Nikolaos A. Krimpas · 2021
Αυτή η διατριβή παρουσιάζει και συνοψίζει μια ευέλικτη και πρόσφατα αναπτυγμένη στατιστική μέθοδο για την αξιολόγηση και ανάλυση μεγάλων συνόλων δεδομένων για ανάλυση χρονοσειρών. Τα Factor Models χρησιμοποιούνται ευρέως στη μακροοικονομική για να αποκαλύψουν τις σχέσεις μεταξύ των μεταβλητών και να εκμεταλλευτούν τις πληροφορίες που είναι διασκορπισμένες σε χιλιάδες οικονομικές μεταβλητές που είναι διαθέσιμες σήμερα. Τα μοντέλα VAR χρησιμοποιούνται ευρέως στη μακροοικονομική ανάλυση και οι εκτιμώμενοι παράγοντες μπορούν να εισαχθούν στο σύστημα VAR προκειμένου να επιτευχθεί μείωση διαστάσεων και να αποφευχθεί η έλλειψη μεταβλητής μεροληψίας.Το πρώτο μέρος αυτής της διατριβής ασχολείται με μια εκτενή βιβλιογραφική ανασκόπηση των μοντέλων παραγόντων και την ανάπτυξή τους μέσω των παραλλαγών που έχουν παρουσιαστεί στη διεθνή βιβλιογραφία. Ως εκ τούτου, αναπτύχθηκαν μοντέλα παραγόντων τόσο στο πεδίο του χρόνου όσο και στο πεδίο των συχνοτήτων, με βάση τις παραδοχές σχετικά με τη συμπεριφορά του ιδιοσυγκρατικού όρου (ακριβές και κατά προσέγγιση) και των δυναμικών παραγόντων που μπορεί να είναι στατικοί ή δυναμικοί. Επιπλέον, μια εκτεταμένη ανασκόπηση της εμπειρικής βιβλιογραφίας που χρησιμοποιεί μοντέλα Factor για την εκτίμηση της συμπεριφοράς του επιχειρηματικού κύκλου, της νομισματικής μετάδοσης, των δεικτών οικονομικής κατάστασης και των θεμάτων πρόβλεψης. Στην επόμενη ενότητα δίνεται ιδιαίτερη έμφαση στις τεχνικές εκτίμησης στον τομέα χρόνου και συχνότητας μαζί με τους αλγορίθμους εκτίμησης που παρέχονται. Στην τελευταία ενότητα χρησιμοποιείται ένα μοντέλο στατικού και δυναμικού παράγοντα στις ελληνικές σειρές πληθωρισμού για την αποσύνθεση του ελληνικού πληθωρισμού σε κοινά στοιχεία. Η ανάλυση στατικών παραγόντων αποκαλύπτει τις τιμές αγαθών που παρουσιάζουν κοινή μεταβλητότητα στην ελληνική οικονομία και υποδηλώνει την ανάγκη εκτίμησης ολοκληρωμένων μέτρων πληθωρισμού που θα χρησιμοποιηθούν για διαρθρωτική ανάλυση και πρόβλεψη. Επιπλέον, χρησιμοποιείται η ανάλυση δυναμικών παραγόντων προκειμένου να αποσυντεθεί ο Ελληνικός πληθωρισμός σε διάφορους δείκτες τιμών που αντιπροσωπεύουν βασικά μακροοικονομικά σοκ στην οικονομία. Παρέχεται προσεκτική εξέταση της συσχέτισης της μεταβλητότητας του πληθωρισμού με τους εκτιμώμενους δείκτες και διεξοδική εξέταση του σχετικού βάρους των αναμενόμενων νομισματικών κρίσεων με διάφορα μέτρα της νομισματικής πολιτικής.
