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Research Interests: Economics, Econometrics, Structural Stability, Monetary Policy, Instability, and 15 moreScientific, Vector Autoregression, Spectral analysis, Central Banking, Sample Size, Publication, Local stability, E, C, VAR model, Autoregressive model, Structural Breaks, dynamic model, Econometric Model, and Structural Break
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Research Interests: Mathematics, Economics, Econometrics, Mathematical Economics, Applied Economics, and 13 moreLow Frequency, Time series analysis, Social Science Research Network, Marginal Models, ARIMA Models, Autoregressive Integrated Moving Average, Economic Modelling, Univariate Analysis, VAR model, Autoregressive model, Arima Model, Finance and Investment Banking, and Autoregressive Moving Average Model
Cubadda, G., & Guardabascio, B. (2009). On the use of partial least squares regression for forecasting large sets of cointegrated time series.. In Statistical methods for the analysis of large data-sets. (pp.371-374). Padova : CLEUP.... more
Cubadda, G., & Guardabascio, B. (2009). On the use of partial least squares regression for forecasting large sets of cointegrated time series.. In Statistical methods for the analysis of large data-sets. (pp.371-374). Padova : CLEUP. ... Tutti i documenti archiviati in ART sono ...
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Research Interests: Mathematics, Econometrics, Time Series, Inference, Applied Economics, and 14 moreDimension Reduction, Statistical Inference, Social Science Research Network, Regression, LAG, Empirical Research, Regression Model, Economic Modelling, Partial Least Square, Autoregressive model, Principal Component Regression, Prediction Method, Autoregressive Distributed lag, and Finance and Investment Banking
This paper extends the notion of common cycles to quarterly time series having unit roots both at the zero and seasonal frequencies. It is shown that common cycles are present in the HyllebergEngleGrangerYoo decomposition of these... more
This paper extends the notion of common cycles to quarterly time series having unit roots both at the zero and seasonal frequencies. It is shown that common cycles are present in the HyllebergEngleGrangerYoo decomposition of these series when there exists a linear ...
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Università degli Studi di Roma Tor Vergata. ...
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ABSTRACT This paper proposes a strategy to detect the presence of common serial cor-relation in large-dimensional systems. We show that partial least squares can be used to consistently recover the common autocorrelation space. Moreover,... more
ABSTRACT This paper proposes a strategy to detect the presence of common serial cor-relation in large-dimensional systems. We show that partial least squares can be used to consistently recover the common autocorrelation space. Moreover, a Monte Carlo study reveals that ...
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This paper extends the multivariate index autoregressive model by Reinsel (1983) to the case of cointegrated time series of order (1,1). In this new modelling, namely the Vector Error-Correction Index Model (VECIM), the first differences... more
This paper extends the multivariate index autoregressive model by Reinsel (1983) to the case of cointegrated time series of order (1,1). In this new modelling, namely the Vector Error-Correction Index Model (VECIM), the first differences of series are driven by some linear combinations of the variables, namely the indexes. When the indexes are significantly fewer than the variables, the VECIM achieves a substantial dimension reduction w.r.t. the Vector Error Correction Model. We show that the VECIM allows one to decompose the reduced form errors into sets of common and uncommon shocks, and that the former can be further decomposed into permanent and transitory shocks. Moreover, we offer a switching algorithm for optimal estimation of the VECIM. Finally, we document the practical value of the proposed approach by both simulations and an empirical application, where we search for the shocks that drive the aggregate fluctuations at different frequency bands in the US.
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This paper proposes concepts and methods to investigate whether the bubble patterns observed in individual time series are common among them. Having established the conditions under which common bubbles are present within the class of... more
This paper proposes concepts and methods to investigate whether the bubble patterns observed in individual time series are common among them. Having established the conditions under which common bubbles are present within the class of mixed causal–noncausal vector autoregressive models, we suggest statistical tools to detect the common locally explosive dynamics in a Student t-distribution maximum likelihood framework. The performances of both likelihood ratio tests and information criteria were investigated in a Monte Carlo study. Finally, we evaluated the practical value of our approach via an empirical application on three commodity prices.
