962 resultados para Auctions Econometrics
Resumo:
This paper analyses the effect of unmet formal care needs on informal caregiving hours in Spain using the two wavesof the Informal Support Survey (1994, 2004). Testing for double sample selection from formal care receipt and theemergence of unmet needs provides evidence that the omission of either variable would causes underestimation of thenumber of informal caregiving hours. After controlling for these two factors the number of hours of care increaseswith both the degree of dependency and unmet needs. More importantly, in the presence of unmet needs, the numberof informal caregiving hours increases when some formal care is received. This result refutes the substitution modeland supports complementarity or task specificity between both types of care. For a given combination of formal careand unmet needs, informal caregiving hours increased between 1994 and 2004. Finally, in the model for 2004, theselection term associated with the unmet needs equation is larger than that of the formal care equation, suggestingthat using the number of formal care recipients as a quality indicator may be confounding, if we do not complete thisinformation with other quality indicators.
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It is proved the algebraic equality between Jennrich's (1970) asymptotic$X^2$ test for equality of correlation matrices, and a Wald test statisticderived from Neudecker and Wesselman's (1990) expression of theasymptoticvariance matrix of the sample correlation matrix.
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Asymptotic chi-squared test statistics for testing the equality ofmoment vectors are developed. The test statistics proposed aregeneralizedWald test statistics that specialize for different settings by inserting andappropriate asymptotic variance matrix of sample moments. Scaled teststatisticsare also considered for dealing with situations of non-iid sampling. Thespecializationwill be carried out for testing the equality of multinomial populations, andtheequality of variance and correlation matrices for both normal andnon-normaldata. When testing the equality of correlation matrices, a scaled versionofthe normal theory chi-squared statistic is proven to be an asymptoticallyexactchi-squared statistic in the case of elliptical data.
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Structural equation models are widely used in economic, socialand behavioral studies to analyze linear interrelationships amongvariables, some of which may be unobservable or subject to measurementerror. Alternative estimation methods that exploit different distributionalassumptions are now available. The present paper deals with issues ofasymptotic statistical inferences, such as the evaluation of standarderrors of estimates and chi--square goodness--of--fit statistics,in the general context of mean and covariance structures. The emphasisis on drawing correct statistical inferences regardless of thedistribution of the data and the method of estimation employed. A(distribution--free) consistent estimate of $\Gamma$, the matrix ofasymptotic variances of the vector of sample second--order moments,will be used to compute robust standard errors and a robust chi--squaregoodness--of--fit squares. Simple modifications of the usual estimateof $\Gamma$ will also permit correct inferences in the case of multi--stage complex samples. We will also discuss the conditions under which,regardless of the distribution of the data, one can rely on the usual(non--robust) inferential statistics. Finally, a multivariate regressionmodel with errors--in--variables will be used to illustrate, by meansof simulated data, various theoretical aspects of the paper.
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We study the effect of the business cycle on the health of newborn babies using 30 years of birth certificate data for Spain. Exploiting regional variation over time, we find that babies are born healthier when the local unemployment rate is high. Although fertility is lower during recessions, the effect on health is not the result of selection (healthier mothers being more likely to conceive when unemployment is high). We match multiple births to the same parents and find that the main result survives the inclusion of parents fixed-effects. We then explore a range of maternal behaviors as potential channels. Fertility-age women do not appear to engage in significantly healthier behaviors during recessions (in terms of exercise, nutrition, smoking and drinking). However, they are more likely to be out of work. Maternal employment during pregnancy is in turn negatively correlated with babies' health. We conclude that maternal employment is a plausible mediating channel.
Resumo:
An endogenous switching model of ex-ante wage changes under indexed and non-indexed settlements is estimated for the Spanish manufacturing sector using collective bargaining firm data for the 1984-1991 period. The likelihood of indexing the settlement is higher for nationwide unions than for other union groups within the works council and increases with the expected level of inflation. For wage change equations, a common structure for indexed and non-indexed settlements is strongly rejected, showing a source of nominal rigidity. For indexed contracts, the expected ex-ante total inflation coverage is nearly complete. It is also shown that workers pay a significant ex-ante wage change premium (differential) to obtain a cost of living allowance clause. However, the realised contingent compensation exceeds such a premium for all industries. Finally, important spillover effects in wage setting and the decision to index the settlement have been detected.
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This paper extends multivariate Granger causality to take into account the subspacesalong which Granger causality occurs as well as long run Granger causality. The propertiesof these new notions of Granger causality, along with the requisite restrictions, are derivedand extensively studied for a wide variety of time series processes including linear invertibleprocess and VARMA. Using the proposed extensions, the paper demonstrates that: (i) meanreversion in L2 is an instance of long run Granger non-causality, (ii) cointegration is a specialcase of long run Granger non-causality along a subspace, (iii) controllability is a special caseof Granger causality, and finally (iv) linear rational expectations entail (possibly testable)Granger causality restriction along subspaces.
Resumo:
In moment structure analysis with nonnormal data, asymptotic valid inferences require the computation of a consistent (under general distributional assumptions) estimate of the matrix $\Gamma$ of asymptotic variances of sample second--order moments. Such a consistent estimate involves the fourth--order sample moments of the data. In practice, the use of fourth--order moments leads to computational burden and lack of robustness against small samples. In this paper we show that, under certain assumptions, correct asymptotic inferences can be attained when $\Gamma$ is replaced by a matrix $\Omega$ that involves only the second--order moments of the data. The present paper extends to the context of multi--sample analysis of second--order moment structures, results derived in the context of (simple--sample) covariance structure analysis (Satorra and Bentler, 1990). The results apply to a variety of estimation methods and general type of statistics. An example involving a test of equality of means under covariance restrictions illustrates theoretical aspects of the paper.
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Two main approaches are commonly used to empirically evaluate linear factor pricingmodels: regression and SDF methods, with centred and uncentred versions of the latter.We show that unlike standard two-step or iterated GMM procedures, single-step estimatorssuch as continuously updated GMM yield numerically identical values for prices of risk,pricing errors, Jensen s alphas and overidentifying restrictions tests irrespective of the modelvalidity. Therefore, there is arguably a single approach regardless of the factors being tradedor not, or the use of excess or gross returns. We illustrate our results by revisiting Lustigand Verdelhan s (2007) empirical analysis of currency returns.
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Graphical displays which show inter--sample distances are importantfor the interpretation and presentation of multivariate data. Except whenthe displays are two--dimensional, however, they are often difficult tovisualize as a whole. A device, based on multidimensional unfolding, isdescribed for presenting some intrinsically high--dimensional displays infewer, usually two, dimensions. This goal is achieved by representing eachsample by a pair of points, say $R_i$ and $r_i$, so that a theoreticaldistance between the $i$-th and $j$-th samples is represented twice, onceby the distance between $R_i$ and $r_j$ and once by the distance between$R_j$ and $r_i$. Self--distances between $R_i$ and $r_i$ need not be zero.The mathematical conditions for unfolding to exhibit symmetry are established.Algorithms for finding approximate fits, not constrained to be symmetric,are discussed and some examples are given.