In this paper, we investigate the problem of spectrum sharing where a macro base station (MBS) is underlaid with multiple femto base stations (FBSs). This problem is investigated from a game theoretic perspective where two games are herein investigated. First, in the non-cooperative case, the MBS and FBSs (i.e., players) behave selfishly aiming at improving their respective payoffs (achievable rate), whereas in the second case a hierarchy is introduced among players as a means to improve the overall network efficiency. This problem is cast as a hierarchical game with a leader-follower approach in which the MBS is designated as the leader whereas FBSs are the followers. Furthermore, in the case of incomplete game information, a learning mechanism based on information exchange among players is investigated in which the leader builds its own estimate of other players's strategies and strategically adapt its decisions to maximize its expected utility. Numerical results corroborate the fact that the hierarchical model outperforms the non-cooperative approach in terms of achievable rate and optimal number of deployed small cells (or femtocells) in the network. Additionally, learning mechanisms based on public information exchange are shown to outperform the private information case as well as the selfish/myopic case.