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In this paper, we propose a time-varying parameter VAR model with stochastic volatility which allows for estimation on data sampled at different frequencies. Our contribution is twofold. First, we extend the methodology developed by Cogley and Sargent (2005), and Primiceri (2005), to a mixed-frequency setting. In particular, our approach allows for the inclusion of two different categories of variables (high-frequency and low-frequency) into the same time varying model. Second, we use this model to study the macroeconomic effects of government spending shocks in Italy over the 1988Q4-2013Q3 period. Italy - as well as most other euro area economies - is characterised by short quarterly time series for fiscal variables, whereas annual data are generally available for a longer sample before 1999. Our results show that the proposed time-varying mixed-frequency model improves on the performance of a simple linear interpolation model in generating the true path of the missing observations. Second, our empirical analysis suggests that government spending shocks tend to have positive effects on output in Italy. The fiscal multiplier, which is maximized at the one year horizon, follows a U-shape over the sample considered: it peaks at around 1.5 at the beginning of the sample, it then stabilizes between 0.8 and 0.9 from the mid-1990s to the late 2000s, before rising again to above unity during of the recent crisis.

This paper provides new insights into expectation-driven cycles by estimating a structural VAR with time-varying coefficients and stochastic volatility, as in Cogley and Sargent (2005) and Primiceri (2005). We use survey-based expectations of the unemployment rate to measure expectations of future developments in economic activity. We find that the effect of expectation shocks on the realized unemployment rate have been particularly large during the most recent recession. Unanticipated changes in expectations contributed to the gradual increase in the persistence of the unemployment rate and to the decline in the correlation between the inflation and the unemployment rate over time. Our results are robust to the introduction of financial variables in the model.

This paper provides new insights into expectation-driven cycles by estimating a structural VAR with time-varying coefficients and stochastic volatility, as in Cogley and Sargent (2005) and Primiceri (2005). We use survey-based expectations of the unemployment rate to measure expectations of future developments in economic activity. We find that the effect of expectation shocks on the realized unemployment rate have been particularly large during the most recent recession. Unanticipated changes in expectations contributed to the gradual increase in the persistence of the unemployment rate and to the decline in the correlation between the inflation and the unemployment rate over time. Our results are robust to the introduction of financial variables in the model. JEL Classification: C32, E24, E32

This paper analyses the transmission of financial shocks to the macroeconomy. The role of macro-financial linkages is investigated from an empirical perspective for the euro area as a whole, for individual euro area member countries and for other EU and OECD countries. The following key economic questions are addressed: 1) Which financial shocks have the largest impact on output over the full sample on average? 2) Are financial developments leading real activity? 3) Is there heterogeneity or a common pattern in macro-financial linkages across the euro area and do these linkages vary over time? 4) Do cross-country spillovers matter? 5) Is the transmission of financial shocks different during episodes of high stress than it is in normal times, i.e. is there evidence of non-linearities? In summary, it is found that real asset prices are significant leading indicators of real activity whereas the latter leads loan developments. Furthermore, evidence is presented that macro-financial linkages are heterogeneous across countries – despite persistent commonalities – and time-varying. Moreover, they differ between euro area and other countries. Results also indicate that cross-country spillovers matter. Finally, important non-linearities in the transmission of financial shocks are documented, as the evidence suggests that the transmission differs in episodes of high stress compared with normal times.

Against the background of the current debate about fiscal sustainability in several advanced economies, this paper estimates determinants of G7 sovereign bond spreads, using high‐frequency proxies for market expectations about macroeconomic fundamentals and allowing for time‐varying parameters. The paper finds substantial asymmetry in the importance of country fundamentals and considerable time variations in the pricing of risks. There has been a reduced pricing of several risk factors in the years preceding the financial crisis, and either an overpricing of risk or the pricing of a re‐denomination risk of euro area bonds during the European sovereign debt crisis.

