Dissertations / Theses on the topic 'Mortality – Forecasting – Mathematical models'

central debates in empirical macroeconomic modeling.

Chapter 1, entitled “A Model for Real-Time Data Assessment with an Application to GDP Growth Rates”, provides a model for the data

revisions of macroeconomic variables that distinguishes between rational expectation updates and noise corrections. Thus, the model encompasses the two polar views regarding the publication process of statistical agencies: noise versus news. Most of the studies previous studies that analyze data revisions are based

on the classical noise and news regression approach introduced by Mankiew, Runkle and Shapiro (1984). The problem is that the statistical tests available do not formulate both extreme hypotheses as collectively exhaustive, as recognized by Aruoba (2008). That is, it would be possible to reject or accept both of them simultaneously. In turn, the model for the

DPP presented here allows for the simultaneous presence of both noise and news. While the “regression approach” followed by Faust et al. (2005), along the lines of Mankiew et al. (1984), identifies noise in the preliminary

figures, it is not possible for them to quantify it, as done by our model.

The second and third chapters acknowledge the possibility that macroeconomic data is measured with errors, but the approach followed to model the missmeasurement is extremely stylized and does not capture the complexity of the revision process that we describe in the first chapter.

Chapter 2, entitled “Revisiting the Success of the RBC model”, proposes the use of dynamic factor models as an alternative to the VAR based tools for the empirical validation of dynamic stochastic general equilibrium (DSGE) theories. Along the lines of Giannone et al. (2006), we use the state-space parameterisation of the factor models proposed by Forni et al. (2007) as a competitive benchmark that is able to capture weak statistical restrictions that DSGE models impose on the data. Our empirical illustration compares the out-of-sample forecasting performance of a simple RBC model augmented with a serially correlated noise component against several specifications belonging to classes of dynamic factor and VAR models. Although the performance of the RBC model is comparable

to that of the reduced form models, a formal test of predictive accuracy reveals that the weak restrictions are more useful at forecasting than the strong behavioral assumptions imposed by the microfoundations in the model economy.

The last chapter, “What are Shocks Capturing in DSGE modeling”, contributes to current debates on the use and interpretation of larger DSGE

models. Recent tendency in academic work and at central banks is to develop and estimate large DSGE models for policy analysis and forecasting. These models typically have many shocks (e.g. Smets and Wouters, 2003 and Adolfson, Laseen, Linde and Villani, 2005). On the other hand, empirical studies point out that few large shocks are sufficient to capture the covariance structure of macro data (Giannone, Reichlin and

Sala, 2005, Uhlig, 2004). In this Chapter, we propose to reconcile both views by considering an alternative DSGE estimation approach which

models explicitly the statistical agency along the lines of Sargent (1989). This enables us to distinguish whether the exogenous shocks in DSGE

modeling are structural or instead serve the purpose of fitting the data in presence of misspecification and measurement problems. When applied to the original Smets and Wouters (2007) model, we find that the explanatory power of the structural shocks decreases at high frequencies. This allows us to back out a smoother measure of the natural output gap than that

resulting from the original specification.
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The first Chapter, entitled “An area wide real time data base for the euro area” and coauthored with Domenico Giannone, Jerome Henry and Magda Lalik, describes how we constructed a real time database for the euro area covering more than 200 series regularly published in the European Central Bank Monthly Bulletin, as made available ahead of publication to the Governing Council members before their first meeting of the month.

Recent research has emphasised that the data revisions can be large for certain indicators and can have a bearing on the decisions made, as well as affect the assessment of their relevance. It is therefore key to be in a position to reconstruct the historical environment of economic decisions at the time they were made by private agents and policy-makers rather than using the data as they become available some years later. For this purpose, it is necessary to have the information in the form of all the different vintages of data as they were published in real time, the so-called "real-time data" that reflect the economic situation at a given point in time when models are estimated or policy decisions made.

We describe the database in details and study the properties of the euro area real-time data flow and data revisions, also providing comparisons with the United States and Japan. We finally illustrate how such revisions can contribute to the uncertainty surrounding key macroeconomic ratios and the NAIRU.

