogen composition to nitrogen fertilizer
Models play an important role in optimizing fertilizer use in agriculture to maintain sustainable crop production and to minimize the risk to the environment. In this study, we present a new Simulation Model for Crop Response to Nitrogen fertilizer (SMCR_N). The SMCR_N model, based on the recently developed model EU-Rotate_N for the N-economies of a wide range of crops and cropping systems, includes new modules for the estimation of N in the roots and an associated treatment of the recovery of soil mineral N by crops, for the reduction of growth rates by excessive fertilizer-N, and for the N mineralization from soil organic matter. The validity of the model was tested against the results from 32 multi-level fertilizer experiments on 16 different crop species. For this exercise none of the coefficients or parameters in the model was adjusted to improve the agreement between measurement and simulation. Over the practical range of fertilizer-N levels model predictions were, with few exceptions, in good agreement with measurements of crop dry weight (excluding fibrous roots) and its %N. The model considered that the entire reduction of soil inorganic N during growth was due to the sum of nitrate leaching, retention of N in fibrous roots and N uptake by the rest of the plant. The good agreement between the measured and simulated uptakes suggests that in this arable soil, losses of N from other soil processes were small. At high levels of fertilizer-N yields were dominated by the negative osmotic effect of fertilizer-N and model predictions for some crops were poor. However, the predictions were significantly improved by using a different value for the coefficient defining the osmotic effect for saline sensitive crops. The developed model SMCR_N uses generally readily available inputs, and is more mechanistic than most agronomic models and thus has the potential to be used as a tool for optimizing fertilizer practice
ALMA CO J=6-5 observations of IRAS16293-2422: Shocks and entrainment
Observations of higher-excited transitions of abundant molecules such as CO
are important for determining where energy in the form of shocks is fed back
into the parental envelope of forming stars. The nearby prototypical and
protobinary low-mass hot core, IRAS16293-2422 (I16293) is ideal for such a
study. The source was targeted with ALMA for science verification purposes in
band 9, which includes CO J=6-5 (E_up/k_B ~ 116 K), at an unprecedented spatial
resolution (~0.2", 25 AU). I16293 itself is composed of two sources, A and B,
with a projected distance of 5". CO J=6-5 emission is detected throughout the
region, particularly in small, arcsecond-sized hotspots, where the outflow
interacts with the envelope. The observations only recover a fraction of the
emission in the line wings when compared to data from single-dish telescopes,
with a higher fraction of emission recovered at higher velocities. The very
high angular resolution of these new data reveal that a bow shock from source A
coincides, in the plane of the sky, with the position of source B. Source B, on
the other hand, does not show current outflow activity. In this region, outflow
entrainment takes place over large spatial scales, >~ 100 AU, and in small
discrete knots. This unique dataset shows that the combination of a
high-temperature tracer (e.g., CO J=6-5) and very high angular resolution
observations is crucial for interpreting the structure of the warm inner
environment of low-mass protostars.Comment: Accepted for publication in A&A Letter
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