In this study, we estimate the macroeconomics instability index over the period . Using the definition of Fischer and Bleaney, the study calculates the "macroeconomics instability index". In order to compute the macroeconomics instability index, four sub-indices of inflation rate, the fractional ratio of budget to the gross national product, the ratio of foreign debt to the gross national product, and the ratio of the free exchange rate to the official exchange rate as the determinant variables of the macroeconomics instability are considered. Then, the study estimates the equations for long-term processes for each variable and determines the deviations from the real values. We also obtain the time series for the macroeconomics instability index, using the calculated simple mean of the variables' deviations and discuss the results.

In this paper, we perform an empirical study to investigate the impact of economical stability on the amount of investment coming from the private sector. We calculate macroeconomics instability index (MII) using the existing methods in the literature. We have also used Glezakos (1973) method [Glezakos,C.(1973). Export instability and economic growth: A statistical verification. Economic Development and Cultural Change, 21(3), 670-678.], which considers long-term deviation of real values as instability index. Therefore, we use four variables of inflation rate (TINF), the ratio of budget deficit on growth domestic product (GDP) (TBD), foreign debt on GDP (TFD) and the ratio of actual currency rate on nominate currency (TRO). The preliminary results show that the short-term changes on logarithm of investment from private sector (LNIP) with one lag and logarithm of value added (LNIV) have positive impact on LNIP. In addition, any short term changes on logarithm of MII (LNMII) has negative and meaningful impact on LNIP and approximately 0.67 percent of difference between the actual and long term are discounted in each period. The results indicate that instability index has negative effect even in short term on Iran's industry. This shows the relevant importance of instability on economy.

Background & Objectives One of the popular studies in medical sciences for finding risk factors and the reason of the disease, are casecontrol studies that the important index we can calculate is odds ratio. but some confounders which effect on response variable challenge the OR's validity and present OR more or less than the real value. One way of omitting the effect of confounder is designing matched studies .Logistic regression is one of the general methods for modeling these studies. This article compares 3 logistic regression models: independence, marginal and conditional. Materials & Methods: This study has been conducted on correlated simulated data. Thus the data is simulated from bivariate normal distribution with the correlation coefficients (0, 0.2, 0.4, 0.6, and 0.8). Then with changing cutoff points at (0.05, 0.25), (0.25, 0.1), (0.25, 0.15), (0.25, 0.25) for their c.d.f ,we convert continues distribution to categorical binary distribution which data are related together. Then 3 logistic regression model in independence, marginal and conditional version fit to data and calculate OR. With 10000 times iteration, we compare 2.5 and 97.5 percentiles values and the median OR percentile value at the above cutoff points for all their models. Results: When the correlation is zero, all three models have the same quantity for OR and also changing in point have the same coefficient. But with the increasing correlation between the observations, OR between marginal model and independence model is not different. But its value will vary with the conditional model. For example, the cutoff point (0.25,0.1) and when the correlation is 0.6, median of OR that obtained in marginal model and independence model is 2.8, but in conditional model this quantity is 5 and it is twice of fitted value. Conclusion: When the correlation between observations is high, using of conditional model is more correctly method and with increasing this correlation, our error rate by using independence or marginal model rises. But when correlation between observations is negligible, using of the three models gives similar estimates.