The second Chapter entitled “Maximum likelihood estimation of large factor model on datasets with arbitrary pattern of missing data” is based on a joint work with Marta Banbura. It proposes a methodology for the estimation of factor models on large cross-sections with a general pattern of missing data. In contrast to Giannone et al (2008), we can handle datasets that are not only characterised by a 'ragged edge', but can include e.g. mixed frequency or short history indicators. The latter is particularly relevant for the euro area or other young economies, for which many series have been compiled only since recently. We adopt the maximum likelihood approach, which, apart from the flexibility with regard to the pattern of missing data, is also more efficient and allows imposing restrictions on the parameters. It has been shown by Doz et al (2006) to be consistent, robust and computationally feasible also in the case of large cross-sections. To circumvent the computational complexity of a direct likelihood maximisation in the case of large cross-section, Doz et al (2006) propose to use the iterative Expectation-Maximisation (EM) algorithm. Our contribution is to modify the EM steps to the case of missing data and to show how to augment the model in order to account for the serial correlation of the idiosyncratic component. In addition, we derive the link between the unexpected part of a data release and the forecast revision and illustrate how this can be used to understand the sources of the latter in the case of simultaneous releases.

We use this methodology for short-term forecasting and backdating of the euro area GDP on the basis of a large panel of monthly and quarterly data.

The third Chapter is entitled “Nowcasting Inflation Using High Frequency Data” and it proposes a methodology for nowcasting and forecasting inflation using data with sampling frequency higher than monthly. In particular, this Chapter focuses on the energy component of inflation given the availability of data like the Weekly Oil Bulletin Price Statistics for the euro area, the Weekly Retail Gasoline and Diesel Prices for the US and the daily spot and future prices of crude oil.

Although nowcasting inflation is a novel idea, there is a rather long literature focusing on nowcasting GDP. The use of higher frequency indicators in order to Nowcast/Forecast lower frequency indicators had started with monthly data for GDP. GDP is a quarterly variable released with a substantial time delay (e.g. two months after the end of the reference quarter for the euro area GDP).

The estimation adopts the methodology described in Chapter 2, modeling the data as a trading day frequency factor model with missing observations in a state space representation. In contrast to other procedures, the methodology proposed models all the data within a unified single framework that allows one to produce forecasts of all the involved variables from a factor model, which, by definition, does not suffer from overparametrisation. Moreover, this offers the possibility to disentangle model-based "news" from each release and then to assess their impact on the forecast revision. The Chapter provides an illustrative example of this procedure, focusing on a specific month.

In order to assess the importance of using high frequency data for forecasting inflation this Chapter compares the forecast performance of the univariate models, i.e. random walk and autoregressive process, with the forecast performance of the model that uses weekly and daily data. The provided empirical evidence shows that exploiting high frequency data relative to oil not only let us nowcast and forecast the energy component of inflation with a precision twice better than the proposed benchmarks, but we obtain a similar improvement even for total inflation.

The fourth Chapter entitled “The forecasting power of international yield curve linkages”, coauthored with Kleopatra Nikolaou, investigates dependency patterns between the yield curves of Germany and the US, by using an out-of-sample forecast exercise.

The motivation for this Chapter stems from the fact that our up to date knowledge on dependency patterns among yields curves of different countries is limited. Looking at the yield curve literature, the empirical evidence to-date informs us of strong contemporaneous interdependencies of yield curves across countries, in line with increased globalization and financial integration. Nevertheless, this yield curve literature does not investigate non-contemporaneous correlations. And yet, clear indication in favour of such dependency patterns is recorded in studies focusing on specific interest rates, which look at the role of certain countries as global players (see Frankel et al. (2004), Chinn and Frankel (2005) and Wang et al. (2007)). Evidence from these studies suggests a leading role for the US. Moreover, dependency patterns recorded in the real business cycles between the US and the euro area (Giannone and Reichlin, 2007) can also rationalize such linkages, to the extent that output affects nominal interest rates.

We propose, estimate and forecast (out-of-sample) a novel dynamic factor model for the yield curve, where dynamic information from foreign yield curves is introduced into domestic yield curve forecasts. This is the International Dependency Model (IDM). We want to compare the yield curve forecast under the IDM versus a purely domestic model and a model that allows for contemporaneous common global factors. These models serve as useful comparisons. The domestic model bears direct modeling links with IDM, as it can be seen as a nested model of IDM. The global model bears less direct links in terms of modeling, but, in line with IDM, it is also an international model that serves to highlight the advantages of introducing international information in yield curve forecasts. However, the global model aims to identify contemporaneous linkages in the yield curve of the two countries, whereas the IDM also allows for detecting dependency patterns.

Our results that shocks appear to be diffused in a rather asymmetric manner across the two countries. Namely, we find a unidirectional causality effect that runs from the US to Germany. This effect is stronger in the last ten years, where out-of-sample forecasts of Germany using the US information are even more accurate than the random walk forecasts. Our statistical results demonstrate a more independent role for the US.
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