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This thesis presents three papers within the field of dynamic macroeconomics. The first paper, entitled “Medium-frequency cycles and the remarkable near trend-stationarity of output”, presents a dynamic stochastic general equilibrium model with endogenous growth, capable of reconciling the observed large medium-frequency fluctuations in output, with its long run (near) trend-stationarity. This requires a model in which standard business cycle shocks lead to highly persistent movements around trend, without significantly altering the trend itself. The robustness of the trend also requires that scale effects are eliminated both in the long and short runs. In an estimated version of the model, a financial-type shock to the stock of ideas emerges as the key driver of the medium frequency cycle. The second paper, entitled “Learning from learners”, is an intervention into two long running debates: the first, on whether learnability may be used to rule out explosive paths for inflation in New Keynesian models, and the second, into whether Taylor rule parameters may be identified from observing the data. We find that in an economy populated with traditional macroeconomic learners, Taylor rule parameters can always be identified by sophisticated econometric techniques. Furthermore, when all agents in the economy use such sophisticated techniques, stationary sunspot solutions are readily learnable, and there is no guarantee of convergence to a stationary solution even in the “determinate” case. This implies that learnability cannot be used for equilibrium selection. Finally, in the third paper, “Efficient simulation of DSGE models with inequality constraints” (joint with Michael Paetz), we present a new algorithm for the simulation of models subject to inequality constraints, such as the zero lower bound on nominal interest rates. Our algorithm is shown to deliver higher accuracy than all other non-global algorithms, and leading speed. We go on to provide a number of applications of our algorithm.

The hippocampus is a medial temporal lobe structure implicated both in consolidation of experience into long-term memory and generation of epileptiform discharges. Information processing in the hippocampus occurs through interactions between glutamatergic granule cells and pyramidal neurons, and a smaller number of GABAergic inhibitory interneurons. Excitatory connections onto interneurons are a relatively poorly characterised class of synapse, yet they have a central role in mediating the recruitment of inhibitory drive within the hippocampus. This thesis describes investigation of modulation of excitatory synaptic transmission to interneurons in area CA1 of the hippocampus by metabotropic glutamate receptors (mGluRs). mGluRs are G protein-coupled heptahelical transmembrane receptors, which exert powerful modulatory effects upon synaptic transmission and neuronal excitability. Use of patch clamp electrophysiology in acute brain slices allowed synaptic responses elicited by stimulation of afferent inputs to be recorded in single neurons within a functionally intact network. The selective group I mGluR agonist (S)-3,5-dihydroxyphenylglycine (DHPG) was found to acutely depress glutamatergic transmission to stratum radiatum interneurons in the rat hippocampal CA1 subfield. Both mGluRI and mGluR5 subtypes contributed to this phenomenon. DHPG-evoked depression was consistently accompanied by an elevation in paired-pulse ratio, implying a presynaptic mechanism of expression. However, it was also attenuated by blocking G protein and Ca2+ signalling within the postsynaptic neuron, arguing for a postsynaptic site of induction. The DHPG-evoked depression was unaffected by antagonists of GABAB and CB1 endocannabinoid receptors but was occluded when presynaptic P/Q-type Ca2+ channels were blocked. A heterosynaptic depression was observed in a test pathway when a high-frequency tetanus was delivered to an independent conditioning pathway. This depression was reversible and abolished by group I mGluR antagonists. Group I mGluRs thus provide a mechanism for population activity in glutamatergic synapses to influence synaptic excitation of cortical interneurons.