Estimate for A. Cook for marsh lands main drain for the month of Sept. Also included is a list of labourers’ time for the month of September for A. Cook and Rose Osborne

Estimate for A. Cook for marsh lands main drain for the month of Sept. Also included is a list of labourers’ time for the month of September for A. Cook and Rose Osborne. This is signed by Fred Holmes, Sept. 26, 1857.

County of Welland estimate of work done on the main drain of the marsh lands by Alexander Cook

County of Welland estimate of work done on the main drain of the marsh lands by Alexander Cook, unsigned. Estimate no.26, Oct., 1857.

Estimate for A. Cook for marsh lands main drain for the month of Sept. Also included is a list of labourers’ time for the month of October for A. Cook’s men and Rose Osborne

Estimate for A. Cook for marsh lands main drain for the month of Sept. Also included is a list of labourers’ time for the month of October for A. Cook’s men and Rose Osborne. This is signed by Fred Holmes, Oct. 27, 1857.

County of Welland estimate of work done on the main drain of the marsh lands by Alexander Cook

County of Welland estimate of work done on the main drain of the marsh lands by Alexander Cook, unsigned. Estimate no.27, Nov., 1857.

County of Welland final estimate of work done on the main drain of the marsh lands by Alexander Cook

County of Welland final estimate of work done on the main drain of the marsh lands by Alexander Cook complete with notes and calculations of quantities. This document is unsigned. Estimate no.28, Dec., 1857.

Estimate from the County of Welland to S.D. Woodruff for engineering services in marsh lands drainage for 1 year

Estimate from the County of Welland to S.D. Woodruff for engineering services in marsh lands drainage for 1 year, Dec. 1857.

Letter to S.D. Woodruff from an illegible signature stating that he suspects a clerical error in one of the columns, n.d.

Letter to S.D. Woodruff from an illegible signature stating that he suspects a clerical error in one of the columns, n.d.

Chart of final estimate of work done on section no.10, locks 24, 25 and 26 by Sharp and Quinn

Chart of final estimate of work done on section no.10, locks 24, 25 and 26 by Sharp and Quinn, contractors commenced Nov. 1843 and was finished May 1845.

Chart of final estimate of work done between Port Dalhousie and lock no.2 by Robert Jobson

Chart of final estimate of work done between Port Dalhousie and lock no.2 by Robert Jobson, contractor. The work commenced Nov. 1846 and was finished April 1847 on sections A and B, July 1847.

Chart of land drainage for the Welland Canal final estimate of work done on sections no.1, 2 and 3

Chart of land drainage for the Welland Canal final estimate of work done on sections no.1, 2 and 3 on the road below lock no. 2 leading to Port Dalhousie. Work commenced Nov. 1846 and finished July 1847. Road work and the waste weir no.1 to Port Dalhousie work commenced Aug. 1847 and finished Sept. 1847, Nov.1, 1847.

Estimate of dredging prices

Estimate of dredging prices, July 14, 1854.

Esta vida no es la vida o : el gran error del siglo XIX

UANL

Testing Mean-Variance Efficiency in CAPM with Possibly Non-Gaussian Errors : An Exact Simulation-Based Approach

In this paper we propose exact likelihood-based mean-variance efficiency tests of the market portfolio in the context of Capital Asset Pricing Model (CAPM), allowing for a wide class of error distributions which include normality as a special case. These tests are developed in the frame-work of multivariate linear regressions (MLR). It is well known however that despite their simple statistical structure, standard asymptotically justified MLR-based tests are unreliable. In financial econometrics, exact tests have been proposed for a few specific hypotheses [Jobson and Korkie (Journal of Financial Economics, 1982), MacKinlay (Journal of Financial Economics, 1987), Gib-bons, Ross and Shanken (Econometrica, 1989), Zhou (Journal of Finance 1993)], most of which depend on normality. For the gaussian model, our tests correspond to Gibbons, Ross and Shanken’s mean-variance efficiency tests. In non-gaussian contexts, we reconsider mean-variance efficiency tests allowing for multivariate Student-t and gaussian mixture errors. Our framework allows to cast more evidence on whether the normality assumption is too restrictive when testing the CAPM. We also propose exact multivariate diagnostic checks (including tests for multivariate GARCH and mul-tivariate generalization of the well known variance ratio tests) and goodness of fit tests as well as a set estimate for the intervening nuisance parameters. Our results [over five-year subperiods] show the following: (i) multivariate normality is rejected in most subperiods, (ii) residual checks reveal no significant departures from the multivariate i.i.d. assumption, and (iii) mean-variance efficiency tests of the market portfolio is not rejected as frequently once it is allowed for the possibility of non-normal errors.

Exact Skewness-Kurtosis Tests for Multivariate Normality and Goodness-of-fit in Multivariate Regressions with Application to Asset Pricing Models

We study the problem of testing the error distribution in a multivariate linear regression (MLR) model. The tests are functions of appropriately standardized multivariate least squares residuals whose distribution is invariant to the unknown cross-equation error covariance matrix. Empirical multivariate skewness and kurtosis criteria are then compared to simulation-based estimate of their expected value under the hypothesized distribution. Special cases considered include testing multivariate normal, Student t; normal mixtures and stable error models. In the Gaussian case, finite-sample versions of the standard multivariate skewness and kurtosis tests are derived. To do this, we exploit simple, double and multi-stage Monte Carlo test methods. For non-Gaussian distribution families involving nuisance parameters, confidence sets are derived for the the nuisance parameters and the error distribution. The procedures considered are evaluated in a small simulation experi-ment. Finally, the tests are applied to an asset pricing model with observable risk-free rates, using monthly returns on New York Stock Exchange (NYSE) portfolios over five-year subperiods from 1926-1995.

Methods to Estimate Dynamic Stochastic General Equilibrium Models

This paper employs the one-sector Real Business Cycle model as a testing ground for four different procedures to estimate Dynamic Stochastic General Equilibrium (DSGE) models. The procedures are: 1 ) Maximum Likelihood, with and without measurement errors and incorporating Bayesian priors, 2) Generalized Method of Moments, 3) Simulated Method of Moments, and 4) Indirect Inference. Monte Carlo analysis indicates that all procedures deliver reasonably good estimates under the null hypothesis. However, there are substantial differences in statistical and computational efficiency in the small samples currently available to estimate DSGE models. GMM and SMM appear to be more robust to misspecification than the alternative procedures. The implications of the stochastic singularity of DSGE models for each estimation method are fully discussed.