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Christian Gouriéroux, Joann Jasiak · Econometric Reviews · 2001
This paper introduces nonlinear dynamic factor models for various applications related to risk analysis. Traditional factor models represent the dynamics of processes driven by movements of latent variables, called the factors. Our approach extends this setup by introducing factors defined as random dynamic parameters and stochastic autocorrelated simulators. This class of factor models can represent processes with time varying conditional mean, variance, skewness and excess kurtosis. Applications discussed in the paper include dynamic risk analysis, such as risk in price variations (models with stochastic mean and volatility), extreme risks (models with stochastic tails), risk on asset liquidity (stochastic volatility duration models), and moral hazard in insurance analysis. We propose estimation procedures for models with the marginal density of the series and factor dynamics parameterized by distinct subsets of parameters. Such a partitioning of the parameter vector found in many applications allows to simplify considerably statistical inference. We develop a two- stage Maximum Likelihood method, called the Finite Memory Maximum Likelihood, which is easy to implement in the presence of multiple factors. We also discuss simulation based estimation, testing, prediction and filtering.
Cited by 14
James H. Stock, Mark W. Watson · Oxford University Press eBooks · 2012
Abstract This article surveys work on a class of models, dynamic factor models (DFMs), that has received considerable attention in the past decade because of their ability to model simultaneously and consistently data sets in which the number of series exceeds the number of time series observations. The aim of this survey is to describe the key theoretical results, applications, and empirical findings in the recent literature on DFMs. The article is organized as follows. The first issue at hand for the econometrician is to estimate the factors and to ascertain how many factors there are; these two topics are covered in Sections 2 and 3. Once one has reliable estimates of the factors, there are a number of things one can do with them beyond using them for forecasting, including using them as instrumental variables, estimating factor-augmented vector autoregressions, and estimating dynamic stochastic general equilibrium models; these applications are covered in Section 4. Section 5 discusses some extensions.
Cited by 373
Eric Hillebrand, Siem Jan Koopman · Advances in econometrics · 2016
Cited by 2
Kathleen M. Gates, Sy‐Miin Chow, Peter C. M. Molenaar · 2023
Cited by 2
Zenide, Andrei · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · 2003
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Isabella Corazziari · Studies in classification, data analysis, and knowledge organization · 1999
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Serena Ng, Emanuel Moench, Simon Potter · 2008
This paper presents an approach to dynamic factor modeling in which variations can be idiosyncratic, block-specific, or common across blocks and units. Existing two level factor models do not usually account for variations at the block level which implies that these can be confounded with genuine common co-movements in the data. Specifying the block structure also facilitates interpretation of shocks to economic activity. Our approach is aimed at three types of applications: (i) decomposition of variance to assess the relative importance of different shocks, (ii) understanding how block-specific shocks impact overall economic activity, and (iii) monitoring and economic forecasting. We estimate a three level model for housing and find evidence of aggregate housing shocks, but these are small relative to common regional shocks and shocks to individual series within regions. We also estimate a model for real economic activity excluding housing. The 315 time series are organized into six blocks according to the timing of data releases. The results suggest that block-specific variations are important. Data released in the household survey component of the monthly employment situation report turn out to be mostly idiosyncratic and bear little information about the state of real economic activity. Finally, we estimate a four level model consisting of 402 series organized into an output, an employment, and a demand block that each are broken down into sub-blocks. According to this model, the level of real economic activity at the end of our sample in February 2008 was about 1.5 standard deviations below average, but still well above the level of 3 standard deviations below average reached at the trough of the recession in 2001.
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Joseph Francois · 2008
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Paolo Andreini, Cosimo Izzo, G. Ricco · arXiv (Cornell University) · 2020
A novel deep neural network framework -- that we refer to as Deep Dynamic Factor Model (D$^2$FM) --, is able to encode the information available, from hundreds of macroeconomic and financial time-series into a handful of unobserved latent states. While similar in spirit to traditional dynamic factor models (DFMs), differently from those, this new class of models allows for nonlinearities between factors and observables due to the autoencoder neural network structure. However, by design, the latent states of the model can still be interpreted as in a standard factor model. Both in a fully real-time out-of-sample nowcasting and forecasting exercise with US data and in a Monte Carlo experiment, the D$^2$FM improves over the performances of a state-of-the-art DFM.
