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In this paper we present an empirically stable money demand model for Euro area M3. We show that housing wealth is an important explanatory variable of long-run money demand that captures the trending behaviour of M3 velocity, in particular its shift in the first half of this decade. We show that the current financial crisis has no impact on the stability of our money demand model.

Factor based forecasting has been at the forefront of developments in the macroeconometric forecasting literature in the recent past. Despite the flurry of activity in the area, a number of specification issues such as the choice of the number of factors in the forecasting regression, the benefits of combining factor-based forecasts and the choice of the dataset from which to extract the factors remain partly unaddressed. This paper provides a comprehensive empirical investigation of these issues using data for the euro area, the six largest euro area countries, and the UK.

This paper investigates the dynamics of aggregate wages and prices in the United States (US) and the Euro Area (EA) with a special focus on persistence of real wages, wage and price inflation. The analysis is conducted within a structural vector error-correction model, where the structural shocks are identified using the long-run properties of the theoretical model, as well as the cointegrating properties of the estimated system. Overall, in the long run, wage and price inflation emerge as more persistent in the EA than in the US in the face of import price, unemployment, or permanent productivity shocks. This finding is robust to the changes in the sample period and in the models’ specifications entertained in the paper.

This paper introduces the concepts of time-specific weak and strong cross section dependence. A double- indexed process is said to be cross sectionally weakly dependent at a given point in time, t, if its weighted average along the cross section dimension (N) converges to its expectation in quadratic mean, as N is increased without bounds for all weights that satisfy certain ‘granularity’ conditions. Relationship with the notions of weak and strong common factors is investigated and an application to the estimation of panel data models with an infinite number of weak factors and a finite number of strong factors is also considered. The paper concludes with a set of Monte Carlo experiments where the small sample properties of estimators based on principal components and CCE estimators are investigated and compared under various assumptions on the nature of the unobserved common effects.

This study presents empirical evidence on the long-run motives for holding euro area money by focusing on the role of equity and labour markets. Equity positively affects money demand through wealth effects, as equities are a significant store of household wealth and thus part of a financial transaction motive. Negative substitution effects through the expected return on equity reflect a speculative motive from the equity market. A precautionary motive from the labour market is captured by the annual change in the unemployment rate. The main conclusion is that equity and labour markets do matter for money. All three new elements, in particular housing and financial wealth, have been found statistically and economically significant in explaining M3 since 1983. These findings are robust across different proxies for the augmented motives and a shorter sample period starting in 1994.

Forecasting the world economy is a difficult task given the complex interrelationships within and across countries. This paper proposes a number of approaches to forecast short-term changes in selected world economic variables and aims, first, at ranking various forecasting methods in terms of forecast accuracy and, second, at checking whether methods forecasting di- rectly aggregate variables (direct approaches)out-perform methods based on the aggregation of country- specific forecasts (bottom-up approaches). Overall, all methods perform better than a simple benchmark for short horizons (up to three months ahead). Among the forecasting approaches used, factor models appear to perform the best. Moreover, direct approaches out-perform bottom-up ones for real variables, but not for prices. Finally, when country-specific forecasts are adjusted to match direct forecasts at the aggregate levels (top-down approaches), the forecast accuracy is neither improved nor deteriorated (i.e. top-down and bottom-up approaches are broadly equivalent in terms of country-specific forecast accuracy).

Over the last two centuries, the cross-spectral coherence between either narrow or broad money growth and inflation at the frequency ω=0 has exhibited little variation–being, most of the time, close to one–in the U.S., the U.K., and several other countries, thus implying that the fraction of inflation’s long-run variation explained by long-run money growth has been very high and relatively stable. The cross-spectral gain at ω=0, on the other hand, has exhibited significant changes, being for long periods of time smaller than one. The unitary gain associated with the quantity theory of money appeared in correspondence with the inflationary outbursts associated with World War I and the Great Inflation–but not World War II–whereas following the disinflation of the early 1980s the gain dropped below one for all the countries and all the monetary aggregates I consider, with one single exception. I propose an interpretation for this pattern of variation based on the combination of systematic velocity shocks and infrequent inflationary outbursts. Based on estimated DSGE models, I show that velocity shocks cause, ceteris paribus, comparatively much larger decreases in the gain between money growth and inflation at ω=0 than in the coherence, thus implying that monetary regimes characterised by low and stable inflation exhibit a low gain, but a still comparatively high coherence. Infrequent inflationary outbursts, on the other hand, boost both the gain and coherence towards one, thus temporarily revealing the one-for-one correlation between money growth and inflation associated with the quantity theory of money, which would otherwise remain hidden in the data.

