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This short essay analyses the evolution of three key aspects of the International Monetary Fund in response to the Global Financial Crisis: (a) the adequacy of its own resources; (b) the shift of representation towards emerging and developing countries; (c) the review of the lending instruments. With respect to each of these topics, we describe the main policy choices which have characterised the past few years and provide our own assessment about them. Our main policy recommendations attain to: (a) the opportunity of preserving the overall size of the Global Financial Safety Net and in particular the role of the IMF; (b) the opportunity of dealing with the legitimacy problem arising from the strong underrepresentation of China at the IMF; (c) the opportunity of a very careful scrutiny for any new lending instrument.

The aim of this paper is to analyze the performance of alternative forecasting methods to predict the index of industrial production in Italy from 1 to 3 months ahead. We use twelve different models, from simple ARIMA to dynamic factor models exploiting the timely information of up to 110 short-term indicators, both qualitative and quantitative. This allows to assess the relevance for the forecasting practice of alternative combinations of types of data (real-time and latest available), estimation methods and periods. Out-of-sample predictive ability tests stress the relevance of more indicators in disaggregate models over sample periods covering a complete business cycle (about 7 years in Italy). Our findings downgrade the emphasis on both the estimation method and data revision issues. In line with the classical “average puzzle”, the use of simple averages of alternative forecasts often improves the predictive ability of their single components, mainly over short horizons. Finally, selected indicators and factor-based models always perform significantly better than ARIMA models, suggesting that the short-run indicator signal always dominates the noise component. On this regard, selected indicators models can further increase the amount of signal extracted to improve up to 30–40% the short-run predictive ability of factor-based models and to forecast-encompass them.

The delayed release of the National Account data for GDP is an impediment to the early understanding of the economic situation. In the short run, this information gap may be at least partially eliminated by bridge models (BM) which exploit the information content of timely updated monthly indicators. In this paper we examine the forecasting ability of BM for GDP growth in the G7 countries and compare their performance to that of univariate and multivariate statistical benchmark models. We run four alternative one-quarter-ahead forecasting experiments to assess BM performance in situations as close as possible to the actual forecasting activity. BM are estimated for GDP both for single countries (USA, Japan, Germany, France, UK, Italy and Canada), and area-wide (G7, European Union, and Euro area). BM forecasting ability is always superior to that of benchmark models, provided that at least some monthly indicator data are available over the forecasting horizon. Copyright © 2007 John Wiley & Sons, Ltd.

This paper proposes a simple procedure to obtain monthly assessments of short-run perspectives for quarterly world GDP and trade. It combines emerging and advanced countries� high frequency information to explain quarterly national accounts variables through bridge models. The union of all bridge equations leads to our world bridge model (WBM). The WBM econometric approach is new for two reasons: its equations combine traditional short-run bridging with theoretical level-relationships; it is the first time that forecasts of world GDP and trade are computed for advanced and emerging countries on the basis of a real-time database of 7,000 time series. Although the performance of the equations that are automatically searched for should be taken as a lower bound, results show a better WBM forecasting ability than the benchmark case and confirm the usefulness of combining WBM real-time forecasts with preliminary releases to improve the prediction of world trade. Finally, we show that the (unrealistic) use of revised data leads to a systematic overstatement of model forecasting performance.

The objective of this article is to reassess the validity of the consumer confidence (or sentiment) indices in anticipating the evolution of economic activity by considering a fairly high number of countries across the world (i.e. France, Germany, Italy, UK, USA, Japan, Canada and Australia) over a period of about thirty years, from the beginning of the seventies till the end of 2002 (quarterly data). To our knowledge this is the first attempt to analyse the consumer confidence index for several countries over such a long period of time. We model the CSIoutput relationship in a cointegrated vector autoregression (VAR) framework, by considering a common set of variables for all countries. Our findings suggest that: (a) what appears to be the main driving forces of consumer confidence cannot be simply summarised on the basis of ...

This paper concerns the cyclical evolution of the Italian economy and, in particular, the role of confidence indicators. In the first part of the paper various confidence indicators, computed according to the European Commission (EC) methodology, are investigated. In particular, the analysis concentrates on the dynamics of some business climate indicators referred to the supply side of the economy (i.e. manufacturing, retail and construction industries). In the second part of the analysis, new confidence indicators exploring the wide informative set characterizing the ISAE business surveys are computed. The ability of these indicators in predicting the short-term evolution of GDP, here considered as a reference series, is compared with that of the previous EC confidence indicators. Finally, in the third part, some estimates of the relationship between the chosen business confidence indicator and some driving variables are presented. Copyright © 2003 John Wiley & Sons, Ltd.

