This paper analyzes three quarterly investment models for the detection of certain specifi- cation errors. The models are those of Anderson (1 and 2), Eisner (4), and Meyer-Glauber (10). The models are applied to thirteen manufacturing industries. A set of specification error tests developed by Ramsey (12, 13, and 14) are applied to the above models so as to detect the specification errors of omission of variables, incorrect functional form, simul- taneous equation problems, and heteroskedasticity. The models are ranked in order of the number of times they failed to be rejected by the specification error tests and the rank scheme is compared to that found in a previous study by Jorgenson, Hunter, and Nadiri (6), where more conventional criteria are used for ranking the models. industries, making use of both quarterly and annual data. Accelerator models and their variations (flexible accelerator models) as well as models considering internal and external finance are common in the estimation of the investment function. The lag structure between investment and its determinants and the manner in which replacement or the depreciation of capital is accounted for has also evoked the interest of researchers.2 From a perusal of the literature it is apparent that we face almost as many possible models for investment behavior as there are researchers. The problem at hand then is to come closer to a single general investment model from the numerous possibilities suggested. To do this we must investigate models of investment which differ both in terms of the determinants of investment as well as their lag structure so as to cover the broad range of specifications suggested. In a recent study, Jorgenson, Hunter, and Nadiri (6) (hereafter JHN) investi- gated various investment functions for several manufacturing industries using deflated, seasonally adjusted, quarterly data. JHN chose the best model based on the following criteria: (i) comparison of a given investment function with an auto- regressive scheme with regard to goodness to fit; (ii) comparison of a given invest- ment function with a model regressing investment on past anticipated investment expenditures; (iii) R2; (iv) estimates of the standard error of the fitted regression residuals corrected for degrees of freedom; and (v) Durbin-Watson ratio. The last three criteria mentioned above (and especially the third and fourth) are often the standard techniques employed by researchers in selecting a model specifica- tion.3

The effects of seat-belt regulations on automobile-related fatality and injury rates have been of great interest to economists and policy-makers over the past few years.' The effects of the laws have been evaluated by various statistical techniques using timeseries data for particular states and pooled time-series data for national models.2 The results of these studies provide some evidence that seat-belt laws (SBL) reduce injury and fatality rates. However, the effects of seat-belt laws vary across states and time periods as well as across the levels of injuries. This study assesses the effects of the Texas seat-belt law on injury rates using policereported accident data. The data are from the U.S. Department of Transportation State Traffic Accident Files and are compiled monthly for the period 1982-1987 for driver-involved accidents. Furthermore, the data comprise singleand multiple-vehicle accidents. Only accidents involving towed vehicles are used in the analysis so as to normalize for changes in accident-reporting thresholds over time.3 The analysis was conducted for several sets of injury classifications using the KABCO scale, which indicates the numbers of fatalities (K), severe injuries (A), moderate injuries (B), complaints of injuries (C), and no injuries (0).