Is spurious behaviour an issue for two independent stationary spatial autoregressive SAR(1) processes?
Spurious regression occurs when two independent stationary or nonstationary time series are found to be correlated. Spurious behaviour is also detected in spatial data. Using a Monte Carlo analysis, this study examines the spurious phenomenon for two independent stationary spatial autoregressive processes of order one, that is, SAR(1), and it finds that when spatial econometric models are estimated, as suggested by the LM specification tests, the spurious behaviour is not detected nor the presence of spatially autocorrelated errors.
Dealing with serially correlated errors in the context of spurious regression for two independent stationary AR(1) processes
ABSTRACT Serially correlated errors are most likely to appear in regression analysis when time-series data are used either as a true symptom of autocorrelation or as an indication of a false specification among variables. This study examines the problem of serially correlated errors in the context of spurious regression for two independent stationary AR(1) processes, showing evidence of removing the presence of both symptoms using a Monte Carlo analysis.
Silver–Gelatin Method for the Determination of Trace Amounts of Carbon Monoxide in Air
The use of silver–gelatin complex was found suitable for the determination of trace amounts of carbon monoxide in air. Silver–gelatin complex is reduced by carbon monoxide to give intensely colored sol. Measurement of the color intensity of the stable silver sol enables the determination of carbon monoxide successful under various environmental conditions. The detection limit is 2–100 ppm. The relative standard deviation for the determination of 10 ppm carbon monoxide is 2.6%.