Reliable and timely information about current economic conditions is crucial for policy makers and expectations formation. This paper demonstrates the efficacy of the Survey of Professional Forecasters (SPF) and the Purchasing Manager Indices (PMI) in anticipating US real economic activity. We conduct a fully-fledged real-time out-ofsample forecasting exercise linking these surveys to US GDP and industrial production growth over a long sample period. We find that both indicators convey valuable information for assessing current economic conditions. The SPF clearly outperforms the PMI in forecasting GDP growth, while it performs quite poorly in anticipating industrial production growth. Combining the information included in both surveys further improves the accuracy of both, the PMI and the SPF-based forecast. JEL Classification: E37, E47, C22, C53

Reliable and timely information about current economic conditions is crucial for policy makers and expectations formation. This paper demonstrates the efficacy of the Survey of Professional Forecasters (SPF) and the Purchasing Manager Indices (PMI) in anticipating US real economic activity. We conduct a fully-fledged real-time out-ofsample forecasting exercise linking these surveys to US GDP and industrial production growth over a long sample period. We find that both indicators convey valuable information for assessing current economic conditions. The SPF clearly outperforms the PMI in forecasting GDP growth, while it performs quite poorly in anticipating industrial production growth. Combining the information included in both surveys further improves the accuracy of both, the PMI and the SPF-based forecast.

This paper examines the out‐of‐sample forecast performance of sectoral stock market indicators for real GDP, private consumption and investment growth up to 4 quarters ahead in the US and the euro area. Our findings are that the predictive content of sectoral stock market indicators: i) is potentially strong, particularly for the financial sector, and is stronger than that of financial spreads; ii) varies over time, with a substantial improvement after 1999 for the euro area; iii) is stronger for investment than for private consumption; and iv) is stronger in the euro area than in the United States.

A number of academic studies suggest that from the mid-1990s onwards there were changes in the link between inflation and economic activity. However, it remains unclear the extent to which this phenomenon can be ascribed to a change in the structural relationship between inflation and output, as opposed to a change in the size and nature of the shocks hitting the economy. This paper uses a suite of models, such as time-varying VAR techniques, traditional macro models, as well as DSGE models, to investigate, for various European countries as well as for the euro area, the evolution of the link between inflation and resource utilization and its dependence on the nature and size of the shocks. Our analysis suggests that the relationship between inflation and activity has indeed been changing over time, while remaining positive, with the correlation peaking during recessions. Quantitatively, the link between output and inflation is found to be highly dependent on which type of shocks hit the economy: while, in general, all demand shocks to output imply a reaction of inflation of the same sign, the latter will be less pronounced when output fluctuations are driven by supply shocks. In addition, a sharp deceleration of activity, as opposed to a subdued but protracted slowdown, results in a swifter decline in inflation. Inflation exhibits a rather strong persistence, with a negative impact still visible three years after the initial shock.

The issue of forecast aggregation is to determine whether it is better to forecast a series directly or instead construct forecasts of its components and then sum these component forecasts. Notwithstanding some underlying theoretical results, it is generally accepted that forecast aggregation is an empirical issue. Empirical results in the literature often go unexplained. This leaves forecasters in the dark when confronted with the option of forecast aggregation. We take our empirical exercise a step further by considering the underlying issues in more detail. We analyse two price datasets, one for the United States and one for the Euro Area, which have distinctive dynamics and provide a guide to model choice. We also consider multiple levels of aggregation for each dataset. The models include an autoregressive model, a factor augmented autoregressive model, a large Bayesian VAR and a time-varying model with stochastic volatility. We find that once the appropriate model has been found, forecast aggregation can significantly improve forecast performance. These results are robust to the choice of data transformation.

The aim of this paper is to assess whether explicitly modeling structural change increases the accuracy of macroeconomic forecasts. We produce real time out-of-sample forecasts for inflation, the unemployment rate and the interest rate using a Time-Varying Coe±cients VAR with Stochastic Volatility (TV-VAR) for the US. The model generates accurate predictions for the three variables. In particular for inflation the TV-VAR outperforms, in terms of mean square forecast error, all the competing models: fixed coefficients VARs, Time-Varying ARs and the naaive random walk model. These results are also shown to hold over the most recent period in which it has been hard to forecast inflation., info:eu-repo/semantics/published

In this paper we present "now-casts" of Irish GDP using timely data from a panel data set of 41 different variables. The approach seeks to resolve two issues which commonly confront forecastors of GDP - how to parsimoniously avail of the many different series, which can potentially influence GDP and how to reconcile the within-quarterly release of many of these series with the quarterly estimates of GDP? The now-casts in this paper are generated by firstly, using dynamic factor analysis to extract a common factor from the panel data set and, secondly, through use of bridging equations to relate the monthly data to the quarterly GDP estimates. We conduct an out-of-sample forecasting simulation exercise, where the results of the now-casting exercise are compared with those of a standard benchmark model.