Measures of potential output and the output gap are increasingly being developed and used to concisely quantify and monitor the risk of price accelerations stemming from rises in aggregate demand that are not met by a corresponding increase in supply. They often play a prominent role in the price determination mechanisms of macroeconometric models. In this paper we build a measure of potential private-sector value added for the Italian economy that is consistent with the capital accumulation process in the Banca d'Italia's Quarterly Model — and more generally with the rest of the supply-side block of that model. More specifically, we exploit the fact that the investment function can be thought of as a relationship transforming desired gross additions to capacity output into capital accumulation by means of a conversion factor (the optimal capital/output ratio). Thus, if one removes the component of investment decisions that stems from changes in the relative price of the production factors, (i.e. in the optimal capital/output ratio), then a measure of the desired gross addition to capacity may be constructed. The results draw a cyclical picture of the degree of capacity utilisation for the period 1970–1997 that is roughly in line with those produced by the Wharton and Hodrick–Prescott filter approaches, as well as with the pictures resulting from the ISAE, IMF, European Commission and OECD measures of the output gap. Our investment-function-based measure appears to be a promising indicator of the pressure exerted on prices by demand accelerations. Its empirical properties are, on the whole, acceptable and plausible.

We investigate the prediction of italian industrial production and first specify a model based on electricity consumption showing that the cubic trend in such a model mostly captures the evolution over time of the electricity coefficient, which can be well approximated by a smooth transition model, with no gains in predictive power. We also analyse the performance of models based on data of two different business surveys. According to the standard statistics of forecasting accuracy, the linear energy-based model is not outperformed by any other model, nor by a combination of forecasts. However, a more comprehensive set of evaluation criteria sheds light on the relative merit of each individual model. A modelling strategy which makes full use of all information available is proposed. Copyright © 2000 John Wiley & Sons, Ltd.

Theoretical models of investment under uncertainty predict that the sign and the strength of the investment-uncertainty relationship is in principle ambiguous and can vary greatly across groups of firms depending on the degree of irreversibility of investment and the market power of the firm. This paper investigates the effects of uncertainty on the investment decisions of a sample of Italian manufacturing firms, using information on the subjective probability distribution of future demand for firms’ products according to entrepreneurs. The results support the view that uncertainty slows down capital accumulation. Consistent with the predictions of the theory, there is considerable heterogeneity in the effect of uncertainty on investment: it is stronger for firms that cannot easily reverse investment decisions and for those with substantial market power. We show that the negative effect of uncertainty on investment cannot be explained by uncertainty proxying for liquidity constraints, credit rationing being more likely among riskier firms. Evidence of a negative effect of past uncertainty on hours currently worked reinforces the conclusion of a negative relationship between uncertainty and investment.

The paper presents a small macroeconometric model for short-term forecasting of Italian households' consumption expenditure. In the search for leading indicators, the authors rely heavily on anticipation variables derived from a national household survey yielding some direct measures of consumers' expectations and subjective uncertainty. While the emphasis of the paper is on prediction, a theory-based approach is adopted in modelling the consumption equations, to the extent permitted by the data. Results appear quite satisfactory. The model outperforms in prediction the best purely time series model as well as a restricted model without the main attitudinal variables.

The rapid growth of the emerging markets and of China in particular has changed the economic landscape: emerging Asias share of world trade has grown from about 13% in 1990 to almost 23% in 2008, and its aggregate GDP now accounts for more than 25% of world output, compared with less than 12% in 1990. In this paper we focus on the consequences for the assessment of the global outlook and the specification of forecasting equations. Our main results are that (1) the rise of the emerging countries has led to a sharp change in the correlation of growth rates among main economic areas; (2) this is clearly detectable in forecasting equations too, as a structural break occurring in the 1990s; (3) hence, inferences about global developments based solely on the industrialized countries are highly unreliable; (4) the otherwise cumbersome task of monitoring many and little-known countries can be tackled by resorting to very simple bridge models (BM); (5) BM performance is in line with that of the most widely quoted predictions (WEO, Consensus Forecasts) both before and during the recent crisis; and (6) for some emerging economies, BMs would have provided even better forecasts during the recent crisis.

This paper contributes to the literature on family firms in two ways. First, it focuses on a largely neglected but important issue of family firms’ investment decisions. Second, it uses a novel and rich data set about Italian private firms; this complements the literature, which typically focuses on publicly traded companies, in an important way, given that most family firms are private and relatively little information is available on their behaviour. Our results suggest that family firms’ investments are significantly more sensitive to uncertainty than nonfamily firms. We find evidence that the greater sensitivity to uncertainty is basically due to the greater opacity of family firms and to their higher risk aversion, rather than to the degree of sunk fixed capital as is typical in the literature on investment decisions.

