1,658 research outputs found

    Risk and Return: Consumption versus Market Beta

    Get PDF
    The interaction between the macroeconomy and asset markets is central to a variety of modern theories of the business cycle. Much recentwork emphasizes the joint nature of the consumption decision and the portfolio allocation decision. In this paper, we compare two formulations of the Capital Asset Pricing Model. The traditional CAPM suggests that the appropriate measure of an asset's risk is the covariance of the asset's return with the market return. The consumption CAPM, on the other hand, implies that a better measure of risk is the covariance with aggregate consumption growth. We examine a cross section of 464 stocks and find that the beta measured with respect to a stock market index outperforms the beta measured with respect to consumption growth.

    News or Noise? An Analysis of GNP Revisions

    Get PDF
    This paper studies the nature of the errors in preliminary GNP data, It first documents that these errors are large. For example, suppose the prelimimary estimate indicates that real GNP did not change over the recent quarter; then one can be only 80 percent confident that the final estimate (annual rate) will be in the range from -2.8 percent to +2.8 percent. The paper also documents that the revisions in GNP data are not forecastable, This finding implies that the preliminary estimates are the efficient given available information. Hence, the Bureau of Economic Analysis appears to follow efficient statistical procedures, in making its preliminary estimates.

    Trends, Random Walks, and Tests of the Permanent Income Hypothesis

    Get PDF
    Recent studies find that consumption is excessively sensitive to income. These studies assume that income is stationary around a deterministic trend. The data, however, do not reject the hypothesis that disposable income is a random walk with drift. If income is indeed a random walk, then the standard testing procedure is greatly biased toward finding excess sensitivity. Moreover, if income is borderline stationary, this procedure is also seriously biased

    Do We Reject Too Often? Small Sample Bias in Tests of Rational Expectations

    Get PDF
    We examine the small sample properties of tests of rational expectations models. We show using Monte Carlo experiments that these tests can be extremely biased toward rejection for sample sizes typical in applied research. These biases are important when the time series examined are highly autoregressive. We also show that these tests are even more biased with detrended data. We present correct small sample critical values for our canonical problem

    Risk and Return: Consumption Beta Versus Market Beta

    Get PDF
    Much recent work emphasizes the joint nature of the consumption decision and the portfolio allocation decision. In this paper, we compare two formulations of the Capital Asset Pricing Model. The traditional CAPM suggests that the appropriate measure of an asset’s risk is the covariance of the asset’s return with the market return. The consumption CAPM, on the other hand, implies that a better measure of risk is the covariance with aggregate consumption growth. We examine a cross-section of 464 stocks and find that the beta measured with respect to a stock market index outperforms the beta measured with respect to consumption growth

    Do We Reject Too Often? Small Sample Properties of Tests of Rational Expectations Models

    Get PDF
    We examine the small sample properties of tests of rational expectations models. We show using Monte Carlo experiments that the asymptotic distribution of test statistics can be extremely misleading when the tine series examined are highly autoregressive. In particular, a practitioner relying on the asymptotic distribution will reject true models too frequently. We also show that this problem is especially severe with detrended data. We present correct small sample critical values for our canonical problem.

    Revealing hidden scenes by photon-efficient occlusion-based opportunistic active imaging

    Full text link
    The ability to see around corners, i.e., recover details of a hidden scene from its reflections in the surrounding environment, is of considerable interest in a wide range of applications. However, the diffuse nature of light reflected from typical surfaces leads to mixing of spatial information in the collected light, precluding useful scene reconstruction. Here, we employ a computational imaging technique that opportunistically exploits the presence of occluding objects, which obstruct probe-light propagation in the hidden scene, to undo the mixing and greatly improve scene recovery. Importantly, our technique obviates the need for the ultrafast time-of-flight measurements employed by most previous approaches to hidden-scene imaging. Moreover, it does so in a photon-efficient manner based on an accurate forward model and a computational algorithm that, together, respect the physics of three-bounce light propagation and single-photon detection. Using our methodology, we demonstrate reconstruction of hidden-surface reflectivity patterns in a meter-scale environment from non-time-resolved measurements. Ultimately, our technique represents an instance of a rich and promising new imaging modality with important potential implications for imaging science.Comment: Related theory in arXiv:1711.0629
    corecore