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Many economic variables feature changes in their conditional mean and volatility, and Time Varying Vector Autoregressive Models are often used to handle such complexity in the data. Unfortunately, when the number of series grows, they... more
Many economic variables feature changes in their conditional mean and volatility, and Time Varying Vector Autoregressive Models are often used to handle such complexity in the data. Unfortunately, when the number of series grows, they present increasing estimation and interpretation problems. This paper tries to address this issue proposing a new Multivariate Autoregressive Index model that features time varying means and volatility. Technically, we develop a new estimation methodology that mix switching algorithms with the forgetting factors strategy of Koop and Korobilis (2012). This substantially reduces the computational burden and allows to select or weight, in real time, the number of common components and other features of the data using Dynamic Model Selection or Dynamic Model Averaging without further computational cost. Using USA macroeconomic data, we provide a structural analysis and a forecasting exercise that demonstrates the feasibility and usefulness of this new mode...
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Seasonality is an important feature which is observed in most economic time series. This article turns back to some common practice when dealing with that "undesirable" component in common feature analyses. To be clear from the... more
Seasonality is an important feature which is observed in most economic time series. This article turns back to some common practice when dealing with that "undesirable" component in common feature analyses. To be clear from the outset, our understanding of co-movements in seasonal time series is twofold. We consider both the impact of seasonality on the detection of other forms of co-movements and the presence of seasonal co-movements per se. The structure of the paper is as follows. We first remind the consequences for short-run comovements and consequently for business cycle analyses of using seasonally adjusted (SA) variables instead of raw data. Indeed, although Cubadda (1999) and Hecq (1998) have both theoretically and empirically explained the damages of such filtering procedures, most studies use seasonally adjusted data in common cycles analyses. Then we review some models which propose to detect the presence of cyclical co-movements in seasonal time series (Cubadd...
Visual inspection of time series suggests that the usual assumptions of linearity and stationarity may often be violated in practice. Indeed, macroeconomic time series usually display strong trending behaviors, financial time series are... more
Visual inspection of time series suggests that the usual assumptions of linearity and stationarity may often be violated in practice. Indeed, macroeconomic time series usually display strong trending behaviors, financial time series are characterized by unstable second moments, asymmetry is often present in ecological time series, and so on. Modeling such dynamic characteristics represents an hard challenge for modern time series analysis. The purpose of this paper is twofold. First, we review some of the most important theoretical and practical issues arising in the statistical analysis of univariate non linear time series, focusing on the connections between this approach and that based on the theory of chaos. In our opinion, the advantages of studying non linear time series from this perspective, already discussed in the seminal work by Tong (1995), has not yet been fully explored in the statistical literature. Second, we reconsider some recent developments in the analysis of non...
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This article aims to decompose a large dimensional vector autoregressive (VAR) model into two components, the first one being generated by a small-scale VAR and the second one being a white noise. Hence, a reduced number of common... more
This article aims to decompose a large dimensional vector autoregressive (VAR) model into two components, the first one being generated by a small-scale VAR and the second one being a white noise. Hence, a reduced number of common components generates the entire dynamics of the large system through a VAR structure. This modelling, which we label as the dimension-reducible VAR, extends the common feature approach to high-dimensional systems, and it differs from the dynamic factor model in which the idiosyncratic component can also embed a dynamic pattern. We show the conditions under which this decomposition exists. We provide statistical tools to detect its presence in the data and to estimate the parameters of the underlying small-scale VAR model. Based on our methodology, we propose a novel approach to identify the shock that is responsible for most of the common variability at the business cycle frequencies. We evaluate the practical value of the proposed methods by simulations as well as by an empirical application to a large set of US economic variables.
