The Cyclical Behavior of Industrial Labor Markets: A Comparison of the Pre-War and Post-War Eras

This paper studies the cyclical behavior of a number of industrial labor markets of the pre-war (1923-1939) and post-war (1954-1982) eras. In the spirit of Burns and Mitchell we do not test a specific structural model of the labor market but instead concentrate on describing the qualitative features of the (monthly, industry-level) data.The two principal questions we ask are: First, how is labor input (as measured by the number of workers, the hours of work, and the intensity of utilization) varied over the cycle ? Second, what is the cyclical behaviorof labor compensation (as measured by real wages, product wages, and real weekly earnings) ? We study these questions in both the frequency domain and the time domain. Many of our findings simply reinforce, or perhaps refine, existing perceptions of cyclical labor market behavior. However, we do find some interesting differences between the pre-war and the post-war periods in ther elative use of layoffs and short hours in downturns, and in the cyclical behavior of the real wage.

Derivation of SPH equations in a moving referential coordinate system

The conventional SPH method uses kernel interpolation to derive the spatial semi-discretisation of the governing equations. These equations, derived using a straight application of the kernel interpolation method, are not used in practice. Instead the equations, commonly used in SPH codes, are heuristically modified to enforce symmetry and local conservation properties. This paper revisits the process of deriving these semi-discrete SPH equations. It is shown that by using the assumption of a moving referential coordinate system and moving control volume, instead of the fixed referential coordinate system and fixed control volume used in the conventional SPH method, a set of new semi- discrete equations can be rigorously derived. The new forms of semi-discrete equations are similar to the SPH equations used in practice. It is shown through numerical examples that the new rigorously derived equations give similar results to those obtained using the conventional SPH equations

Fourier domain diffuse correlation spectroscopy with heterodyne holographic detection

We present a new approach to diffuse correlation spectroscopy which overcomes the limited light throughput of single-mode photon counting techniques. Our system employs heterodyne holographic detection to allow parallel measurement of the power spectrum of a fluctuating electric field across thousands of modes, at the shot noise limit, using a conventional sCMOS camera. This yields an order of magnitude reduction in detector cost compared to conventional techniques, whilst also providing robustness to the effects of ambient light and an improved signal-to-noise ratio during in vitro experiments. We demonstrate a GPU-accelerated holographic demodulation system capable of processing the incoming data (79.4 M pixels per second) in real-time, and a novel Fourier domain model of diffuse correlation spectroscopy which permits the direct recovery of flow parameters from the measured data. Our detection and modelling strategy are rigorously validated by modulating the Brownian component of an optical tissue phantom, demonstrating absolute measurements of the Brownian diffusion coefficient in excellent agreement with conventional methods. We further demonstrate the feasibility of our system through in vivo measurement of pulsatile flow rates measured in the human forearm

Diffuse correlation spectroscopy in the Fourier domain with holographic camera-based detection

We present a new approach to Diffuse Correlation Spectroscopy (DCS) which overcomes the limited light throughput of single mode photon counting techniques, and operates with continuous wave illumination without disturbance from ambient light. Heterodyne holographic detection allows parallel measurement of the power spectrum of a fluctuating electric field across thousands of modes, from which we may directly compute flow parameters using a novel Fourier domain DCS model. Our detection and modelling strategy are rigorously validated by modulating the Brownian and flow components of an optical tissue phantom, demonstrating absolute measurements of the Brownian diffusion coefficient in excellent agreement with conventional methods. We demonstrate the feasability of in vivo measurement through the recovery of pulsatile flow rates measured in the human forearm

Simulation of time-integrated dynamic speckle patterns in biomedical optics

The simulation of statistically accurate 2D time-integrated dynamic speckle patterns using a model that accounts for spatially varying sample properties (such as scatterer motion and decorrelation time) is yet to be presented in biomedical optics. We propose a solution to this important problem based on the Karhunen-Loève expansion of the field autocorrelation function, and apply our method to the formalisms of both laser speckle contrast imaging and diffuse correlation spectroscopy

Identification and Estimation of 'Irregular' Correlated Random Coefficient Models

In this paper we study identification and estimation of a correlated random coefficients (CRC) panel data model. The outcome of interest varies linearly with a vector of endogenous regressors. The coefficients on these regressors are heterogenous across units and may covary with them. We consider the average partial effect (APE) of a small change in the regressor vector on the outcome (cf., Chamberlain, 1984; Wooldridge, 2005a). Chamberlain (1992) calculates the semiparametric efficiency bound for the APE in our model and proposes a √ N consistent estimator. Nonsingularity of the APEâs information bound, and hence the appropriateness of Chamberlainâs (1992) estimator, requires (i) the time dimension of the panel ( T) to strictly exceed the number of random coefficients ( p) and (ii) strong conditions on the time series properties of the regressor vector. We demonstrate irregular identification of the APE when T = p and for more persistent regressor processes. Our approach exploits the different identifying information in the subpopulations of âstayersâ â or units whose regressor values change little across periods â and âmoversâ â or units whose regressor values change substantially across periods. We propose a feasible estimator based on our identification result and characterize its large sample properties. While irregularity precludes our estimator from attaining parametric rates of convergence, it limiting distribution is normal and inference is straightforward to conduct. Standard software may be used to compute point estimates and standard errors. We use our methods to estimate the average elasticity of calorie consumption with respect to total outlay for a sample of poor Nicaraguan households.