This paper evaluates how well sectoral stock prices forecast future economic activity compared to traditional predictors such as the term spread, dividend yield, exchange rates and money growth. The study is applied to euro area financial asset prices and real economic growth, covering the period 1973 to 2006. The paper finds that the term spread is the best predictor of future growth in the period leading up to the introduction of Monetary Union. After 1999, however, sectoral stock prices in general provide more accurate forecasts than traditional asset price measures across all forecast horizons. JEL Classification: C52, C53

We construct a measure of global liquidity using the growth rates of broad money for the G7 economies. Global liquidity produces forecasts of US inflation that are significantly more accurate than the forecasts based on US money growth, Phillips curve, autoregressive and moving average models. The marginal predictive power of global liquidity is strong at three years horizons. Results are robust to alternative measures of inflation.

Using data from the period 1970-1991, Romer and Romer (2000) showed that Federal Reserve forecasts of inflation and output were superior to those provided by commercial forecasters. In this paper, we show that this superior forecasting performance deteriorated after 1991. Over the decade 1992-2001, the superior forecast accuracy of the Fed held only over a very short time horizon and was limited to its forecasts of inflation. In addition, the performance of both the Fed and the commerical forecasters in predicting inflation and output, relative to that of "naive" benchmark models, dropped remarkably during this period.

