Lasse Bork

Housing Price Forecastability: A Factor Analysis

We examine U.S. housing price forecastability using principal component analysis (PCA), partial least squares (PLS), and sparse PLS (SPLS). We incorporate information from a large panel of 128 economic time series and show that macroeconomic fundamentals have strong predictive power for future movements in housing prices. We find that (S)PLS models systematically dominate PCA models. (S)PLS models also generate significant out-of-sample predictive power over and above the predictive power contained by the price-rent ratio, autoregressive benchmarks, and regression models based on small datasets.

Estimating US Monetary Policy Shocks Using a Factor-Augmented Vector Autoregression: An EM Algorithm Approach

Economy-wide effects of shocks to the US federal funds rate are estimated in a state space model with 120 US macroeconomic and financial time series driven by the dynamics of the federal funds rate and a few dynamic factors. This state space system is denoted a factor-augmented VAR (FAVAR) by Bernanke et al. (2005). I estimate the FAVAR by the fully parametric one-step EM algorithm as an alternative to the two-step principal component method and the one-step Bayesian method in Bernanke et al. (2005). The EM algorithm which is an iterative maximum likelihood method estimates all the parameters and the dynamic factors simultaneously and allows for classical inference. I demonstrate empirically that the same impulse responses but better fit emerge robustly from a low order FAVAR with eight correlated factors compared to a high order FAVAR with fewer correlated factors, for instance four factors. This empirical result accords with one of the theoretical results from Bai & Ng (2007) in which it is shown that the information in complicated factor dynamics may be substituted by panel information.

Identification of Macroeconomic Factors in Large Panels

This paper presents a dynamic factor model in which the extracted factors and shocks are given a clear economic interpretation. The economic interpretation of the factors is obtained by means of a set of over-identifying loading restrictions, while the structural shocks are estimated following standard practices in the SVAR literature. Estimators based on the EM algorithm are developped. We apply this framework to a large panel of US monthly macroeconomic series. In particular, we identify nine macroeconomic factors and discuss the economic impact of monetary policy stocks. The results are theoretically plausible and in line with other findings in the literature.

Do Exchange Rates Really Help Forecasting Commodity Prices

Engel and West (2005) model log exchange rates as discounted log fundamentals. For ‘commodity currencies’, commodity prices are often viewed as key fundamentals, implying that commodity prices should, therefore, be predicted by exchange rates and not vice-versa - which would run counter to a vast literature that regards commodities as financial assets, whose price changes are normally hard to predict. Prior empirical results have not lead to a consensus. Using more and cleaner data, various frequencies, different indices like principal components and Efficient Y-Index regression (EYRe), and a wider array of tests (including model averaging and trading tests), we find no asymmetric predictive ability either way; the link is essentially contemporaneous.

A New Index of Housing Sentiment

We propose a new measure for housing sentiment and show that it accurately tracks expectations about future house price growth rates. We construct the housing sentiment index using partial least squares on household survey responses to questions about buying conditions for houses. We ?find that housing sentiment explains a large share of the time-variation in house prices during both boom and bust cycles and it strongly outperforms several macroeconomic variables typically used to forecast house prices.

A Large-Dimensional Factor Analysis of the Federal Reserve's Large-Scale Asset Purchases

This paper assesses the economy-wide effects of US unconventional monetary policy shocks. A precise identification of the unconventional monetary policy shocks is achieved by imposing zero and sign restrictions on a number of impulse responses from a large-dimensional dynamic factor model. In particular, an unconventional expansionary monetary policy shock is identified as a shock that increases the Federal Reserves market share of US treasuries and mortgage-backed securities, and leads to an improvement in the real economy and improved credit conditions.I find that an unconventional monetary policy shock significantly drives down the long-term interest rate spread and the credit spread, and improves both the financial market conditions and the commercial and industrial loans activity. Moreover, the impact on the real economy is significant.The roughly

Housing price forecastability: A factor analysis

2 trillion purchases of mortgage backed securities by the Federal Reserve Bank avoided a severe downturn according to estimates from a counterfactual analysis.