Band 68, Heft 2 - Special Issue: 11th Workshop on Macroeconomics & Business Cycles Research 20th/21st January 2017 at the ifo Institute, Dresden

Using newly available investment data from the World Bank’s World Development Indicators database we provide an update and an extension of the aggregate capital stock estimations provided in ( Berlemann , M. and J.-E. Wesselhöft (2014): Estimating Aggregate Capital Stocks Using the Perpetual Inventory Method: A Survey of Previous Implementations and New Empirical Evidence for 103 Countries, Review of Economics 65(1), 1–34). The new database contains comparable unbalanced panel data for 122 countries and the period of 1960 to 2016.

In light of the rising political and economic uncertainty in Europe, we aim to provide a basic understanding of the impact of policy and stock market uncertainty on a set of macroeconomic variables such as production and investment. In this paper, we apply a structural vector autoregressive (SVAR) model to gain first insights that may help to identify avenues for further research. We find that stock market volatility shows a fairly consistently negative effect. However, the implications of policy uncertainty for Europe and the euro area in particular are not so straightforward.

Abstract: This paper presents a theoretical analysis of the simulated impact of uncertainty in a New Keynesian model. In order to incorporate uncertainty, the basic three-equation framework is modified by higher-order approximation resulting in a non-linear (dynamic) IS curve. Using impulse response analyses to examine the behavior of the model after a cost shock, I find interest rates in the version with uncertainty to be lower in contrast to the case under certainty.

This paper introduces an economically important new idea for detrending macroeconomic time series and examines the Spanish business cycle pattern with respect to potential asymmetries. To address difficulties in the trend and cycle decomposition, a nonparametric trend estimation approach is introduced and exemplary applied to the Spanish GDP data for the period 1850 to 2015. The application of an iterative plug-in (IPI) algorithm for endogenous bandwidth selection solves the problem of choosing an adequate smoothing parameter for nonparametric regression. The algorithm identifies continuously Moving Trends (MT) with a time length of 34 years. After we estimate the trend nonparametrically, we fit several time series models to the residuals for further analysis. Although asymmetry during expansion and recession phases is indicated, it is not unambiguous.