Over the last years, the growing availability of large datasets and the improvements in the computational speed of computers have further fostered the research in the fields of both macroeconomic modeling and forecasting analysis. A primary focus of these research areas is to improve the models performance by exploiting the informational content of several time series. Increasing the dimension of macro models is indeed crucial for a detailed structural understanding of the economic environment, as well as for an accurate forecasting analysis. As consequence, a new generation of large-scale macro models, based on the micro-foundations of a fully specified dynamic stochastic general equilibrium set-up, has became one of the most flourishing research areas of interest both in central banks and academia. At the same time, there has been a revival of forecasting methods dealing with many predictors, such as the factor models. The central idea of factor models is to exploit co-movements among variables through a parsimonious econometric structure. Few underlying common shocks or factors explain most of the co-variations among variables. The unexplained component of series movements is on the other hand due to pure idiosyncratic dynamics. The generality of their framework allows factor models to be suitable for describing a broad variety of models in a macroeconomic and a financial context. The revival of factor models, over the recent years, comes from important developments achieved by Stock and Watson (2002) and Forni, Hallin, Lippi and Reichlin (2000). These authors find the conditions under which some data averages become collinear to the space spanned by the factors when, the cross section dimension, becomes large. Moreover, their factor specifications allow the idiosyncratic dynamics to be mildly cross-correlated (an effect referred to as the 'approximate factor structure' by Chamberlain and Rothschild, 1983), a situation empirically verified in many applications. These findings have relevant implications. The most important being that the use of a large number of series is no longer representative of a dimensional constraint. On the other hand, it does help to identify the factor space. This new generation of factor models has been applied in several areas of macroeconomics and finance as well as for policy evaluation. It is consequently very likely to become a milestone in the literature of forecasting methods using many predictors. This thesis contributes to the empirical literature on factor models by proposing four original applications. In the first chapter of this thesis, the generalized dynamic factor model of Forni et. al (2002) is employed to explore the predictive content of the asset returns in forecasting Consumer Price Index (CPI) inflation and the growth rate of Industrial Production (IP). The connection between stock markets and economic growth is well known. In the fundamental valuation of equity, the stock price is equal to the discounted future streams of expected dividends. Since the future dividends are related to future growth, a revision of prices, and hence returns, should signal movements in the future growth path. Though other important transmission channels, such as the Tobin's q theory (Tobin, 1969), the wealth effect as well as capital market imperfections, have been widely studied in this literature. I show that an aggregate index, such as the S&P500, could be misleading if used as a proxy for the informative content of the stock market as a whole. Despite the widespread wisdom of considering such index as a leading variable, only part of the assets included in the composition of the index has a leading behaviour with respect to the variables of interest. Its forecasting performance might be poor, leading to sceptical conclusions about the effectiveness of asset prices in forecasting macroeconomic variables. The main idea of the first essay is therefore to analyze the lead-lag structure of the assets composing the S&P500. The classification in leading, lagging and coincident variables is achieved by means of the cross correlation function cleaned of idiosyncratic noise and short run fluctuations. I assume that asset returns follow a factor structure. That is, they are the sum of two parts: a common part driven by few shocks common to all the assets and an idiosyncratic part, which is rather asset specific. The correlationfunction, computed on the common part of the series, is not affected by the assets' specific dynamics and should provide information only on the series driven by the same common factors. Once the leading series are identified, they are grouped within the economic sector they belong to. The predictive content that such aggregates have in forecasting IP growth and CPI inflation is then explored and compared with the forecasting power of the S&P500 composite index. The forecasting exercise is addressed in the following way: first, in an autoregressive (AR) model I choose the truncation lag that minimizes the Mean Square Forecast Error (MSFE) in 11 years out of sample simulations for 1, 6 and 12 steps ahead, both for the IP growth rate and the CPI inflation. Second, the S&P500 is added as an explanatory variable to the previous AR specification. I repeat the simulation exercise and find that there are very small improvements of the MSFE statistics. Third, averages of stock return leading series, in the respective sector, are added as additional explanatory variables in the benchmark regression. Remarkable improvements are achieved with respect to the benchmark specification especially for one year horizon forecast. Significant improvements are also achieved for the shorter forecast horizons, when the leading series of the technology and energy sectors are used. The second chapter of this thesis disentangles the sources of aggregate risk and measures the extent of co-movements in five European stock markets. Based on the static factor model of Stock and Watson (2002), it proposes a new method for measuring the impact of international, national and industry-specific shocks. The process of European economic and monetary integration with the advent of the EMU has been a central issue for investors and policy makers. During these years, the number of studies on the integration and linkages among European stock markets has increased enormously. Given their forward looking nature, stock prices are considered a key variable to use for establishing the developments in the economic and financial markets. Therefore, measuring the extent of co-movements between European stock markets has became, especially over the last years, one of the main concerns both for policy makers, who want to best shape their policy responses, and for investors who need to adapt their hedging strategies to the new political and economic environment. An optimal portfolio allocation strategy is based on a timely identification of the factors affecting asset returns. So far, literature dating back to Solnik (1974) identifies national factors as the main contributors to the co-variations among stock returns, with the industry factors playing a marginal role. The increasing financial and economic integration over the past years, fostered by the decline of trade barriers and a greater policy coordination, should have strongly reduced the importance of national factors and increased the importance of global determinants, such as industry determinants. However, somehow