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This paper examines the cyclical implications of the efficiency wage model for the labor market and inflation dynamics in a New Keynesian framework with search frictions. Shapiro and Stiglitz's (1984) efficiency wage framework is incorporated into the otherwise ordinary Nash-bargaining wage determination, thereby generating downward real wage rigidity over business cycles. Introducing the efficiency wage scheme enables the model to replicate the asymmetric dynamics of real activity indicators, especially labor market quantities, observed in the data; the model exhibits a significantly left-skewed distribution for employment, vacancy, and real output. Furthermore, real wage rigidity induced by the efficiency wage scheme can address Shimer's (2005) volatility puzzle and explain the observed weak cyclicality of real wages.

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We evaluate out-of-sample forecasting performance of different prediction models using different estimation windows to account for a variety of statistical characteristics such as the long range dependence and the structural breaks of the process. We identify the timing of the deterministic shifts in the unconditional variance and evaluate the impact of accounting for the level shifts in the unconditional variance on out-of-sample volatility forecasting. The modified iterated cumulative sums of squares algorithm identifies two shifts in the unconditional variance for the KOSPI (Korea Composite Stock Price Index) returns. For the KOSPI returns process, the full sample performance of the recursive GARCH(1,1) model is worse than the competing models, which is unsurprising given two structural breaks in the process. The superiority of the competing models in forecasting performance can be attributed to the capability of the model which accommodates both the long range dependence by giving a slow hyperbolic rate of decaying weights on the past observations in forming the likelihood and the structural changes in the variance by discarding observations beyond a rolling window length distance in the past which may have come from a different regime. Although we try to improve the forecasting performance by incorporating statistical characteristics of the process into a prediction model, the out-of-sample performance of the prediction model can be tainted with uncertainties related to statistical tests and estimation methodologies.

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The system GMM estimator in dynamic panel data models which combines two sets of moment conditions, i.e., for the differenced equation and for the model in levels, is known to be more efficient than the first-difference GMM estimator. However, an initial optimal weight matrix is not known for the system estimation procedure. Therefore, we suggest the use of `a suboptimal weight matrix' which may reduce the finite sample bias whilst increasing its asymptotic efficiency. Our Monte Carlo experiments show that the small sample properties of the suboptimal system estimator are much more reliable than any other conventional system GMM estimator in terms of bias.

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In this paper, heterogeneous expectations of households about future economic growth take place because of different forecasts of future economic growth made by different public agencies. In an economy with only riskless real bonds, households with pessimistic beliefs tend to accumulate more wealth than those with correct beliefs about future economic growth. To the extent which the equilibrium interest rate rises with the outstanding amount of the public debt, this result implies that fiscal policy can have impact on the distribution of consumption and wealth of individual households. In particular, this distributional impact of fiscal policy depends on the structure of financial market when households are heterogeneous in their beliefs.