The prominent role of monetary policy in the U.S. interwar depression has been conventional wisdom since Friedman and Schwartz [1963]. This paper presents evidence on both the surprise and the systematic components of monetary policy between 1929 and 1933. Doubts surrounding GDP estimates for...

It is commonly accepted that information is helpful if it can be exploited to improve a decision making process. In economics, decisions are often based on forecasts of up- or downward movements of the variable of interest. We point out that directional forecasts can provide a useful framework...

This paper examines the role of currency and banking in the German financial crisis of 1931 for both Germany and the U.S. We specify a structural dynamic factor model to identify financial and monetary factors separately for each of the two economies. We find that monetary transmission through...

Common approaches to test for the economic value of directional forecasts are based on the classical Chi-square test for independence, Fisher’s exact test or the Pesaran and Timmerman (1992) test for market timing. These tests are asymptotically valid for serially independent observations....

We propose the use of a local autoregressive (LAR) model for adaptive estimation and forecasting of three of China’s key macroeconomic variables: GDP growth, inflation and the 7-day interbank lending rate. The approach takes into account possible structural changes in the data-generating...

This paper investigates why financial market experts misperceive the interest rate policy of the European Central Bank (ECB). Assuming a Taylor-rule-type reaction function of the ECB, we use qualitative survey data on expectations about the future interest rate, inflation, and output to discover...

In this article, we present new ideas concerning Non-Gaussian Component Analysis (NGCA). We use the structural assumption that a high-dimensional random vector X can be represented as a sum of two components - a lowdimensional signal S and a noise component N. We show that this assumption...

Let a high-dimensional random vector ⃗X can be represented as a sum of two components - a signal ⃗S , which belongs to some low-dimensional subspace S, and a noise component ⃗N . This paper presents a new approach for estimating the subspace S based on the ideas of the Non-Gaussian...