puzzling, recent studies demonstrated that countries sources are still very important and generally more important of the industry ones. This paper tries to cast some light on these conflicting results. The chapter proposes an econometric estimation strategy more flexible and suitable to disentangle and measure the impact of global and country factors. Results point to a declining influence of national determinants and to an increasing influence of the industries ones. The international influences remains the most important driving forces of excess returns. These findings overturn the results in the literature and have important implications for strategic portfolio allocation policies; they need to be revisited and adapted to the changed financial and economic scenario. The third chapter presents a new stylized fact which can be helpful for discriminating among alternative explanations of the U.S. macroeconomic stability. The main finding is that the fall in time series volatility is associated with a sizable decline, of the order of 30% on average, in the predictive accuracy of several widely used forecasting models, included the factor models proposed by Stock and Watson (2002). This pattern is not limited to the measures of inflation but also extends to several indicators of real economic activity and interest rates. The generalized fall in predictive ability after the mid-1980s is particularly pronounced for forecast horizons beyond one quarter. Furthermore, this empirical regularity is not simply specific to a single method, rather it is a common feature of all models including those used by public and private institutions. In particular, the forecasts for output and inflation of the Fed's Green book and the Survey of Professional Forecasters (SPF) are significantly more accurate than a random walk only before 1985. After this date, in contrast, the hypothesis of equal predictive ability between naive random walk forecasts and the predictions of those institutions is not rejected for all horizons, the only exception being the current quarter. The results of this chapter may also be of interest for the empirical literature on asymmetric information. Romer and Romer (2000), for instance, consider a sample ending in the early 1990s and find that the Fed produced more accurate forecasts of inflation and output compared to several commercial providers. The results imply that the informational advantage of the Fed and those private forecasters is in fact limited to the 1970s and the beginning of the 1980s. In contrast, during the last two decades no forecasting model is better than "tossing a coin" beyond the first quarter horizon, thereby implying that on average uninformed economic agents can effectively anticipate future macroeconomics developments. On the other hand, econometric models and economists' judgement are quite helpful for the forecasts over the very short horizon, that is relevant for conjunctural analysis. Moreover, the literature on forecasting methods, recently surveyed by Stock and Watson (2005), has devoted a great deal of attention towards identifying the best model for predicting inflation and output. The majority of studies however are based on full-sample periods. The main findings in the chapter reveal that most of the full sample predictability of U.S. macroeconomic series arises from the years before 1985. Long time series appearto attach a far larger weight on the earlier sub-sample, which is characterized by a larger volatility of inflation and output. Results also suggest that some caution should be used in evaluating the performance of alternative forecasting models on the basis of a pool of different sub-periods as full sample analysis are likely to miss parameter instability. The fourth chapter performs a detailed forecast comparison between the static factor model of Stock and Watson (2002) (SW) and the dynamic factor model of Forni et. al. (2005) (FHLR). It is not the first work in performing such an evaluation. Boivin and Ng (2005) focus on a very similar problem, while Stock and Watson (2005) compare the performances of a larger class of predictors. The SW and FHLR methods essentially differ in the computation of the forecast of the common component. In particular, they differ in the estimation of the factor space and in the way projections onto this space are performed. In SW, the factors are estimated by static Principal Components (PC) of the sample covariance matrix and the forecast of the common component is simply the projection of the predicted variable on the factors. FHLR propose efficiency improvements in two directions. First, they estimate the common factors based on Generalized Principal Components (GPC) in which observations are weighted according to their signal to noise ratio. Second, they impose the constraints implied by the dynamic factors structure when the variables of interest are projected on the common factors. Specifically, they take into account the leading and lagging relations across series by means of principal components in the frequency domain. This allows for an efficient aggregation of variables that may be out of phase. Whether these efficiency improvements are helpful to forecast in a finite sample is however an empirical question. Literature has not yet reached a consensus. On the one hand, Stock and Watson (2005) show that both methods perform similarly (although they focus on the weighting of the idiosyncratic and not on the dynamic restrictions), while Boivin and Ng (2005) show that SW's method largely outperforms the FHLR's and, in particular, conjecture that the dynamic restrictions implied by the method are harmful for the forecast accuracy of the model. This chapter tries to shed some new light on these conflicting results. Itfocuses on the Industrial Production index (IP) and the Consumer Price Index (CPI) and bases the evaluation on a simulated out-of sample forecasting exercise. The data set, borrowed from Stock and Watson (2002), consists of 146 monthly observations for the US economy. The data spans from 1959 to 1999. In order to isolate and evaluate specific characteristics of the methods, a procedure, where thetwo non-parametric approaches are nested in a common framework, is designed. In addition, for both versions of the factor model forecasts, the chapter studies the contribution of the idiosyncratic component to the forecast. Other non-core aspects of the model are also investigated: robustness with respect to the choice of the number of factors and variable transformations. Finally, the chapter performs a sub-sample performances of the factor based forecasts. The purpose of this exercise is to design an experiment for assessing the contribution of the core characteristics of different models to the forecasting performance and discussing auxiliary issues. Hopefully this may also serve as a guide for practitioners in the field. As in Stock and Watson (2005), results show that efficiency improvements due to the weighting of the idiosyncratic components do not lead to significant more accurate forecasts, but, in contrast to Boivin and Ng (2005), it is shown that the dynamic restrictions imposed by the procedure of Forni et al. (2005) are not harmful for predictability. The main conclusion is that the two methods have a similar performance and produce highly collinear forecasts., Doctorat en sciences économiques, Orientation économie, info:eu-repo/semantics/nonPublished

