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We compare local and global polynomial solution methods for DSGE models with Epstein- Zin-Weil utility. We show that model implications for macroeconomic quantities are relatively invariant to choice of solution method but that a global method can yield substantial improvements for asset prices and welfare costs. The divergence in solution quality is highly dependent on parameters which affect value function sensitivity to TFP volatility, as well as the magnitude of TFP volatility itself. This problem is pronounced for calibrations at the extreme of those accepted in the asset pricing literature and disappears for more traditional macroeconomic parameterizations.

The Federal Reserve target rate has a lower bound. Changes to the target rate occur with discrete increments. Using out-of-sample forecasts of the target rate, we evaluate models incorporating these two realistic non-linear features. Incorporating these features mitigates in-sample over-fitting and improves out-of-sample forecast accuracy of the target rate level and volatility. A model with these features performs better relative to the linear models because (i) it produces stronger responses of forecasts to inflation and unemployment and a weaker response to lagged target rate, and because (ii) it yields very different forecast distributions when the target rate is close to the lower bound.

In dealing with a panel of seasonal data with cross-section dependence, this paper establishes a common factor model to investigate whether the seasonal and non-seasonal non-stationarity in a series is pervasive, or specific, or both. Without knowing a priori whether the data are seasonal stationary or not, we propose a procedure for consistently estimating the model; thus, the seasonal non-stationarity of common factors and idiosyncratic errors can be separately detected accordingly. We evaluate the methodology in a series of Monte Carlo simulations and apply it to test for non-stationarity and to disentangle their sources in panels of worldwide real exchange rates and of consumer price indexes for 37 advanced economies.

This paper illustrates how outliers can affect both the estimation and testing of leverage effect by focusing on the TGARCH model. Three estimation methods are compared through Monte Carlo experiments: Gaussian Quasi-Maximum Likelihood, Quasi-Maximum Likelihood based on the Student-t likelihood and Least Absolute Deviation method. The empirical behavior of the t-ratio and the Likelihood Ratio tests for the significance of the leverage parameter is also analyzed. Our results put forward the unreliability of Gaussian Quasi-Maximum Likelihood methods in the presence of outliers. In particular, we show that one isolated outlier could hide true leverage effect whereas two consecutive outliers bias the estimated leverage coefficient in a direction that crucially depends on the sign of the first outlier and could lead to wrongly reject the null of no leverage effect or to estimate asymmetries of the wrong sign. By contrast, we highlight the good performance of the robust estimators in the presence of one isolated outlier. However, when there are patches of outliers, our findings suggest that the sizes and powers of the tests as well as the estimated parameters based on robust methods may still be distorted in some cases. We illustrate these results with two series of daily returns.

In light of several economic and financial crises and institutional changes experienced by the European countries, we examine whether these economies achieved synchronization of their business cycles and fostered synchronization of their growth rates. Controlling for reverse causality, we conduct multiple endogenous break tests and find that (i) several endogenous break dates correspond to idiosyncratic shocks affecting individual countries or major shocks in international arena but not the adoption of the euro; this result suggests that the convergence process has been nonlinear for a number of countries and that studies imposing break dates exogenously, such as the launch of euro, may lead to biased conclusions; (ii) while output growth was increasingly synchronized for some countries, integration occurred in an asymmetric way and it did not change or did not occur for others despite being in the same common currency area (iii) convergence has been prevalent among the non-Eurozone economies in our sample.

This paper shows how stock market volatility regimes affect the cross-section of stock returns along quality and liquidity dimensions. We find that, during crisis periods, low quality and low liquidity stocks experience relatively higher losses than predicted in normal times, while high quality and high liquidity stocks experience rather relatively lower losses. These findings lend strong support to the presence of cross-market and within-market flight-to-quality and to-liquidity episodes during crisis periods. During low volatility periods, however, low quality and low liquidity stocks earn relatively larger returns, while high quality and high liquidity stocks yield lower returns; suggesting that low volatility conditions benefit junk and illiquid stocks but not quality and liquid stocks. Finally, our results reveal that liquidity level dominates liquidity beta in explaining stock returns across the different market volatility regimes.