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We examine the conditions under which each individual series that is generated by a vector autoregressive model can be represented as an autoregressive model that is augmented with the lags of few linear combinations of all the variables... more
We examine the conditions under which each individual series that is generated by a vector autoregressive model can be represented as an autoregressive model that is augmented with the lags of few linear combinations of all the variables in the system. We call this modelling Multivariate Index-Augmented Autoregression (MIAAR). We show that the parameters of the MIAAR can be estimated by a switching algorithm that increases the Gaussian likelihood at each iteration. Since maximum likelihood estimation may perform poorly when the number of parameters gets larger, we propose a regularized version of our algorithm to handle a medium-large number of time series. We illustrate the usefulness of the MIAAR modelling both by empirical applications and simulations.
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Research Interests: Mathematics, Economics, Econometrics, Time Series, Applied Economics, and 12 moreDimension Reduction, Statistical Inference, Social Science Research Network, Partial Least Squares, Empirical Research, Regression Model, Economic Modelling, Partial Least Square, Principal Component Regression, Prediction Method, Autoregressive Distributed lag, and Finance and Investment Banking
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Combining series with the aim to obtain an indicator for business cycle analyses is an im-portant issue for policy makers for instance. In this area, econometric techniques usually rely on small systems (VAR or VECM) or at the other... more
Combining series with the aim to obtain an indicator for business cycle analyses is an im-portant issue for policy makers for instance. In this area, econometric techniques usually rely on small systems (VAR or VECM) or at the other extreme on models for a very large number of ...
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We analyze herein the importance of four types of shocks in contributing to the business cycles of the G7 economies. After disentangling the common permanent and transitory shocks in the G7 outputs, we identify the domestic and foreign... more
We analyze herein the importance of four types of shocks in contributing to the business cycles of the G7 economies. After disentangling the common permanent and transitory shocks in the G7 outputs, we identify the domestic and foreign components of such shocks for each country. This provides us with quite a flexible palette for understanding the degree of openness of the G7 countries, useful information for the analysis of the strengths and weaknesses of each national economy. Our empirical analysis reveals that the cycles of most of the G7 outputs are dominated by their domestic components and that the foreign components are almost entirely due to permanent shocks.
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The analysis of the factors determining different growth rates and convergence across geographical areas has been a major theme in recent years in the regional analysis of economic research. Generally, the debate has been focused on... more
The analysis of the factors determining different growth rates and convergence across geographical areas has been a major theme in recent years in the regional analysis of economic research. Generally, the debate has been focused on factual predictions, of different specification, of regional growth models, based on yearly data, using a cross-section approach and per capita output (regional GDP) (Barro and Sala-i-Martin, 1992). However, the attention paid by economists to analysing growth and convergence across areas does not represent the only useful analysis that may be worth pursuing on the dynamic properties of different areas. National and aggregated economic analysis does not contribute to shaping comovements in different regions and interest in local dynamics can be motivated in several ways. It is well-known that permanent change in output and employment results from unexpected shocks to the economy, and that any economic change over time can be a result of the trend-cycle a...
This paper aims to decompose a large dimensional vector autoregessive (VAR) model into two components, the first one being generated by a small-scale VAR and the second one being a white noise sequence. Hence, a reduced number of common... more
This paper aims to decompose a large dimensional vector autoregessive (VAR) model into two components, the first one being generated by a small-scale VAR and the second one being a white noise sequence. Hence, a reduced number of common components generates the entire dynamics of the large system through a VAR structure. This modelling, which we label as the dimension-reducible VAR, extends the common feature approach to high dimensional systems, and it differs from the dynamic factor model in which the idiosyncratic component can also embed a dynamic pattern. We show the conditions under which this decomposition exists. We provide statistical tools to detect its presence in the data and to estimate the parameters of the underlying small-scale VAR model. Based on our methodology, we propose a novel approach to identify the shock that is responsible for most of the common variability at the business cycle frequencies. We evaluate the practical value of the proposed methods by simulations as well as by an empirical application to a large set of US economic variables.