Using data from the period 1970-1991, Romer and Romer (2000) showed that Federal Reserve forecasts of inflation and output were superior to those provided by commercial forecasters. In this paper, we show that this superior forecasting performance deteriorated after 1991. Over the decade 1992-2001, the superior forecast accuracy of the Fed held only over a very short time horizon and was limited to its forecasts of inflation. In addition, the performance of both the Fed and the commercial forecasters in predicting inflation and output, relative to that of “naive” benchmark models, dropped remarkably during this period.

This paper compares the predictive ability of the factor models of Stock and Watson (2002) and Forni, Hallin, Lippi, and Reichlin (2005) using a "large" panel of US macroeconomic variables. We propose a nesting procedure of comparison that clarifies and partially overturns the results of similar exercises in the literature. As in Stock and Watson (2002), we find that effciency improvements due to the weighting of the idiosyncratic components do not lead to significant more accurate forecasts. In contrast to Boivin and Ng (2005), we show that the dynamic restrictions imposed by the procedure of Forni, Hallin, Lippi, and Reichlin (2005) are not harmful for predictability. Our main conclusion is that for the dataset at hand the two methods have a similar performance and produce highly collinear forecasts. JEL Classification: C31, C52, C53

This paper compares the predictive ability of the factor models of Stock and Watson (2002) and Forni, Hallin, Lippi, and Reichlin (2005) using a "large" panel of US macroeconomic variables. We propose a nesting procedure of comparison that clarifies and partially overturns the results of similar exercises in the literature. As in Stock and Watson (2002), we find that effciency improvements due to the weighting of the idiosyncratic components do not lead to significant more accurate forecasts. In contrast to Boivin and Ng (2005), we show that the dynamic restrictions imposed by the procedure of Forni, Hallin, Lippi, and Reichlin (2005) are not harmful for predictability. Our main conclusion is that for the dataset at hand the two methods have a similar performance and produce highly collinear forecasts.

This paper documents the structure, estimation and simulation properties of the Italian block of the ESCB-multi-country model (MCM). The model is used regularly as an input into Eurosystem projection exercises and, to a lesser extent, in simulation analysis. The specification of the Italian model follows closely that of the Area-Wide Model (AWM) and indeed the other MCM country blocks (in terms of specification and accounting framework). The MCM is a quarterly estimated structural macroeconomic model that treats the economy in a relatively closed manner. It has a long-run classical equilibrium with a vertical Phillips curve but with some short-run frictions in price/wage setting and factor demands. Consequently, activity is demand-determined in the short-run but supply-determined in the longer run with employment having converged to a level consistent with an exogenously given level of equilibrium unemployment. The precise properties of the model are illustrated using a number of standard variant simulations. JEL Classification: C3, C5, E1, E2

This paper investigates the determinants of the service sector employment share in the EU-15, for the aggregate service sector, four sub-sectors and twelve service sector branches. Recently, both Europe and the US have experienced an increase in the share of service-related jobs in total employment. Although converging in all European countries, a significant gap in the growth of service jobs in Europe relative to the US persists. Understanding the main factors behind this gap is key to achieving higher employment levels in Europe. This paper focuses on the role of barriers in the EU-15 which may have hindered its ability to absorb labour supply and therefore to adjust efficiently to the sectoral reallocation of labour. We find that a crucial role in this process has been played by the institutional framework affecting the setting up of new businesses and by the mismatch between workers’ skills and job vacancies.

This paper documents a new stylized fact of the greater macroeconomic stability of the U.S. economy over the last two decades. Using 131 monthly time series, three popular statistical methods and the forecasts of the Federal Reserve’s Greenbook and the Survey of Professional Forecasters, we show that the ability to predict several measures of inflation and real activity declined remarkably, relative to naive forecasts, since the mid-1980s. This break down in forecast ability appears to be an inherent feature of the most recent period and thus represents a new challenge for competing explanations of the ‘Great Moderation’.