Assessing the global economic outlook is a fundamentally important task of international financial institutions, governments and central banks. In this paper we focus on the consequences of the rapid growth of emerging markets for monitoring and forecasting the global outlook. Our main results are that (i) the rise of the emerging countries has sharply altered the correlation of growth rates among the main economic areas; (ii) this is clearly detectable in forecasting equations as a structural break occurring in the 1990s; (iii) hence, inferences on global developments based solely on the industrialized countries are highly unreliable; (iv) the otherwise cumbersome task of monitoring many � and less studied � countries can be tackled by resorting to very simple bridge models (BM); (v) BM performance is in line with that of the most widely quoted predictions (WEO, Consensus) both before and during the recent crisis; (vi) for some emerging economies, BMs would have provided even better forecasts during the recent crisis.

From a theoretical point of view, uncertainty over the demand for a firm's product may not have clear effects on investments, because of the influence of a number of factors, such as the production technology and the amount of competition in the product market. Until now, a deeper investigation of the interplay of different factors in the temporal dimension has not been possible because the empirical research has been based on cross-section analysis. This omission makes biased estimates of the investment-uncertainty relationship likely. The aim of this paper is to extend the findings of the empirical literature by using a panel of Italian firms over the period 1996-2004, covering a complete business cycle. The availability of a panel of survey data on companies' investment plans, expected future sales and demand uncertainty allows us to account for unobservable individual firm differences, macroeconomic shocks and the temporal evolution of the investment-uncertainty relationship. A key finding of our paper concerns the role of the competition faced by Italian firms in 1996-2004. The gradual loss of market power experienced by Italian manufacturing firms along with the increasing flexibility of labour input may have weakened the negative effect of uncertainty on investment decisions. We show that, in repeated cross-section estimates, the omission of firm-specific effects together with the dynamic interplay described above, would have lead to misleading conclusions about the relevance of demand uncertainty in explaining investment decisions.

Family firms are a widespread control structure in most countries, especially among smaller firms. A vast literature addresses the question of whether they are performing better or worse than comparable non family firms, with not entirely conclusive results. Here we take a different, indirect approach and test whether investment decisions in family firms are more sensitive to uncertainty than in other firms. By using a novel dataset that includess both a better definition of family firms than commonly used (through self evaluation) and a very good proxy of the uncertainty on future demand that firms face, we are able to verify that – as compared to other firms – family firms are significantly more sensitive to uncertainty: this might contribute to explain why in some situations they perform better, whereas in others they do worse. We find evidence that this greater sensitivity to uncertainty in family firms is basically due to the effects of risk aversion and capital irreversibility, where the latter appear to be associated to a greater opaqueness of family firms rather than to the degree of sunkness of fixed capital. Finally, we propose some evidence that the prevalence of family firms in Italy might be associated to long standing institutional factors, such as an inefficient law enforcement system and a low social capital.

This paper uses real-time data to mimic real-time GDP forecasting activity. Through automatic searches for the best indicators for predicting GDP one and four steps ahead, we compare the out-of-sample forecasting performance of adaptive models using different data vintages, and produce three main findings. First, despite data revisions, the forecasting performance of models with indicators is better, but this advantage tends to vanish over longer forecasting horizons. Second, the practice of using fully updated datasets at the time the forecast is made (i.e., taking the best available measures of today's economic situation) does not appear to bring any effective improvement in forecasting ability: the first GDP release is predicted equally well by models using real-time data as by models using the latest available data. Third, although the first release is a rational forecast of GDP data after all statistical revisions have taken place, the forecast based on the latest available GDP data (i.e. the “temporarily best” measures) may be improved by combining preliminary official releases with one-step-ahead forecasts.

From a theoretical point of view, uncertainty over the demand for a firm�s product may not have clear effects on investments, because of the influence of a number of factors, such as the production technology and the amount of competition in the product market.Until now, a deeper investigation of the interplay of different factors in the temporal dimension has not been possible because the empirical research has been based on cross-section analysis. This omission makes biased estimates of the investment-uncertainty relationship likely.The aim of this paper is to extend the findings of the empirical literature by using a panel of Italian firms over the period 1996-2004, covering a complete business cycle. The availability of a panel of survey data on companies� investment plans, expected future sales and demand uncertainty allows us to account for unobservable individual firm differences, macroeconomic shocks and the temporal evolution of the investment-uncertainty relationship. A key finding of our paper concerns the role of the competition faced by Italian firms in 1996-2004. The gradual loss of market power experienced by Italian manufacturing firms along with the increasing flexibility of labour input may have weakened the negative effect of uncertainty on investment decisions. We show that, in repeated cross-section estimates, the omission of firm-specific effects together with the dynamic interplay described above, would have lead to misleading conclusions about the relevance of demand uncertainty in explaining investment decisions.