This paper tests the expectations hypothesis (EH) of the term structure of interest rates in US data, using spectral regression techniques that allow us to consider different frequency bands. We find a positive relation between the term spread and the change in the long-term interest rate in a frequency band of 6 months to 4 years, whereas the relation is negative at higher and lower frequencies. We confirm that the variance of term premia relative to expected changes in long-term interest rates dominates at high and low frequencies, leading the EH to be rejected in those bands but not in the intermediate frequency band.

We evaluate residual projection strategies in the context of a large-scale macro model of the euro area and smaller benchmark time-series models. The exercises attempt to measure the accuracy of model-based forecasts simulated both out-of-sample and in-sample. Both exercises incorporate alternative residual-projection methods, to assess the importance of unaccounted-for breaks in forecast accuracy and off-model judgment. Conclusions reached are that simple mechanical residual adjustments have a significant impact of forecasting accuracy irrespective of the model in use, ostensibly due to the presence of breaks in trends in the data. The testing procedure and conclusions are applicable to a wide class of models and thus of general interest.

In this study we combine clustering techniques with a moving window algorithm in order to filter financial market data outliers. We apply the algorithm to a set of financial market data which consists of 25 series selected from a larger dataset using a cluster analysis technique taking into account the daily behaviour of the market; each of these series is an element of a cluster that represents a different segment of the market. We set up a framework of possible algorithm parameter combinations that detect most of the outliers by market segment. In addition, the algorithm parameters that have been found can also be used to detect outliers in other series with similar economic behaviour in the same cluster. Moreover, the crosschecking of the behaviour of different series within each cluster reduces the possibility of observations being misclassified as outliers.

We estimate and forecast growth in euro area monthly GDP and its components from a dynamic factor model due to Doz et al. (2005), which handles unbalanced data sets in an efficient way. We extend the model to integrate interpolation and forecasting together with cross-equation accounting identities. A pseudo real-time forecasting exercise indicates that the model outperforms various benchmarks, such as quarterly time series models and bridge equations in forecasting growth in quarterly GDP and its components.

Testing and estimating the rank of a matrix of estimated parameters is key in a large variety of econometric modelling scenarios. This paper describes general methods to test for and estimate the rank of a matrix, and provides details on a variety of modelling scenarios in the econometrics literature where such methods are required. Four different methods to test the true rank of a general matrix are described, as well as one method that can handle the case of a matrix subject to parameter constraints associated with defineteness structures. The technical requirements for the implementation of the tests of rank of a general matrix differ and hence there are merits to all of them that justify their use in applied work. Nonetheless, we review available evidence of their small sample properties in the context of different modelling scenarios where all, or some, are applicable.

In a globalised world economy, global factors have become increasingly important to explain trade flows at the expense of country-specific determinants. This paper shows empirically the superiority of direct forecasting methods, in which world trade is directly forecasted at the aggregate levels, relative to "bottom-up" approaches, where world trade results from an aggregation of country-specific forecasts. Factor models in particular prove rather accurate, where the factors summarise large-scale datasets relevant in the determination of trade flows.

This paper attempts to extract market expectations about the Japanese economy and the BOJ’s policy stance from the yen yield curves augmented by money market interest rates, during the period from the end of the quantitative easing policy in March 2006. We use (i) the swap yield curves augmented by OIS interest rates (OIS/Swap), and (ii) the JGB yield curve augmented by FB/TB interest rates. First, using the Nelson-Siegel [1987] model, we estimate three latent dynamic factors, which can be interpreted as reflecting market expectations. Second, we investigate the relative importance of price discovery for each factor between OIS/Swap and FBTB/JGB, and find that the former has a more dominant role of price discovery for all factors. Third, we estimate the efficient price for each factor common to both yield curves using a time-series structural model, which enables us to decompose each factor into the efficient price and idiosyncratic factor.

Several factor-based models are estimated to investigate the role of country-specific trade and survey data in forecasting euro area manufacturing production. Following Boivin and Ng (2006), the emphasis is put on the role of dataset selection on the empirical performance of factor models. First, spectral analysis is used to assess the information content for euro area manufacturing production of external trade and surveys data of the three largest economies as well as two medium-sized highly opened economies. Second, common factors are estimated on four datasets, following two methodologies, Stock and Watson (2002a, 2002b) and Forni et al. (2005). Third, a rolling out of sample forecast comparison exercise is carried out on nine models. Compared to univariate benchmarks, our results are supportive of factor-based models up to two quarters. They show that incorporating survey and external trade information improves the forecast of manufacturing production. They also confirm the findings of Marcellino, Stock and Watson (2003) that, using country information, it is possible to improve forecasts for the euro area. Interesting, the medium-sized highly opened economies provide valuable information to monitor area wide developments, beyond their weight in the aggregate. Conversely, the large countries do not add much to the monitoring of the aggregate, when considered separately.

