How well are Australian infants and children aged 4 to 5 years doing? Findings from the Longitudinal Study of Australian Children Wave 1

This report presents an analysis of the data from the first wave of the Longitudinal Study of Australian Children (LSAC) to explore the wellbeing of 5,107 children in the infant cohort of the study and the 4,983 children, aged 4 to 5 years, in the child cohort. Wave 1 of LSAC includes measures of multiple aspects of children’s early development. These developmental measures are summarised in the LSAC Outcome Index, a composite measure which includes an overall index as well as three separate domain scores, tapping physical development, social and emotional functioning, and learning and cognitive development.

An empirical investigation of the level effect in Australian interest rates

An extensive literature examines the dynamics of interest rates, with particular attention given to the positive relationship between interest-rate volatility and the level of interest rates—the so-called level effect. This paper examines the interaction between the estimated level effect and competing parameterisations of interest-rate volatility for the Australian yield curve. We adopt a new methodology that estimates elasticity in a multivariate setting that explicitly accommodates the correlations that exist between various yield factors. Results show that significant correlations exist between the residuals of yield factors and that such correlations do indeed impact on model estimates. Within the multivariate setting, the level of the short rate is shown to be a crucial determinant of the conditional volatility of all three yield factors. Measures of model fit suggest that, in addition to the usual level effect, the incorporation of GARCH effects and possible regime shifts is important

Evaluating Specification Tests for Markov-Switching Time-Series Models

We evaluate the performance of several specification tests for Markov regime-switching time-series models. We consider the Lagrange multiplier (LM) and dynamic specification tests of Hamilton (1996) and Ljung–Box tests based on both the generalized residual and a standard-normal residual constructed using the Rosenblatt transformation. The size and power of the tests are studied using Monte Carlo experiments. We find that the LM tests have the best size and power properties. The Ljung–Box tests exhibit slight size distortions, though tests based on the Rosenblatt transformation perform better than the generalized residual-based tests. The tests exhibit impressive power to detect both autocorrelation and autoregressive conditional heteroscedasticity (ARCH). The tests are illustrated with a Markov-switching generalized ARCH (GARCH) model fitted to the US dollar–British pound exchange rate, with the finding that both autocorrelation and GARCH effects are needed to adequately fit the data.

Isopropylaminium 2-carboxy-4, 5-dichlorobenzoate

In the structure of the 1:1 proton-transfer compound of isopropylamine with 4,5-dichlorophthalic acid, C3H10N+·C8H3Cl2O4-, the three cation H-atom donors associate with three separate carboxyl O-atom anion acceptors, giving conjoint cyclic R44(12), R44(16) hydrogen-bonding cation-anion interactions in a one-dimensional ribbon structure. In the anions, the carboxyl groups lie slightly out of the plane of the benzene ring [maximum deviations = 0.439 (1) for a carboxylic acid O atom and 0.433 (1) Å for a carboxylate O atom]. However, the syn-related proton of the carboxylic acid group forms the common short intramolecular O-HOcarboxyl hydrogen bond.

Ethane-1,2-diaminium 4,5-dichlorophthalate

In the structure of the title compound, C2H10N22+·C8H2Cl2O42-, the dications and dianions form hydrogen-bonded ribbon substructures which enclose conjoint cyclic R21(7), R12(7) and R42(8) associations and extend down the c-axis direction. These ribbons inter-associate down b, giving a two-dimensional sheet structure. In the dianions, one of the carboxylate groups is essentially coplanar with the benzene ring, while the other is normal to it [C-C-C-O torsion angles = 177.67 (12) and 81.94 (17)°, respectively].

The distribution of the sample minimum-variance frontier

In this paper, we present a finite sample analysis of the sample minimum-variance frontier under the assumption that the returns are independent and multivariate normally distributed. We show that the sample minimum-variance frontier is a highly biased estimator of the population frontier, and we propose an improved estimator of the population frontier. In addition, we provide the exact distribution of the out-of-sample mean and variance of sample minimum-variance portfolios. This allows us to understand the impact of estimation error on the performance of in-sample optimal portfolios. Key Words: minimum-variance frontier; efficiency set constants; finite sample distribution