3,946 research outputs found
Financial Structure and Corporate Growth: Evidence from Italian Panel Data
We study the relationships between firm financial structure and growth for a large sample of Italian firms (1998-2003). We expand upon existing analyses testing whether liquidity constraints affect firm performance by considering among growth determinants also firm debt structure. Panel regression analyses show that more liquid firms tend to grow more. However, firms do not use their capital to expand, but rather to increase debt. We also find that firm growth is highly fragile as it is positively correlated with non-financial liabilities and it is not sustained by a long-term debt maturity. Finally, quantile regressions suggest that fast-growing firms are characterized by higher growth/cash-flow sensitivities and heavily rely on external debt, but seem to be less bank-backed than the rest of the sample. Overall, our findings suggest that the link between firmsâ investment and expansion decisions is far more complicated than postulated by standard tests of investment/cash-flow sensitivities.Firm growth; Financial structure; Cash flow; Financial constraints; Gibrat law; Quantile regressions
Instrumental variables quantile regression for panel data with measurement errors
This paper develops an instrumental variables estimator for quantile regression in panel data with fixed effects. Asymptotic properties of the instrumental variables estimator are studied for large N and T when Na/T ! 0, for some a > 0. Wald and Kolmogorov-Smirnov type tests for general linear restrictions are developed. The estimator is applied to the problem of measurement errors in variables, which induces endogeneity and as a result bias in the model. We derive an approximation to the bias in the quantile regression fixed effects estimator in the presence of measurement error and show its connection to similar effects in standard least squares models. Monte Carlo simulations are conducted to evaluate the finite sample properties of the estimator in terms of bias and root mean squared error. Finally, the methods are applied to a model of firm investment. The results show interesting heterogeneity in the Tobinâs q and cash flow sensitivities of investment. In both cases, the sensitivities are monotonically increasing along the quantiles
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Computational Methods for Parameter Estimation in Climate Models
Intensive computational methods have been used by Earth scientists in a wide range of problems in data inversion and uncertainty quantification such as earthquake epicenter location and climate projections. To quantify the uncertainties resulting from a range of plausible model configurations it is necessary to estimate a multidimensional probability distribution. The computational cost of estimating these distributions for geoscience applications is impractical using traditional methods such as Metropolis/Gibbs algorithms as simulation costs limit the number of experiments that can be obtained reasonably. Several alternate sampling strategies have been proposed that could improve on the sampling efficiency including Multiple Very Fast Simulated Annealing (MVFSA) and Adaptive Metropolis algorithms. The performance of these proposed sampling strategies are evaluated with a surrogate climate model that is able to approximate the noise and response behavior of a realistic atmospheric general circulation model (AGCM). The surrogate model is fast enough that its evaluation can be embedded in these Monte Carlo algorithms. We show that adaptive methods can be superior to MVFSA to approximate the known posterior distribution with fewer forward evaluations. However the adaptive methods can also be limited by inadequate sample mixing. The Single Component and Delayed Rejection Adaptive Metropolis algorithms were found to resolve these limitations, although challenges remain to approximating multi-modal distributions. The results show that these advanced methods of statistical inference can provide practical solutions to the climate model calibration problem and challenges in quantifying climate projection uncertainties. The computational methods would also be useful to problems outside climate prediction, particularly those where sampling is limited by availability of computational resources.National Science Foundation OCE-0415251CONACyT-Mexico 159764Institute for Geophysic
The price elasticity of electricity demand in South Australia
In this paper, the price elasticity of electricity demand, representing the sensitivity of customer demand to the price of electricity, has been estimated for South Australia. We first undertake a review of the scholarly literature regarding electricity price elasticity for different regions and systems. Then we perform an empirical evaluation of the historic South Australian price elasticity, focussing on the relationship between price and demand quantiles at each half-hour of the day. This work attempts to determine whether there is any variation in price sensitivity with the time of day or quantile, and to estimate the form of any relationship that might exist in South Australia.Electricity demand; Price elasticity
On the maximum bias functions of MM-estimates and constrained M-estimates of regression
We derive the maximum bias functions of the MM-estimates and the constrained
M-estimates or CM-estimates of regression and compare them to the maximum bias
functions of the S-estimates and the -estimates of regression. In these
comparisons, the CM-estimates tend to exhibit the most favorable
bias-robustness properties. Also, under the Gaussian model, it is shown how one
can construct a CM-estimate which has a smaller maximum bias function than a
given S-estimate, that is, the resulting CM-estimate dominates the S-estimate
in terms of maxbias and, at the same time, is considerably more efficient.Comment: Published at http://dx.doi.org/10.1214/009053606000000975 in the
Annals of Statistics (http://www.imstat.org/aos/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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