This paper explores the role of model and vintage combination in forecasting, with a novel approach that exploits the information contained in the revision history of a given variable. We analyse the forecast performance of eleven widely used models to predict inflation and GDP growth, in the three dimensions of accuracy, uncertainty and stability by using the real-time data set for macroeconomists developed at the Federal Reserve Bank of Philadelphia. Instead of following the common practice of investigating only therelationship between first available and fully revised data, we analyse the entire revision history for each variable and extract a signal from the entire distribution of vintages of a given variable to improve forecast accuracy and precision. The novelty of our study relies on the interpretation of the vintages of a real time data base as related realizations or units of a panel data set. The results suggest that imposing appropriate weights on competing models of inflation forecasts and output growth — reflecting the relative ability each model has over different sub-sample periods — substantially increases the forecast performance. More interestingly, our results indicate that augmenting the information set with a signal extracted from all available vintages of time-series consistently leads to a substantial improvement in forecast accuracy, precision and stability.

In this paper we compare alternative approaches for the construction of time series of macroeconomic variables for Unified Germany prior to 1991, and then use them for the construction of corresponding time series for the euro area. The resulting series for Germany and the euro area are compared with existing ones on the basis of both descriptive statistics and results of econometric analyses conducted with the alternative time series. We find that more sophisticated time series methods for backdating can yield sizeable gains.

Using a Bayesian likelihood approach, we estimate a dynamic stochastic general equilibrium model for the US economy using seven macro-economic time series. The model incorporates many types of real and nominal frictions and seven types of structural shocks. We show that this model is able to compete with Bayesian Vector Autoregression models in out-of-sample prediction. We investigate the relative empirical importance of the various frictions. Finally, using the estimated model we address a number of key issues in business cycle analysis: What are the sources of business cycle fluctuations? Can the model explain the cross-correlation between output and inflation? What are the effects of productivity on hours worked? What are the sources of the “Great Moderation”?

Is maximum likelihood suitable for factor models in large cross-sections of time series? We answer this question from both an asymptotic and an empirical perspective. We show that estimates of the common factors based on maximum likelihood are consistent for the size of the cross-section (n) and the sample size (T) going to infinity along any path of n and T and that therefore maximum likelihood is viable for n large. The estimator is robust to misspecification of the cross-sectional and time series correlation of the the idiosyncratic components. In practice, the estimator can be easily implemented using the Kalman smoother and the EM algorithm as in traditional factor analysis., info:eu-repo/semantics/published

This paper elaborates on the alternative measure of persistence recently suggested in Marques (2004), which is based on the idea of mean reversion. A formal distinction between the “unconditional probability of a given process not crossing its mean in period t” and its estimator, is made clear and the relationship between this new measure and the widely used “sum of the autoregressive coefficients”, as alternative measures of persistence, is investigated. Using the law of large numbers and the central limit theorem, properties for the estimator of the new measure of persistence are established, which allow tests of hypotheses to be performed, under very general conditions. Finally, some Monte Carlo experiments are conducted in order to compare the finite sample properties of the estimator for the “unconditional probability of a given process not crossing its mean in period t” and the OLS estimator for the “sum of the autoregressive coefficients”.

We propose the method of eigenvalue filtering as a new tool to extract time series subcomponents (such as business-cycle or irregular) defined by properties of the underlying eigenvalues. We logically extend the Beveridge-Nelson decomposition of the VAR time-series models focusing on the transient component. We introduce the canonical state-space representation of the VAR models to facilitate this type of analysis. We illustrate the eigenvalue filtering by examining a stylized model of inflation determination estimated on the Czech data.We characterize the estimated components of CPI, WPI and import inflations, together with the real production wage and real output, survey their basic properties, and impose an identification scheme to calculate the structural innovations. We test the results in a simple bootstrap simulation experiment. We find two major areas for further research: first, verifying and improving the robustness of the method, and second, exploring the method’s potential for empirical validation of structural economic models.

The paper analyses whether communication and actual interventions in FX markets are successful in moving exchange rates over the medium- to long-run. It compares empirical evidence based on time-series analysis with that obtained from an eventstudy approach. Both the time-series approach based on option contracts and the event-study methodology yield compelling evidence that communication and actual interventions tend to be successful in moving exchange rates in the desired direction contemporaneously as well as over the medium- to long term. This finding is consistent with recent work on microstructure models that emphasises the importance of dynamic effects of news and fundamentals on exchange rates.

This paper investigates whether output and inflation respond asymmetrically to credit shocks in the euro area. The methodology, based on a non-linear VAR system, follows work by Balke (2000) for the US. The results reveal evidence of threshold effects related to credit conditions in the economy. Consistent with this finding, the impulse responses show some signs of asymmetric responses over the lending cycle.