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Tesi di dottorato in economia >
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http://hdl.handle.net/2108/691
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| DC Field | Value | Language |
|---|
| contributor.advisor | Liseo, Brunero | - |
| contributor.advisor | Proietti, Tommaso | - |
| contributor.advisor | Fiorentini, Gabriele | - |
| contributor.author | Macaro, Christian | - |
| date.accessioned | 2008-11-17T10:02:55Z | - |
| date.available | 2008-11-17T10:02:55Z | - |
| date.issued | 2008-11-17T10:02:55Z | - |
| identifier.uri | http://hdl.handle.net/2108/691 | - |
| description | 19. ciclo | en |
| description.abstract | The Impact of Vintage on the Persistence of Gross Domestic Product Shocks.
The first chapter of the thesis aims to demonstrate that the data revision process affects the persistence of gross domestic
product shocks.
The analysis is based on two alternative models, the Fractional Unit Root and the Linear Trend, and it benefits
from new semiparametric
procedures. The analysis of results seems to suggest that changes in the definition of the output
significantly affects the performance of the models which are typically used to study the GDP series. - Seasonality in HighFrequency
Data.
The second chapter of the thesis aims to study the intradaily
seasonal pattern of the Dow Jones volatility. An unobserved
component analysis, following the socalled
ModelBased
approach, is examined in order to separate the seasonal pattern
from the remaining long and short term components. The major novelty of the work is to explore the seasonal behavior of
the volatility as a stochastic component which evolves over time according to a specific ARMA model.
In more detail, high frequency data are used to recover 30minute
realized volatility measures. Particular attention
has been devoted to checking whether estimates were robust to market microstructure,
jumps and outliers.
The analysis of results emphasizes that the volatility of the Dow Jones is characterized by a stochastic seasonal
component which recalls the “Ushape”
pattern. - Bayesian Unobserved Components in Time Series. The third chapter of the thesis aims to present a full Bayesian framework to identify, extract and forecast unobserved
components in time series.
The major novelty of the approach is the definition of a probabilistic framework to analyze the identification
conditions. More precisely, informative prior distributions are assigned to the spectral densities of the unobserved
components. This entails a interesting feature: the possibility to analyze more than one decomposition at once by studying
the posterior distributions of the unobserved spectra. Particular attention is given to an empirical application where the
canonical decomposition of sunspot data is compared with some alternative decompositions.
The posterior distributions of the unobserved components are recovered by exploiting some recent developments
in the WienerKolmogorov
and circular process literature. An empirical application shows how to capture the seasonal
component in the volatility of financial high frequency data.
The posterior forecasting distributions are finally recovered by exploiting a relationship between spectral densities
and linear processes. An empirical application shows how to forecast seasonal adjusted financial time series.
Finally, a generalization of the BernsteinDirichlet
prior distribution is proposed in order to implement a frequencypass
spectral density estimator. - Objective Priors for AR(p) models. The fourth chapter of the thesis aims to derive objective prior distributions for general autoregressive models.
The core of the paper is based on the study of the Jeffreys' principle and on a particular adaptation of the
“reference” algorithm for dependent data. The analysis of stationarity turns out to be rather complicated for general
autoregressive processes. Therefore, two alternative parameterizations which respectively depend on the partial
autocorrelation functions (PACFs) and the roots are considered. For the causal and stationary parameter subspace, the
PACFs and the roots are always defined within the unit circle. This simplifies the derivation of the prior distributions.
Two main results are obtained. The first is a general formula which depends on the PACFs and represents the
Jeffreys' prior distribution for the causal subspace. The second result depends on the roots of the process and represents a
particular reference prior distribution which is asymptotically independent from the initial conditions and covers the noncausal
subspace. Ultimately, simulation results are presented for the autoregressive process of order two. | en |
| description.tableofcontents | 1. The Impact of Vintage on the Persistence of Gross Domestic Product Shocks - 2. Seasonality in HighFrequency Data. - 3. Bayesian Unobserved Components in Time Series. - 4. Objective Priors for AR(p) models | en |
| format.extent | 1881966 bytes | - |
| format.mimetype | application/pdf | - |
| language.iso | en | en |
| subject | GDP | en |
| subject | long memory | en |
| subject | unobserved components | en |
| subject | realized volatility | en |
| subject | BernsteinDirichlet | en |
| subject | BandPass | en |
| subject | Jeffreys Prior | en |
| subject | Reference Prior | en |
| subject | time series | en |
| subject.classification | SECS-P/05 Econometria | en |
| title | Topics on unobserved component detection for time series | en |
| type | Doctoral thesis | en |
| degree.name | Dottorato in econometria ed economia empirica | en |
| degree.level | Dottorato | en |
| degree.discipline | Facoltà di economia | en |
| degree.grantor | Università degli studi di Roma Tor Vergata | en |
| subject.jel | C22; Time-series models | en |
| subject.jel | C82; Methodology for collecting, estimating, and organizing macroeconomic data | en |
| subject.jel | E3; Prices, business fluctuations, and cycles | en |
| date.dateofdefense | A.A. 2006/2007 | en |
| Appears in Collections: | Tesi di dottorato in economia
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Size | Format |
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| tesi.pdf | | 1837Kb | Adobe PDF | View/Open |
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