Quantitative information on the current state of the economy is crucial to economic policy-making and to early understanding of the economic situation, but the quarterly national account (NA) data for GDP in the euro area are released with a substantial delay. The aim of the paper is to examine the forecast ability of bridge models (BM) for GDP growth in the euro area. BM ‘bridge the gap’ between the information content of timely updated indicators and the delayed (but more complete) NA. In this paper, BM are estimated for aggregate GDP and components both area-wide and for the three main countries of the euro area. Their short-term (one- and two-quarter ahead) forecasting performance is assessed with respect to benchmark univariate/multivariate statistical models, and a small structural model. The paper shows that national BM fare better than benchmark models. In addition, euro area GDP and its components are more precisely predicted by aggregating national forecasts.

The paper examines the evolution of consumer confidence indices in Australia, Canada, France, Germany, Italy, Japan, the United Kingdom and the United States of America since the 1970s, by modelling them in a multivariate framework of common macroeconomic variables for each country. Results suggest that: (a) the main economic determinants of consumer confidence cannot be summarized only on the basis of some macroeconomic variables; (b) consumer confidence indices have some ability to forecast economic activity, provided that both their coincident nature is taken into account and that a number of data-coherent parameter restrictions are imposed. A number of analyses (both insample and out-of-sample) are devoted to assessing the robustness of previous findings.

Quantitative information on the current state of the economy is crucial to economic policy-making, but the quarterly national accounts data for GDP in the euro area are released with a significant delay. This paper presents alternative models for the real-time forecasting of euro area GDP and assesses their performance. We estimate univariate/multivariate statistical models, bridge models (systems of autoregressive distributed lags equations with indicators) and a small structural model. The models are estimated for aggregate GDP and components both area-wide and for the three main countries. They are estimated and tested for the period 1980-1999. Data from 1999 to 2001 are used to compare the forecasting ability, gauged by rolling-origin one-step-ahead errors.

The paper examines the evolution of consumer and business confidence indexes in France, Germany and Italy since the mid-eighties, using regressions of the indexes on a set of common macroeconomic variables for each country. Comparison of the results across agents (i.e. consumers and entrepreneurs in the same country) and across countries highlights some differences in behaviour that have emerged in the last fifteen years. In particular, the paper inquires into the causes of the recent break in the relationship between the consumer and business confidence indexes.

The creation of the Euro area has increased the importance of obtaining timely information about short-term changes in the area's real activity. In this paper we propose a number of alternative short term forecasting models, ranging from simple ARIMA models to more complex cointegrated VAR and conditional models, to forecast the index of industrial production in the euro area. A conditional error-correction model in which the aggregate index of industrial production for the area is explained by the US industrial production index and the business confidence index from the European Commission harmonised survey on manufacturing firms achieves the best score in terms of forecasting capacity.

Measures of potential output and the output gap are increasingly being developed and used to concisely quantify and monitor the risk of price accelerations stemming from rises in aggregate demand that are not met by a corresponding increase in supply. They often play a prominent role in the price determination mechanisms of macroeconometric models. In this paper we build a measure of potential private-sector value added for the Italian economy that is consistent with the capital accumulation process in the Banca d'Italia’s Quarterly Model — and more generally with the rest of the supply-side block of that model. More specifically, we exploit the fact that the investment function can be thought of as a relationship transforming desired gross additions to capacity output into capital accumulation by means of a conversion factor (the optimal capital/output ratio). Thus, if one removes the component of investment decisions that stems from changes in the relative price of the production factors (i.e., in the optimal capital/output ratio), then a measure of the desired gross addition to capacity may be constructed. The results draw a cyclical picture of the degree of capacity utilisation for the period 1970-1997 that is roughly in line with those produced by the Wharton and Hodrick-Prescott filter approaches, as well as with the pictures resulting from the ISAE, IMF, European Commission and OECD measures of the output gap. Our investment-function-based measure appears to be a promising indicator of the pressure exerted on prices by demand accelerations. Its empirical properties are, on the whole, acceptable and plausible.

We investigate the prediction of Italian industrial production. We first specify a model based on electricity consumption; we show that the cubic trend in such a model mostly captures the evolution over time of the electricity coefficient, which can be well approximated by a smooth transition model � la Terasvirta, with no gains in predictive power, though. We also analyze the performance of models based on data of different business surveys. According to basic statistics of forecasting accuracy, the linear energy-based model is not outperformed by any other single model, neither by a combination of forecasts. However, a more comprehensive set of evaluation criteria sheds light on the advantages of using the whole information available. Overall, the best forecasting performance is achieved by estimating a combined model which includes among regressors both energy consumption and survey data.