The term structure of interest rates is closely related to the macro economy, in that it contains information about the current state of the economy and reflects the bond market participants' expectations regarding the economy's future path. It can be effectively used to predict major economic variables, including inflation, and therefore has potential as an indicator variable in the establishment of monetary policy. Due to its economic importance, many researchers in the field of finance and macroeconomics have studied the modeling of the term structure of interest rates. In finance, the focus of such research has been the prediction of interest rates and the pricing of interest rate-related derivatives. In macroeconomics, researchers have mainly attempted to understand the changes in the term structure of interest rates in the context of monetary policy implementation. Since the pioneering work of Ang and Piazzesi (2003), however, macro-finance term structure models combining the advantages of the two traditional approaches have provided a useful means of interpreting the movements of the term structure in the economic context while maintaining the consistency between short- and long-term interest rates with the no-arbitrage restrictions. In this study, we apply the unspanned macro-finance term structure model (unspanned MTSM) to analyze the empirical relationship between the term structure of interest rates and the macroeconomic variables using Korean data. An ‘unspanned' model is free from the unrealistic assumption that the macroeconomic variables can be entirely generated by the first Nth-principal components of the term structure (macro-spanning restriction). It is also more flexible in the sense that the model permits feedback between the term structure and macroeconomic variables. The unspanned MTSM incorporated in this paper was suggested in Joslin, Priebsch, and Singleton (2012) and extended to the multi-lag form in Joslin, Le, and Singleton (2013). In the empirical section, we first conduct the macro-spanning test to determine the validity of the empirical analysis using the unspanned MTSM. The test results show that the term structure factors only explain some of the variations in the real economy (R2 = 13.43%) and the price variables (R2 = 15.40%), confirming the validity of modeling ‘unspanned' macro variables. Empirically, we identify the macroeconomic variables with the highest explanatory power over the term structure of the interest rate in Korea. We estimate the model with the three term structure factors (level, slope, and curvature) and two combinations of the 82 key macroeconomic variables (i.e. 82C2 = 3,321 models are estimated). The performance of each combination of macroeconomic variables is measured by the various information criteria (AIC, SIC, and HQIC). In our analysis, the combination of the ‘nonfarm payrolls' in the real economy variable group and ‘consumer price index excluding agricultural products and oils' in the inflation variable group provides the best performance in explaining the Korean term structure of interest rates. The robustness of these variables is maintained even when we add more macroeconomic variables (i.e. combining the three macroeconomic variables) to the macro-finance term structure model. In the three macroeconomic variable cases, the third additional variable with a high explanatory power is ‘credit spreads', the spread between AAA-rated and BBB+-rated corporate bond spread. We believe that the ‘nonfarm payrolls' has the highest explanatory power, as noted by Wu and Zhang (2008) in the US case, because this variable carries information about not only the real economy growth, but also inflation. Then, using the identified three macroeconomic variables, we estimate the unspanned MTSM and analyze the effect that macroeconomic factors have on the market price of risk. Due to the ambiguity of the economic meaning of ‘nonfarm payrolls', we use the ‘gross domestic product'—the second-best performing real economy variable—in the estimation. The results of this analysis show that the inflation variables have a pro-cyclical effect on the market price of the slope factor while the real economy variables have a pro-cyclical (counter-cyclical) effect on the market price of the level (curvature) factor. However, the third macroeconomic variable—credit spreads—has a negligible effect on the bond risk premium. Finally, we conduct the impulse-response and variance decomposition analysis to show the empirical relationship between the three term structure factors and the three identified macroeconomic variables. We observe that a positive shock on the real economy variables increases the level factor, and as the credit spread widens, the level factor falls. A positive shock on the inflation variable also increases the slope factor, which widens the spread between short- and long-term yields. Finally, through variance decomposition, we confirm that the three macroeconomic variables explain 91% of the variation in the level factor, and 91.42% and 95.39% of the slope and curvature factors in the 36-month forecasting period.