This paper explores the shape of the Japanese money demand function in relation to the historical path of the Bank of Japan's policy rate by employing Saikkonen and Choi's [Econometric Theory 20, 301–340 (2004)] cointegrating smooth transition model. The nonlinear model provides a unified econometric framework, not only for pursuing the time profile of interest elasticity, but also to test the linearity of the Japanese money demand function. The test results for the linearity of the Japanese money demand function provide evidence of nonlinearity with a semilog model and linearity with a double-log model. Using a nonlinear semilog model, the analysis also finds that Japanese money demand comprises three regimes and that the interest semielasticity began to increase in the early 1990s when the Bank of Japan set the policy rate below 3%.

Previous research has found that the dynamic response of hours worked to a technology shock crucially depends on whether the hours variable is assumed to be an I(0) or an I(1) variable ex ante. In this paper we employ a multivariate fractionally integrated model that allows us to simultaneously estimate the order of integration of hours worked and its dynamic response to a technology shock. Our evidence lends support to the hypothesis that hours fall in response to a positive technology shock.

Business cycle models with sticky prices and endogenous firm entry make novel predictions on the transmission of shocks through the extensive margin of investment. I test some of these predictions using a VAR with model-based sign restrictions. I find a positive and significant response of firm entry to expansionary shocks to productivity, aggregate spending, monetary policy, and entry costs. The estimated response to a monetary expansion does not support the monetary policy transmission mechanism proposed by the model. Insofar as firm startups require labor services, wage stickiness is needed to make the signs of the model responses consistent with the estimated ones. The shapes of the empirical responses suggest that congestion effects in entry make it harder for new firms to survive when the number of startups rises.

In this paper, we explore the consequence of learning to forecast in a very simple environment. Agents have bounded memory and incorrectly believe that there is nonlinear structure underlying the aggregate time series dynamics. Under social learning with finite memory, agents may be unable to learn the true structure of the economy and rather may chase spurious trends, destabilizing the actual aggregate dynamics. We explore the degree to which agents' forecasts are drawn toward a minimal state variable learning equilibrium as well as a weaker long-run consistency condition.

The objective of this paper is to develop closed-form formulae for the exact discretization of a third-order system of stochastic differential equations, with fixed initial conditions, driven by observable stochastic trends and white noise innovations. The model provides a realistic alternative to first- and second-order differential equation specifications of the time lag distribution, forming the basis of a testing and estimation procedure. The exact discrete models, derived under two sampling schemes with either stock or flow variables, are put into a system error correction form that preserves the information of the underlying continuous time model regarding the order of integration and the dimension of cointegration space.

We generalize existing fair wage models to allow effort to vary over the business cycle. When effort is variable, wage fluctuations are partially compensated for by endogenous effort fluctuations, so that the sensitivity of marginal cost to output and employment variations is decreased. This new mechanism decreases the need for sluggishness to explain the observed high inflation persistence.

Empirical literature provided convincing evidence that explicit (i.e., legislated) inflation targets anchor expectations. I propose a novel game theoretic framework with generalized timing that allows us to formally capture this beneficial anchoring effect. Using the framework I identify several factors that influence whether and how strongly expectations are anchored, namely (i) the public's cost of decision making, (ii) the public's inflation aversion, (iii) the slope of the Phillips curve, (iv) the magnitude of supply shocks, (v) the degree of central bank conservatism, and under many (but not all) circumstances, (vi) the explicitness of the inflation target.