ENMonetary policy conducted by the Eurosystem, as well as micro- and macroprudential regulation implemented by the Bank of Lithuania have a significant impact on credit market developments in Lithuania. In order to ensure effectiveness of policy interventions it is necessary to gain a deeper understanding about the workings of the local credit market, its empirical regularities, relationship between credit and the broader economy, and key drivers behind credit market activity. The current paper is aimed at adressing some of these issues. To this end, we develop a suite of simple empirical models: four vector error-correction models (VECMs) allow us to analyse dynamic and cointegrating relationships between credit and various macroeconomic variables, a disequilibrium model sheds light on credit supply and demand conditions in Lithuania over the last decade, wheareas a panel regression model explains bank lending activity with the help of both macroeconomic and bank-specific variables. VECM results suggest that the Lithuanian credit market activity is quite closely linked to housing prices and the overall level of economic activity. In contrast, the role of nominal interest rates in determining credit activity over the financial cycle was found to be quite modest: during the credit boom, gradually rising interest rates had little dampening effect on credit market activity, whereas in the post-crisis period extremely favourable interest environment did not promptly ignite credit market buoyancy. One of the more important drivers behind strong credit growth during the better half of the previous decade was a low initial price level (also linked to low initial wage and indebtedness levels), which created strong potential for nominal convergence and credit market deepening. VECM analysis also provides interesting insights on the role of credit in endogenous relationships with other economic variables.There are indications that plentiful and cheap credit might lead to increases in house prices, whereas the positive impact of rising house prices on credit is less pronouned. Likewise, credit fluctuations do a much better job in explaining variation in bank deposits than vice versa. Positive credit shocks also lead to postitive changes in investment, corporate earnings and overall economic activity but credit developments can explain only a relatively small part of the general variation in these variables. Strong credit growth is a precursor to a rise in economic imbalances, for instance, in the form of current account deficits. Credit supply and demand disequilibrium model reveals that credit supply in Lithuania positively depends on the rates of return on capital, banks‘ net borrowing from abroad and housing prices and negatively depends on interest rate margins. Also, prior to the financial crisis credit supply was positively related to the level of capital in the banking system but it was no longer the case after the crisis when the system as a whole became very well capitalised. Credit demand is positively related to real economic activity and investment and negatively linked to housing prices and real interest rates. The panel regression model yields quite similar results. Notably, the model does not show significant differences in the lending activitity of domestically and foreign-owned banks, except that the latter can effectively boost their lending activity by borrowing from their parent institutions.