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This paper explores the possibility of cointegration existing between processes integrated at di¤erent frequencies. Using the demodulator operator, we show that such cointegration can exist and explore its form using both complex-and... more
This paper explores the possibility of cointegration existing between processes integrated at di¤erent frequencies. Using the demodulator operator, we show that such cointegration can exist and explore its form using both complex-and real-valued representations. A straightforward approach to test for the presence of cointegration between processes integrated at di¤erent frequencies is proposed, with a Monte Carol study and an application showing that the testing approach works well.
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This paper aims to decompose a large dimensional vector autoregessive (VAR) model into two components, the first one being generated by a small-scale VAR and the second one being a white noise sequence. Hence, a reduced number of common... more
This paper aims to decompose a large dimensional vector autoregessive (VAR) model into two components, the first one being generated by a small-scale VAR and the second one being a white noise sequence. Hence, a reduced number of common factors generates the entire dynamics of the large system through a VAR structure. This modelling extends the common feature approach to high dimensional systems, and it differs from the dynamic factor model in which the idiosyncratic component can also embed a dynamic pattern. We show the conditions under which this decomposition exists. We provide statistical tools to detect its presence in the data and to estimate the parameters of the underlying small-scale VAR model. Based on our methodology, we propose a novel approach to identify the shock that is responsible for most of the common variability at the business cycle frequencies. We evaluate the practical value of the proposed methods by simulations as well as by an empirical application to a large set of US economic variables.
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This paper compares the forecasting performances of both univariate and multivariate models for realized volatilities series. We consider realized volatility measures of the returns of 13 major banks traded in the NYSE. Since our... more
This paper compares the forecasting performances of both univariate and multivariate models for realized volatilities series. We consider realized volatility measures of the returns of 13 major banks traded in the NYSE. Since our variables are characterized by the presence of long range dependence, we use several modelling approaches that are able to capture such feature. We look at the forecasting accuracy of the considered models to make inference on the underlying mechanism that has generated volatilities of the assets. Our main conclusion is that the contagion effect among the considered volatilities is small or, at least, not well captured by the considered multivariate models
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This paper introduces the notion of common noncausal features and proposes tools to detect them in multivariate time series models. We argue that the existence of co-movements might not be detected using the conventional stationary vector... more
This paper introduces the notion of common noncausal features and proposes tools to detect them in multivariate time series models. We argue that the existence of co-movements might not be detected using the conventional stationary vector autoregressive (VAR) model as the common dynamics are present in the noncausal (i.e. forward-looking) component of the series. In particular, we show that the presence of a reduced rank structure allows to identify purely causal and noncausal
VAR processes of order two and higher even in the Gaussian likelihood framework. Hence, usual test statistics and canonical correlation analysis can still be applied, where both lags and leads are used as instruments to determine whether the common features are present in either the backward-or forward-looking dynamics of the series. The proposed definitions of co-movements also valid for the mixed causal-noncausal VAR, with the exception that an approximate non-Gaussian maximum likelihood estimator is necessary for these cases. This means however that one loses the benefits of the simple tools proposed in this paper. An empirical analysis on European Brent and U.S. West Texas Intermediate oil prices illustrates the main findings. Whereas we fail to find any short run co-movements in a conventional causal VAR, they are detected in the growth rates of the series when considering a purely noncausal VAR.
VAR processes of order two and higher even in the Gaussian likelihood framework. Hence, usual test statistics and canonical correlation analysis can still be applied, where both lags and leads are used as instruments to determine whether the common features are present in either the backward-or forward-looking dynamics of the series. The proposed definitions of co-movements also valid for the mixed causal-noncausal VAR, with the exception that an approximate non-Gaussian maximum likelihood estimator is necessary for these cases. This means however that one loses the benefits of the simple tools proposed in this paper. An empirical analysis on European Brent and U.S. West Texas Intermediate oil prices illustrates the main findings. Whereas we fail to find any short run co-movements in a conventional causal VAR, they are detected in the growth rates of the series when considering a purely noncausal VAR.