Towards a new Keynesian theory of the price level

Modifying the standard New-Keynesian model to replace firms’ full information and sticky prices with flexible prices and dispersed information, and imposing mild and plausible restrictions on the monetary authority’s decision rule, produces the striking results that (i) there exists a unique and globally stable steady-state rate of inflation, despite the possibility of a lower bound on nominal interest rates; and (ii) in the vicinity of steady-state, the price level is determinate (and not just the rate of inflation), despite the central bank targeting inflation. The specification of firms’ signal extraction problem under dispersed information removes the need to make use of Blanchard-Kahn conditions to solve the model, thereby removing the need to adhere to the Taylor principle and consequently circumventing the critique of Cochrane (2011). The model admits a determinate, stable solution with no role for sunspot shocks when the monetary authority responds by less than one-for-one to changes in expected inflation, including under an interest rate peg. An extension to include incomplete information on the part of the central bank permits the consideration of (rational) errors of judgement on the part of policymakers and provides a theoretical basis for inertial policymaking without interest rate smoothing, in support of Rudebusch (2002, 2006)

Incomplete information and the idiosyncratic foundations of aggregate volatility

This thesis considers two interrelated themes: the emergence of aggregate volatility from idiosyncratic shocks and optimisation under incomplete information when, for reasons of strategic complementarity, agents are interested in both simple and weighted averages of their competitors' actions. I first develop a model of Bayesian social learning over a network. Unlike earlier literature that abandons one of the assumptions that agents (a) act repeatedly; (b) are rational; and (c) face strategic complementarities, I obtain tractability for arbitrarily large networks by also assuming that agents do not know the full structure of the network, but do know its link distribution. An AR(1) process for the underlying state induces an ARMA(1,1) process for the hierarchy of expectations, with current and lagged weighted averages of agents' idiosyncratic shocks entering at an aggregate level. For sufficiently irregular networks, these shocks do not wash out, thus causing persistent aggregate effects. I next apply this to firms' price-setting problem, demonstrating that even when firms possess complete price exibility, network learning induces considerable persistence in aggregate variables following monetary and real shocks and that network shocks plausibly represent a source of aggregate economic volatility. Finally, I explore price setting under monopolistic competition when facing TransLog preferences. I solve explicitly for a firm's best-response pricing rule under full information and show that in partial equilibrium under incomplete information, larer firms will focus on their marginal costs while smaller firms will place more weight on changes in consumer preferences and competitors' prices. In general equilibrium, I estimate the effect of two distinct sources of real rigidity that emerge from TransLog preferences: the well-known curvature in demand and the dramatic increase in complexity of firms' signal-extraction problems. With non-uniform preferences, the model represents another channel through which idiosyncratic shocks can cause aggregate volatility

Peering into the mist: social learning over an opaque observation network

I present a model of social learning over an exogenous, directed network that may be readily nested within broader macroeconomic models with dispersed information and combines the attributes that agents (a) act repeatedly and simultaneously; (b) are Bayes-rational; and (c) have strategic interaction in their decision rules. To overcome the challenges imposed by these requirements, I suppose that the network is opaque: agents do not know the full structure of the network, but do know the link distribution. I derive a specific law of motion for the hierarchy of aggregate expectations, which includes a role for network shocks (weighted sums of agents’ idiosyncratic shocks). The network causes agents’ beliefs to exhibit increased persistence, so that average expectations overshoot the truth following an aggregate shock. When the network is sufficiently (and plausibly) irregular, transitory idiosyncratic shocks cause persistent aggregate effects, even when agents are identically sized and do not trade