Housing price forecastability: A factor analysis.
(with Stig Vinther Møller). Accepted at Real Estate Economics. Presented at EFA 2012, IFABS 2013. SSRN
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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 121 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 signi.cant out-of-sample predictive
power over and above the predictive power contained by the price-rent ratio and autoregressive benchmarks.

Keywords: House prices; Forecasting; Factor model; Principal components; Macroeconomic factors; Factor forecast combination; Bootstrap.

A New Index of Housing Sentiment.
(with Stig Vinther Møller and Thomas Quistgaard Pedersen). Sentiment index data is available here https://www.dropbox.com/s/al3sddq1026xci2/Online%20data.xlsx?dl=0.
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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.

Keywords: Housing sentiment; house price forecastability; partial least squares; dynamic model averaging-



A large-dimensional factor analysis of the Federal Reserve's large-scale asset purchases
Presented at IAAE in Thessaloniki, June 2015. SSRN
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This paper assesses the economy-wide effects of US unconventional monetary policy shocks. An unconventional expansionary monetary policy shock is identified as a shock that increases the Federal Reserve's 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 $2 trillion purchases of mortgage backed securities by the Federal Reserve Bank avoided a severe downturn according to estimates from a counterfactual analysis. [Read more under PAPERS .....]

Keywords: unconventional monetary policy, zero lower bound, large cross-sections, dynamic factor model, factor-augmented vector autoregression (FAVAR), Expectation-Maximization algorithm


Do Exchange Rates Really Help Forecasting Commodity Prices?
(with Pablo Rovira Kaltwasser and Piet Sercu). Presented at SNDE NY, CEF Oslo, and EFA Lugano.
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Chen et al. (2010) report that for ‘commodity currencies’, the exchange rate predicts the country’s commodity index but not vice versa. Using three different data sets and various econometric techniques, we do find the contemporaneous correlations as predicted by the financial asset view of commodity prices. However, cross-predictability, in contrast, seems to be only minor at best, not robust to plausible variations in the test design, and bi-directional rather than one-directional. [Read more under PAPERS .....]

Keywords: Commodity prices, exchange rates, Granger Causality, forecast, forecast combination.

Forecasting House Prices in the 50 States Using Dynamic Model Averaging and Dynamic Model Selection.
(with Stig Vinther Møller). International Journal of Forecasting, volume 31, issue 1, pages 63-78.  SSRN, AAU
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We examine house price forecastability across the 50 states using Dynamic Model Averaging and Dynamic Model Selection, which allow for model change and parameter shifts. By allowing the entire forecasting model to change over time and across locations, the forecasting accuracy improves substantially. The states in which housing markets have been the most volatile are the states where model change and parameter shifts have been the most needed.

Keywords: Forecasting housing markets; 50 states; Kalman filtering methods; Model change; Parameter shifts; BMA; DMS; Boom-bust cycle.
Estimating US Monetary Policy Shocks Using a Factor-Augmented Vector Autoregression: An EM Algorithm Approach
(under revision).
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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) .......

Keywords: Monetary policy, Dynamic Factor Models, EM Algorithm, Factor-augmented VAR (FAVAR). 

Identification of Macroeconomic Factors in Large Panels.
(with Hans Dewachter & Romain Houssa). Under revision.
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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 developed. .......

Keywords: Monetary policy, Dynamic Factor Models, EM Algorithm, Factor-augmented VAR (FAVAR).

PhD thesis:
Macro Factors, Monetary Policy Analysis and Affine Term Structure Models
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Defended February 26, 2010.


The thesis consists of three self-contained chapters. Chapter 1 is about estimation of the Factor-Augmented VAR (FAVAR) by the EM algorithm. Chapter 2 is about well-defined economic interpretation of the dynamic factors which is achieved by means of over-identifying loading restrictions. Chapter 3 is about macroeconomic sources of variation in the expected excess bond returns as implied by an affine term structure model driven by data-rich macroeconomic state variables.

Keywords: Affine term structure model, Monetary policy. Dynamic Factor Models, EM Algorithm, Factor-augmented VAR (FAVAR). 


Associate professor, Department of Business and Management, Aalborg University.
PhD in Finance, Finance Research Group, Aarhus School of Business, Aarhus University. MSc Economics.
Research interests:
Asset Pricing and Macro-Finance models of the yield curve. Dynamic factor models. State-space models. Monetary economics and macroeconomics. Financial econometrics.