Cited by 2Open access
Daniel Peña, Pilar Poncela · Journal of Statistical Planning and Inference · 2004
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John Leslie, Shayok Dutta, Amnay Amimeur · OakTrust (Texas A&M University Libraries) · 2014
This thesis identifies the current limitations of electronic remote authentication systems and presents a new remote authentication system that addresses these limitations. Examples of these limitations can be easily observed in everyday life. Some more common examples include: credit card theft, identity theft, insurance fraud and hacking of private computer networks. Our proposed solution includes a multi-factor protocol which has two key features. First, it dynamically updates private ID numbers such that no two iterations of the authentication protocol use the same set if private IDs for each involved party, using a True Random Number Generator (TRNG). This prevents any unauthorized access of private information, and even if this information is compromised, the authentication protocol is not compromised, since the subsequent iteration of authentication uses new IDs. Second, the protocol uses multiple authentication factors (two in our implementation), to further enhance security. These additional authentication factors are also dynamically updated after each iteration of the protocol. The protocol was implemented in a system which simulates a credit card transaction, highlighting the usefulness of our protocol in real world remote authentication. We expect this new electronic remote authentication system to solve many of the current failings of modern electronic authentication schemes.
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Sebastian Krantz, Rytis Bagdziunas · 2022
Efficient estimation of Dynamic Factor Models using the Expectation Maximization (EM) algorithm or Two-Step (2S) estimation, supporting datasets with missing data. Factors are assumed to follow a stationary VAR process of order p. The estimation options follow advances in the econometric literature: either running the Kalman Filter and Smoother once with initial values from PCA - 2S estimation as in Doz, Giannone and Reichlin (2011) <<a href="https://doi.org/10.1016%2Fj.jeconom.2011.02.012" target="_top">doi:10.1016/j.jeconom.2011.02.012</a>> - or via iterated Kalman Filtering and Smoothing until EM convergence - following Doz, Giannone and Reichlin (2012) <<a href="https://doi.org/10.1162%2FREST_a_00225" target="_top">doi:10.1162/REST_a_00225</a>> - or using the adapted EM algorithm of Banbura and Modugno (2014) <<a href="https://doi.org/10.1002%2Fjae.2306" target="_top">doi:10.1002/jae.2306</a>>, allowing arbitrary patterns of missing data. The implementation makes heavy use of the 'Armadillo' 'C++' library and the 'collapse' package, providing for particularly speedy estimation. A comprehensive set of methods supports interpretation and visualization of the model as well as forecasting. Information criteria to choose the number of factors are also provided - following Bai and Ng (2002) <<a href="https://doi.org/10.1111%2F1468-0262.00273" target="_top">doi:10.1111/1468-0262.00273</a>>.
Cited by 2Open access
Ahmet Şükrü Eti · Istanbul Technical University Academic Open Archive (Istanbul Technical University) · 1992
Tez (Yüksek Lisans) -- İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, 1992
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2015
Based on the Jones (1971) model, we construct two dynamic models of international trade in which the rate of time preference is either constant or time-varying. The main purpose is to study whether and under what conditions the results derived from the Jones model still hold in the dynamic framework. It is shown that the results of dynamic models may be similar or different to those obtained in the static model. For example, it is possible that, in both static and dynamic models, an increase in a commodity price raises this commodity’s output and the rate of return to the specific factor in this sector. However, the effect on the wage rate may be different due to the factor accumulation impact in the dynamic framework. JEL Classification: D90, F11
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Ken Jackson, Alex Kreinin, Wanhe Zhang · 2009
The Gaussian factor copula model is the market standard model for multi-name credit derivatives. Its main drawback is that factor copula models exhibit correlation smiles when calibrating against market tranche quotes. To overcome the calibration deficiency, we introduce a multi-period factor copula model by chaining one-period factor copula models. The correlation coefficients in our model are allowed to be timedependent, and hence they are allowed to follow certain stochastic processes. Therefore, we can calibrate against market quotes more consistently. Usually, multi-period factor copula models require multi-dimensional integration, typically computed by Monte Carlo simulation, which makes calibration extremely time consuming. In our model, the portfolio loss of a completely homogeneous pool possesses the Markov property, thus we can compute the portfolio loss distribution analytically without multi-dimensional integration. Numerical results demonstrate the efficiency and flexibility of our model to match market quotes.
