Volume 18, Issue 3

This paper contributes to the literature on changes in the transmission mechanism of monetary policy by introducing a model whose parameter evolution explicitly depends on the stance of monetary policy. The model, a structural break endogenous threshold VAR, also captures changes in the variance of shocks, and allows for a break in the parameters at an estimated time. We show that the transmission is asymmetric depending on the extention of the deviation of the actual policy rate from the one required by the Taylor rule. When the policy stance is tight – actual rate is higher than the one implied by the Taylor rule – contractionary shocks have stronger negative effects on output and prices.

The paper examines whether the effect of inequality on growth varies with the level of economic development. Using a comprehensive panel of annual data for the 48 contiguous US states over the period 1945–2004, we find overwhelming evidence in support of threshold effects in the relationship between inequality and growth. Our analysis shows that while the effect of inequality on growth is significantly negative at lower levels of development, this effect diminishes along the growth process and then turns significantly positive at higher levels of development. Quantitatively, the coefficient estimates imply that when real income per capita is below the threshold of $12,140 (2004 US dollar), a one standard deviation increase in the share of income held by the top 1% of the population reduces the growth rate of real per capita income by 0.6479 percentage points. In contrast, when income per capita is above the threshold of $21,065 (2004 US dollar), a one standard deviation increase in the top percentile income share raises the rate of growth of real per capita income by 0.2561 percentage points.

This paper provides the first estimation strategy for the Wishart Affine Stochastic Correlation (WASC) model. We provide elements showing that the use of empirical characteristic function-based estimates is advisable as this function is exponential affine in the WASC case. We use a GMM estimation strategy with a continuum of moment conditions based on the characteristic function. We present the estimation results obtained using a dataset of equity indexes. The WASC model captures most of the known stylized facts associated with financial markets, including leverage and asymmetric correlation effects.

Bils and Kahn [Bils, M., and J. A. Kahn. 2000. “What Inventory Behavior Tells us about Business Cycles.” American Economic Review 90, 458–481.] conjectured that a competitive technology-driven business cycle model could not generate countercyclical inventory-sales ratios. Khan and Thomas [Khan, A., and J. Thomas. 2007a. “Explaining Inventories: A Business Cycle Assessment of the Stockout Avoidance and (s,s) Motives.” Macroeconomic Dynamics 11(5): 638–664. Khan, A., and J. K. Thomas. 2007b. “Inventories and the Business Cycle: An Equilibrium Analysis of (s,s) Policies.” American Economic Review 94(4): 1165–1188.] developed a model that disproved this conjecture. However, as this paper shows, that model underperforms a baseline model without inventories for many important moments. However, when variable utilization is added to the model, many of these moments perform better in the full model than in the baseline. The results suggest important interactions between variable utilization and inventory dynamics.

Macroeconomic forecasting using factor models estimated by principal components has become a popular research topic with many both theoretical and applied contributions in the literature. In this paper we attempt to address an often neglected issue in these models: The problem of outliers in the data. Most papers take an ad-hoc approach to this problem and simply screen datasets prior to estimation and remove anomalous observations. We investigate whether forecasting performance can be improved by using the original unscreened dataset and replacing principal components with a robust alternative. We propose to use an estimator based on least absolute deviations (LAD) as this alternative and establish a tractable method for computing the estimator. In addition to this we demonstrate the robustness features of the estimator through a number of Monte Carlo simulation studies. Finally, we apply the estimator in a simulated real-time forecasting exercise to test its merits. We use a newly compiled dataset of US macroeconomic series spanning the period 1971:2–2012:10. Our findings suggest that the chosen treatment of outliers does affect forecasting performance and that in many cases improvements can be made using a robust estimator such as the proposed LAD estimator.

This paper analyzes the performance of multiple steps estimators of vector autoregressive multivariate conditional correlation GARCH models by means of Monte Carlo experiments. We show that if innovations are Gaussian, estimating the parameters in multiple steps is a reasonable alternative to the maximization of the full likelihood function. Our results also suggest that for the sample sizes usually encountered in financial econometrics, the differences between the volatility and correlation estimates obtained with the more efficient estimator and the multiple steps estimators are negligible. However, when innovations are distributed as a Student-t, using multiple steps estimators might not be a good idea.