Karim Barhoumi

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.

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 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.

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.

Dynamic Factor Models: A Review of the Literature

In the last few years, the growth in the amount of economic and financial data available has prompted econometricians to develop or adapt new methods enabling them to summarise efficiently the information contained in large databases. Of these methods, dynamic factor models have seen rapid growth and become very popular among macroeconomists. In this paper, we carry out a survey of recent literature on dynamic factor models. We start by presenting the models used before looking at parameter estimation methods and statistical tests available for choosing the number of factors. We then focus on recent empirical applications dealing with the construction of economic outlook indicators, macroeconomic forecasts, and both macroeconomic and monetary policy analyses.

UNE REVUE DE LA LITTÉRATURE DES MODÈLES À FACTEURS DYNAMIQUES (Dynamic Factor Models: A Review of the Literature)

For few years, the increasing size of available economic and financial databases has led econometricians to develop and adapt new methods in order to efficiently summarize information contained in those large datasets. Among those methods, dynamic factor models have known a rapid development and a large success among macroeconomists. In this paper, we carry out a review of the recent literature on dynamic factor models. First we present the models used, then the parameter estimation methods and finally the statistical tests available to choose the number of factors. In the last section, we focus on recent empirical applications, especially dealing with the building of economic outlook indicators, macroeconomic forecasting and macroeconomic and monetary policy analyses.The English version of this document can be found at: http://ssrn.com/abstract=2291459

Stochastic Trends, Debt Sustainability and Fiscal Policy

We study empirically the reaction of fiscal policy to changes in the permanent and transitory components of GDP in a panel of countries. We find evidence that government spending tends to be counter-cyclical conditional on temporary shocks and pro-cyclical conditional on permanent shocks. We also find no evidence that developing countries are systematically different from developed ones in terms of fiscal policy. We present a theory featuring a fiscal reaction function to the output gap and a measure of debt sustainability. The fiscal impulse response to a permanent (temporary) shock to GDP is positive (negative) as the effect on debt sustainability (current output gap) dominates. The results are mostly sensitive to the relative weight of debt sustainability in the fiscal reaction function as well as to the extent of real rigidities in the economy.