In line with Basel Ⅱ capital guidelines financial institutions have developed internal credit rating systems to mainly assess credit risk. Meanwhile, ever since introduced in Ohlson(1980)'s seminal paper, conditional default probability models, such as logit and probit, have been universally adopted as a standard credit risk model while neglecting significant theoretical progresses that have been made in this field in the recent years. Further, for nearly half a century since Altman(1968) the older model has been also actively utilized in evaluating firms' financial information as a way to gauge the default risk. Meanwhile, credit theories claim that credit risk is likely to be influenced by macroeconomic environments. In fact, a large body of literature has provided evidence that credit risk is closely related to the state of an economy. If it is really so, then we should take into account salient macroeconomic characteristics as the determinants of credit risk in order to accurately evaluate systematic risk. However, it is difficult for a static default probability model based merely on cross-sectional accounting information to fully account for credit risk dynamics affected by volatile business cycles. Moreover, there has been no consensus established on what financial ratios should be benchmarked in making default prediction in spite of empirical evidences. For these reasons, this paper empirically examines how macroeconomic characteristics as well as firm-specific factors are being used to evaluate credit risk through random effect probit model. To do this, we looked into an unbalanced panel data set of small and medium-sized enterprises(henceforth, SMEs) for a 10 year period of 1997 to 2006. In addition, it is important to note that previous research has not fully delved into the effect of firm age on credit risk while credit risk is known to be largely dependent on firm duration. In this regard, the default probability model can be useful in identifying default and non-default firms as well as the factors contributing to firm failures; however, such framework does not reveal much information about the timing of a default. To improve on these potential short comings, the Cox proportional hazard model and the Kaplan-Meier survival analysis were performed to demonstrate whether credit risk would actually depend on firm duration. However, the Cox model is still vulnerable to the censored data problem. Thus, Cox regression was applied to start- up firms' sub-samples from 2000 to 2006. The accounting dataset is obtained by Korea Credit Guarantee Fund and Korea Enterprise Data Co., Ltd. The main results of empirical analysis can be summarized as follows: First, utilizing quarterly data for the period between 1990Q1 and 2008Q4 we estimated cross-correlation coefficients, which is to shed light on the relation between macroeconomic variables and loan default. Loan default is proxied by the default amount of loans guaranteed by Korea Credit Guarantee Fund. We found out that loan interest rates, government bond yields, and term structures rather than inflation and unemployment rates are closely related to loan default. Second, most financial data pertaining to idiosyncratic risk factors appear to be statistically significant in both probit and Cox regressions, including: sales growth; cash ratio calculated as cash and cash equivalents divided by total assets less cash and cash equivalents; profitability ratio defined as EBIT divided by total assets; and solvency ratio proxied by equity capital as a percentage of total assets. They are, in fact negatively related to default probability even after controlling for firm size and industry as well as time effects. This result suggests that companies with healthier financial status are less likely to go default on their debts. Third, some of macroeconomic factors which are often blamed for being drivers of systematic risk fail to properly function in the assessment of default probability for SMEs. Nevertheless, empirical results revealed that models with variables representing financial market conditions, such as government bond yields, loan interest rates, KOSPI, and foreign exchange rates, are more significant factors for estimating default probability than other macroeconomic factors such as real GDP and inflation proxied by growth rate of consumer price index. The results using lagged macroeconomic variables are fairly similar to the former. In short, macroeconomic characteristics appear to be salient in assessing macroeconomic condition but probit models with macroeconomic variables are inferior to those with time dummy variables as compared with pseudo R-squared. Fourth, we find that there exists a negative relationship between credit risk for SMEs and their firm ages in Korea. The Kaplan-Meier analysis shows that a hazard rate steeply increases over time during the first 4 years of a firm life. Afterwards, hazard rate decreases, resulting in a hump‐shaped curve with time. Through the survival analysis we could also find out that a non-linear relationship exists between hazard rate and firm age. This evidence supports the empirical results of Bonfim(2009). Finally, the larger firm size is or/and the more collateral increases, the less credit risk increases. Firm size is defined as the logarithm of total assets and collateral as the share of tangible assets on firms' total assets. Empirical results from the Cox model show that profitability ratios and collaterals are more useful than macroeconomic factors on default probability measurement of start-up firms. Of other factors, government bond yield is positively correlated with credit risk in start-up SMEs. From these findings, it may be safer for us to conclude that both macroeconomic characteristics and firm- specific factors play important roles in estimating default probabilities for SMEs. It is worthwhile to note that our study resulting from an extensive accounting data set of more than 21,000 SMEs minimizes the likelihood of sample selection bias which is one of the drawbacks of prior default prediction models.