Olivier Darné

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.

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.

New Trends in Macroeconomics

The Propagation of Macroeconomic Shocks: A Dynamic Model with Contracts and Imperfect Competition.- Variable Elasticity of Substitution and Economic Growth: Theory and Evidence.- Financial Intermediation and Economic Growth: A Semiparametric Approach.- Bridging the Gap: Linking Economics and Econometrics.- Revenue Smoothing in an ARIMA Framework: Evidence from the United States.- What VAR Tell us about DSGE Models?.- Ex ante Real Returns in Forward Market Speculation in the Inter-War Period: Evidence and Prediction.- Testing for Fractional Cointegration: The Relationship between Government Popularity and Economic Performance in the UK.- Non-stationarity Tests in Macroeconomic Time Series.- Seasonality, Nonstationarity and the Structural Forecasting of the Index of Industrial Production.- Complex Dynamics in Macroeconomics: A Novel Approach.

Nowcasting German GDP: A comparison of bridge and factor models

Governments and central banks need to have an accurate and timely assessment of Gross Domestic Products (GDP) growth rate for the current quarter, as this is essential for providing a reliable and early analysis of the current economic situation. This paper presents a series of models conceived to forecast the current German GDPs quarterly growth rate. These models are designed to be used on a monthly basis by integrating monthly economic information through bridge models, thus allowing for the economic interpretation of the data. We do also forecast German GDP by dynamic factor models. The combination of these two approaches allows selecting economically relevant explanatory variables among a large data set of hard and soft data. In addition, a rolling forecast study is carried out to assess the forecasting performance of the estimated models. To this end, publication lags are taken into account in order to run pseudo out-of-sample forecasts. We show that it is possible to get reasonably good estimates of current quarterly GDP growth in anticipation of the official release, especially from bridge models.

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.

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.

Market Efficiency in the European Carbon Markets

In this paper, we study the relationship between futures and spot prices in the European carbon markets from the cost-of-carry hypothesis. The aim is to investigate the extent of efficiency market. The three main European markets (BlueNext, EEX and ECX) are analyzed during Phase II, covering the period from March 13, 2009 to January, 17, 2012. Futures contracts are found to be cointegrated with spot prices and interest rates for several maturities in the three CO2 markets. Results are similar when structural breaks are taken into account. According to individual and joint tests, the cost-of-carry model is rejected for all maturities and CO2 markets, implying that neither contract is priced according to the cost-of-carry model. The absence of the cost-of-carry relationship can be interpreted as an indicator of market inefficiency and may bring arbitrage opportunities in the CO2 market.

The purchasing power parity in Australia: evidence from unit root test with structural break

The unit root test with structural break developed by Perron and Rodriguez are used to study the purchasing power parity (PPP) in the spirit of Balassa–Samuelson in Australia for the period January 1977 to April 2004. The results indicate that there is a break in February 1985 which coincides with the exchange rate crisis in 1985, occurring after the establishment of the dirty flexible exchange rate system. We also show that there is no tendency to the PPP in Australia to hold in the long-run during this period.

Testing the Number of Factors: An Empirical Assessment for a Forecasting Purpose

GDP forecasts based on dynamic factor models, applied to a large data set, are now widely used by practitioners involved in nowcasting and short-term macroeconomic forecasting. One recurrent empirical question that arises when dealing with such models is the way to determine the optimal number of factors. At the same time, statistical tests have recently been put forward in the literature in order to optimally determine the number of significant factors. In this article, we propose to reconcile both fields of interest by selecting the number of factors, through a testing procedure, to include in the forecasting equation. Through an empirical exercise on French and German GDPs, we assess the impact of a battery of recent statistical tests for the number of factors for a forecasting purpose. By implementing a rolling experience, we also assess the stability of the results overtime.

Non-stationarity Tests in Macroeconomic Time Series

This paper presents a selective survey of the literature on non-stationarity tests, namely standard and efficient unit root tests and stationarity tests, with or without structural changes. We also present the direct relation between non-stationarity tests and four economic theories, such as business cycles, hysteresis, purchasing power parity and convergence.