Additional Sources of Bias in Half-Life Estimation

Department of Economics, Mail Code 4515, Southern Illinois University, Carbondale, IL 62901, USA
Computational Statistics & Data Analysis (Impact Factor: 1.4). 12/2006; 51(3):2056-2064. DOI: 10.1016/j.csda.2005.12.016
Source: RePEc


When the automobile was developed near the beginning of the last century, it was the relatively new fuel gasoline, not the familiar ethanol that became the fuel of choice. We examine the intersections of the early development of the automobile and the petroleum industry and consider the state of the agriculture sector during the same period. Through this process, we find a series of influences, such as relative prices and alternative markets, that help to explain how in the early years of automobile development, gasoline won out over the equally likely technical alternative ethanol. We also examine the industrial relations in the automobile industry that seem to have influenced the later adoption of leaded gasoline, rather than ethanol, as a solution to the problem of engine knock.

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Available from: Sung Ahn, Oct 13, 2015
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    • "In the case of a simple AR(1), the half-life of a shock can be estimated using the formula − ln(2)/ ln(δ), where δ indicates the value of the autoregressive coefficient. However, Goldberg and Verboven [42], in footnote 11, observe that in case of more complicated processes, such as a higher order AR(p) process or an ARMA(p, q) process, the previous formula is no longer valid, and thus impulse response functions should be preferred (see also Seong et al. [43]). "
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    ABSTRACT: This paper provides evidence that unemployment rates across US states are stationary and therefore behave according to the natural rate hypothesis. We provide new insights by considering the effect of key variables on the speed of adjustment associated with unemployment shocks. A highly-dimensional VAR analysis of the half-lives associated with shocks to unemployment rates in pairs of states suggests that the distance between states and vacancy rates respectively exert a positive and negative influence. We find that higher homeownership rates do not lead to higher half-lives. When the symmetry assumption is relaxed through quantile regression, support for the Oswald hypothesis through a positive relationship between homeownership rates and half-lives is found at the higher quantiles.
    Physica A: Statistical Mechanics and its Applications 11/2013; 392(22):5711-5722. DOI:10.1016/j.physa.2013.07.023 · 1.73 Impact Factor
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    • ") and others that applying the same formula in the presence of higher order autoregressive processes above AR(1) may lead to bias in the half-life approximation. We therefore follow Seong et al. (2006) and modify our calculations accordingly. If , we may define the concept of varied RIP because stationary behavior is confirmed across the entire study period, but the autoregressive coefficients and speeds of adjustment towards long-run equilibrium are different. "
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