Valerie Grossman

A Multi-Country Approach to Forecasting Output Growth Using PMIS

This paper derives new theoretical results for forecasting with Global VAR (GVAR) models. It is shown that the presence of a strong unobserved common factor can lead to an undeter-mined GVAR model. To solve this problem, we propose augmenting the GVAR with additional proxy equations for the strong factors and establish conditions under which forecasts from the augmented GVAR model (AugGVAR) uniformly converge in probability to the infeasible optimal forecasts obtained from a factor-augmented high-dimensional VAR model. The small sample properties of the proposed solution are investigated by Monte Carlo experiments as well as empirically. In the empirical part, we investigate the value of the information content of Purchasing Managers Indices (PMIs) for forecasting global (48 countries) growth, and compare forecasts from AugGVAR models with a number of data-rich forecasting methods, including Lasso, Ridge, partial least squares and factor-based methods. It is found that (a) regardless of the forecasting methods considered, PMIs are useful for nowcasting, but their value added diminishes quite rapidly with the forecast horizon, and (b) AugGVAR forecasts do as well as other data-rich forecasting techniques for short horizons, and tend to do better for longer forecast horizons.

A New Database of Global Economic Indicators

The Database of Global Economic Indicators (DGEI) from the Federal Reserve Bank of Dallas aims to standardize and disseminate economic indicators for policy analysis and scholarly work on the role of globalization. Its main purpose is to offer a broad perspective on a number of global factors affecting the U.S. economy. DGEI indicators are based on a core sample of 40 countries with aggregates for the rest of the world (ex. the U.S.) and by level of development attainment and openness to trade. DGEI indicators currently include real GDP, industrial production (IP), Purchasing Managers Index (PMI), merchandise exports and imports, headline CPI, core CPI (ex. food and energy), PPI/WPI inflation, nominal and real exchange rates, and short-term interest rates. Here we describe our methodology to transform and combine different time series, for temporal and cross-country aggregation, and to highlight the importance of using representative data in international macroeconomics research. Our paper makes a related contribution to the literature by providing a formal assessment of conventional interpolation methods used to adjust the data frequency. A selection of the DGEI-derived global indicators – to be updated monthly – can be accessed at the following URL: http://www.dallasfed.org/institute/dgei/index.cfm.

Monitoring Housing Markets for Episodes of Exuberance: An Application of the Phillips Et Al. (2012, 2013) GSADF Test on the Dallas Fed International House Price Database

The detection of explosive behavior in house prices and the implementation of early warning diagnosis tests are of great importance for policy-making. This paper applies the GSADF test developed by Phillips et al. (2012) and Phillips et al. (2013), a novel procedure for testing, detection and date-stamping of explosive behavior, to the data from the Dallas Fed International House Price Database documented in Mack and Martinez-Garcia (2011). We discuss the use of the GSADF test to monitor international housing markets. We assess the international boom and bust cycle experienced during the past 15 years through this lens — with special attention to the United States, the United Kingdom, and Spain. Our empirical results suggest that these three countries experienced a period of exuberance in housing prices during the late 90s and the first half of the 2000s that cannot be attributed solely to the behavior of fundamentals. Looking at all 22 countries covered in the International House Price Database, we detect a pattern of synchronized explosive behavior during the last international house boom-bust episode not seen before.

Explosive Dynamics in House Prices? An Exploration of Financial Market Spillovers in Housing Markets Around the World

Asset prices in general, and real house prices in particular, are often characterized by a nonlinear data-generating process which displays mildly explosive behavior in some periods. Here, we investigate the emergence of explosiveness in the dynamics of real house prices and the role played by asset market spillovers. We establish a timeline of periodically-collapsing episodes of explosiveness for a panel of 23 countries from the Federal Reserve Bank of Dallas’ International House Price Database (Mack and MartÃnez-GarcÃa (2011)) between first quarter 1975 and fourth quarter 2015 using the recursive unit root test methodology proposed by Phillips et al. (2015a,b). Motivated by the theory of financial arbitrage, we examine within a dynamic panel logit/probit framework whether macro fundamentalsâ€�?and, more specifically, financial variablesâ€�?help predict episodes of explosiveness in real house prices. We find that interest rate spreads and real stock market growth together with standard macro variables (growth in personal disposable income per capita and inflation) are amongst the best predictors. We, therefore, argue that financial developments in other asset markets play a significant role in the emergence of explosiveness in housing markets.

Detecting Periods of Exuberance: A Look at the Role of Aggregation with an Application to House Prices

The recently developed SADF and GSADF unit root tests of Phillips et al. (2011) and Phillips et al. (2015) have become popular in the literature for detecting exuberance in asset prices. In this paper, we examine through simulation experiments the effect of cross-sectional aggregation on the power properties of these tests. The simulation design considered is based on actual housing data for both U.S. metropolitan and international housing markets and thus allows us to draw conclusions for different levels of aggregation. Our findings suggest that aggregation lowers the power of both the SADF and GSADF tests. The effect, however, is much larger for the SADF test. We also provide evidence that tests based on panel data techniques, namely the panel GSADF test recently proposed by Pavlidis et al. (2015), can perform substantially better than univariate tests applied to aggregated series.