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Elke Hahn, Elke Hahn · 2002
Since the second half of the nineties the euro area has been subject to a considerable accumulation of temporary and idiosyncratic price shocks. Core inflation indicators for the euro area are thus of utmost interest. Based on euro area-wide data core inflation in this paper is analyzed by means of an indicator derived from the generalized dynamic factor model. This indicator reveals that HICP inflation strongly exaggerated both the decline as well as the increase in the price trend in 1999 and 2000/2001. Our results reinforce those achieved by Cristadoro, Forni, Reichlin and Versonese (2001) based on euro area country data which indicates the robustness of the indicator.
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Giudice, Gianluca · London School of Economics and Political Science Research Online (London School of Economics and Political Science) · 2022
This thesis discusses latent variable models with the aim of uncovering hidden structure in multi-dimensional data. In rich data settings, dimension reduction has made latent variable methods, such as Dynamic Factor Models, extremely popular. Nonetheless, the dynamics of the factors have in most cases been modelled as independent and identically distributed (i.i.d.) white noise even though many financial and economic variables exhibit conditional heteroskedasticity, i.e., their variances conditional on the past evolve with time. This feature, modelled in the literature by GARCH models, has been applied to factor analysis either in the finite n case or by use of two-step estimators, producing inefficient results. In the first chapter, we show that when n ! 1, estimators for the latent factors and their conditional variance are, indeed, consistent. First, we convert the model in state-space form explicitly taking into account heteroskedasticity. Subsequently, we apply the Kalman filter to jointly estimate the parameter via the Expectation Conditional Maximization Either algorithm (ECME). This version of the EM replaces some of the steps which conditionally maximize the expected complete-data log-likelihood, with steps that maximize the real prediction-error likelihood, thus dealing with the lack of closed-form solution for the GARCH parameters. We then propose further modifications to the original model, introducing potential Dynamic Conditional Correlation (DCC) dynamics in the factors and a time-varying volatility for the observation equation disturbances. These extensions are subsequently assessed empirically, in the context of portfolio allocation and the economically relevant Growth at Risk (GaR). Finally, when the data dimension is limited, a Multi-Output Gaussian Process with Semiparametric Latent Factor structure can provide an extremely valuable opportunity to explore unobserved states in a multivariate setting. These non-linear models offer a novel and efficient approach to estimate the causal effect of interventions in time. As such, we analyse whether the early and intense vaccination campaign introduced in the UK affected the number of deaths and level of contagiousness of Covid-19 in the first semester of 2021.
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Yuefeng Leng (20678476), Chunlai Cui (20678479), Zhichao Jiang (4724856) · Figshare · 2025
In the mid-to-late stages, it gradually increases, which helps escape local optima and enables broader searches.
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Gabriele Fiorentini, Enrique Sentana · Journal of Applied Econometrics · 2018
Summary We derive computationally simple expressions for score tests of misspecification in parametric dynamic factor models using frequency domain techniques. We interpret those diagnostics as time domain moment tests which assess whether certain autocovariances of the smoothed latent variables match their theoretical values under the null of correct model specification. We also reinterpret reduced‐form residual tests as checking specific restrictions on structural parameters. Our Gaussian tests are robust to nonnormal, independent innovations. Monte Carlo exercises confirm the finite‐sample reliability and power of our proposals. Finally, we illustrate their empirical usefulness in an application that constructs a US coincident indicator.
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