Laurent Ferrara

Are Disaggregate Data Useful for Factor Analysis in Forecasting French GDP

This paper compares the GDP forecasting performance of alternative factor models based on monthly time series for the French economy. These models are based on static and dynamic principal components. The dynamic principal components are obtained using time and frequency domain methods. The forecasting accuracy is evaluated in two ways for GDP growth. First, we question whether it is more appropriate to use aggregate or disaggregate data (with three disaggregating levels) to extract the factors. Second, we focus on the determination of the number of factors obtained either from various criteria or from a fixed choice.

A three-regime real-time indicator for the US economy

Abstract This note proposes a monthly real-time cyclical indicator for the US economy computed through a three-regime multivariate Markov-Switching model. The model specification is based on the reconciliation of the concepts of growth and business cycles.

A System for Dating and Detecting Turning Points in the Euro Area

In the paper we aim to introduce a statistical dating and detection of turning points giving them a first economic interpretation. The main advantage of the proposed approach is represented by the fact that classical and growth cycles are jointly considered both in the dating and in the detecting stage. A key result of this choice is a better description of different economic phases as well as a more accurate investigation of the economic cyclical behaviour. The proposed approach considerably improves the relevance of information delivered to users in comparison with a standard analysis based only on classical or growth cycle components.

Housing Cycles in the Major Euro Area Countries

The recent burst of the house price bubble in the United States and its spillover effects on real economies worldwide has rekindled the interest in the role of housing in the business cycle. In this paper, we investigate the relationships between housing cycles among the four major euro area countries (Germany, France, Italy and Spain) over the sample 1980Q1-2008Q4. Our main findings are that GDP cycles show a high degree of comovement across these four countries, reflecting trade linkages, but much weaker ones for housing market cycles, where idiosyncratic factors play a major role. House prices are even less related than quantities across countries. We also find much stronger relationships in the common monetary policy period.

Identification of Slowdowns and Accelerations for the Euro Area Economy

In addition to quantitative assessment of economic growth using econometric models, business cycle analyses have been proved to be helpful to practitioners in order to assess current economic conditions or to anticipate upcoming fluctuations. In this paper, we focus on the acceleration cycle in the euro area, namely the peaks and troughs of the growth rate which delimitate the slowdown and acceleration phases of the economy. Our aim is twofold: First, we put forward a reference turning point chronology of this cycle on a monthly basis, based on gross domestic product and industrial production index. We consider both euro area aggregate level and country specific cycles for the six main countries of the zone. Second, we come up with a new turning point indicator, based on business surveys carefully watched by central banks and short-term analysts, in order to follow in real-time the fluctuations of the acceleration cycle.

Forecasting Euro-Area Recessions Using Time-Varying Binary Response Models for Financial Markets

Recent macroeconomic evolutions during the years 2008 and 2009 have pointed out the impact of financial markets on economic activity. In this paper, we propose to evaluate the ability of a set of financial variables to forecast recessions in the euro area by using a non-linear binary response model associated with information combination. Especially, we focus on a time-varying probit model whose parameters evolve according to a Markov chain. For various forecast horizons, we provide a readable and leading signal of recession by combining information according to two combining schemes over the sample 1970-2006. First we average recession probabilities and second we linearly combine variables through a dynamic factor model in order to estimate an innovative factor-augmented probit model. Out-of-sample results over the period 2007-2008 show that financial variables would have been helpful in predicting a recession signal as September 2007, that is around six months before the effective start of the 2008-2009 recession in the euro area.

Monthly Forecasting of French GDP: A Revised Version of the Optim Model

This paper presents a revised version of the model OPTIM, proposed by Irac and Sedillot (2002), used at the Banque de France in order to predict French GDP quarterly growth rate, for the current and next quarters. The model is designed to be used on a monthly basis by integrating monthly economic information through bridge models, for both supply and demand sides of GDP. For each GDP component, bridge equations are specified by using a general-to-specific approach implemented in an automated way by Hoover and Perez (1999) and improved by Krolzig and Hendry (2001). This approach allows to select explanatory variables among a large data set of hard and soft data. The final choice of equations relies on a recursive forecast study, which also helps to assess the forecasting performance of the revised OPTIM model in the prediction of aggregated GDP. This study is based on pseudo real-time forecasts taking publication lags into account. It turns out that the model outperforms benchmark models.

Forecasting Growth During the Great Recession: Is Financial Volatility the Missing Ingredient?

The Great Recession endured by the main industrialized countries during the period 2008–2009, in the wake of the financial and banking crisis, has pointed out the major role of the financial sector on macroeconomic fluctuations. In this respect, many researchers have started to reconsider the linkages between financial and macroeconomic areas. In this paper, we evaluate the leading role of the daily volatility of two major financial variables, namely commodity and stock prices, in their ability to anticipate the output growth. For this purpose, we propose an extended MIDAS model that allows the forecasting of the quarterly output growth rate using exogenous variables sampled at various higher frequencies. Empirical results on three industrialized countries (US, France, and UK) show that mixing daily financial volatilities and monthly industrial production is useful at the time of predicting gross domestic product growth over the Great Recession period.

Monthly GDP Forecasting Using Bridge Models: Application for the French Economy

This paper presents a model to predict French gross domestic product (GDP) quarterly growth rate. The model is designed to be used on a monthly basis by integrating monthly economic information through bridge models, for both supply and demand sides, allowing thus economic interpretations. For each GDP component, bridge equations are specified by using a general‐to‐specific approach implemented in an automated way by Hoover and Perez and improved by Krolzig and Hendry. This approach allows to select explanatory variables among a large data set of hard and soft data. A rolling forecast study is carried out to assess the forecasting performance in the prediction of aggregated GDP, by taking publication lags into account in order to run pseudo real‐time forecasts. It turns out that the model outperforms benchmark models. The results show that changing the set of equations over the quarter is superior to keeping the same equations over time. In addition, GDP growth seems to be more precisely predicted from a supply‐side approach rather than a demand‐side approach.

Cyclical Relationships Between GDP and Housing Market in France: Facts and Factors at Play

In this paper we focus on cycles and trends of some macroeconomic and housing market variables representative of the French economy. In a first part, we empirically show that cycles in the housing sector, measured by housing prices, housing starts, building permits, sales or residential investment, are strongly correlated to GDP cycles with a lead lying between of one and four quarters, suggesting thus that a monitoring of housing fluctuations could bring useful information for macroeconomic forecasting. Interestingly, this result is robust to the various considered approaches. Moreover, it seems that the housing sector long-term trend possesses its own dynamics, quite different from the global French economic activity. Thus, in a second part, we review various structural factors that could drive housing market developments in France in the future.