Approximate Solutions to Dynamic Models - Linear Methods

Linear Methods are often used to compute approximate solutions to dynamic models, as these models often cannot be solved analytically. Linear methods are very popular, as they can easily be implemented. Also, they provide a useful starting point for understanding more elaborate numerical methods. It shall be described here first for the example of a simple real business cycle model, including how to easily generate the log-linearized equations needed before solving the linear system. For a general framework, formulas are provided for calculating the recursive law of motion. The algorithm described here is implemented with the toolkit programs available per http://www.wiwi.hu-berlin.de/wpol/html/toolkit.htm

Methods for Computing Marginal Data Densities from the Gibbs Output

We introduce two new methods for estimating the Marginal Data Density (MDD) from the Gibbs output, which are based on exploiting the analytical tractability condition. Such a condition requires that some parameter blocks can be analytically integrated out from the conditional posterior densities. Our estimators are applicable to densely parameterized time series models such as VARs or DFMs. An empirical application to six-variate VAR models shows that the bias of a fully computational estimator is sufficiently large to distort the implied model rankings. One estimator is fast enough to make multiple computations of MDDs in densely parameterized models feasible

Quantitative Easing in Joseph's Egypt with Keynesian Producers

This paper considers monetary and fiscal policy when tangible assets can be accumulated after shocks that increase desired savings, like Joseph's biblical prophecy of seven fat years followed by seven lean years. The model's flexible-price allocation mimics Joseph's saving to smooth consumption. With nominal rigidities, monetary policy that eliminates liquidity traps leaves the economy vulnerable to confidence recessions with low consumption and investment. Josephean Quantitative Easing, a fiscal policy that purchases either obligations collateralized by tangible assets or the assets themselves, eliminates both liquidity traps and confidence recessions by putting a floor under future consumption. This requires no commitment to a time-inconsistent plan

Real-Time Forecasting with a MIDAS VAR

This paper presents a MIDAS type mixed frequency VAR forecasting model. First, we propose a general and compact mixed frequency VAR framework using a stacked vector approach. Second, we integrate the mixed frequency VAR with a MIDAS type Almon lag polynomial scheme which is designed to reduce the parameter space while keeping models flexible. We show how to recast the resulting non-linear MIDAS type mixed frequency VAR into a linear equation system that can be easily estimated. A pseudo out-of-sample forecasting exercise with US real-time data yields that the mixed frequency VAR substantially improves predictive accuracy upon a standard VAR for different VAR specififications. Forecast errors for, e.g., GDP growth decrease by 30 to 60 percent for forecast horizons up to six months and by around 20 percent for a forecast horizon of one year

Uncertainty About Federal Reserve Policy and Its Transmission to Emerging Economies: Evidence from Twitter

It is well known that a tightening or easing of the United States' monetary policy affects financial markets in emerging economies. This paper argues that uncertainty about future monetary policy is a separate transmission channel. We focus on the taper tantrum episode in 2013, a period with an elevated uncertainty about monetary policy, and use a data set that contains 90,000 Twitter messages ("tweets") on Federal Reserve tapering. Based on this data set, we construct a new index about monetary policy uncertainty using a list of uncertainty keywords. An advantage of this index is that it reflects uncertainty about a specific policy decision. An estimated vector autoregression (VAR) shows that uncertainty shocks lead to a fall in asset prices and a depreciation of local currencies. We also discuss the policy implications of this uncertainty channel of monetary policy transmission

The Role of Data Revisions and Disagreement in Professional Forecasts

This paper aims at evaluating individual expectation accuracy of professional forecasters for 57 U.S., European, and German macroeconomic indicators over the period 1999-2010. The empirical analysis shows that initial announcements are partly considerably revised, and that some revisions occur systematically. Taking into account whether announcements are revised systematically and whether economists (assumingly) aim at forecasting the initial release or the latest revision, signi cant differences can be observed with regard to forecasters' expectation errors. In general, forecasters that are (assumingly) aiming to predict the latest revisions of German indicators are able to form better forecasts if these indicators are revised systematically. Though to a lower extent, this relationship is also observable regarding U.S. indicators. Forecasters' disagreement about fundamentals is higher during recessions and when stock markets are volatile