Dissertations / Theses on the topic 'Utility theory Econometric models'

Chapter 1 surveys the econometric methodology of temporal aggregation for a wide range of univariate and multivariate time series models.

A unified overview of temporal aggregation techniques for this broad class of processes is presented in the first part of the chapter and the main results are summarized. In each case, assuming to know the underlying process at the disaggregate frequency, the aim is to find the appropriate model for the aggregated data. Additional topics concerning temporal aggregation of ARIMA-GARCH models (see Drost and Nijman, 1993) are discussed and several examples presented. Systematic sampling schemes are also reviewed.

Multivariate models, which show interesting features under temporal aggregation (Breitung and Swanson, 2002, Marcellino, 1999, Hafner, 2008), are examined in the second part of the chapter. In particular, the focus is on temporal aggregation of VARMA models and on the related concept of spurious instantaneous causality, which is not a time series property invariant to temporal aggregation. On the other hand, as pointed out by Marcellino (1999), other important time series features as cointegration and presence of unit roots are invariant to temporal aggregation and are not induced by it.

Some empirical applications based on macroeconomic and financial data illustrate all the techniques surveyed and the main results.

Chapter 2 is an attempt to monitor fiscal variables in the Euro area, building an early warning signal indicator for assessing the development of public finances in the short-run and exploiting the existence of monthly budgetary statistics from France, taken as "example country".

The application is conducted focusing on the cash State deficit, looking at components from the revenue and expenditure sides. For each component, monthly ARIMA models are estimated and then temporally aggregated to the annual frequency, as the policy makers are interested in yearly predictions.

The short-run forecasting exercises carried out for years 2002, 2003 and 2004 highlight the fact that the one-step-ahead predictions based on the temporally aggregated models generally outperform those delivered by standard monthly ARIMA modeling, as well as the official forecasts made available by the French government, for each of the eleven components and thus for the whole State deficit. More importantly, by the middle of the year, very accurate predictions for the current year are made available.

The proposed method could be extremely useful, providing policy makers with a valuable indicator when assessing the development of public finances in the short-run (one year horizon or even less).

Chapter 3 deals with the issue of forecasting contemporaneous time series aggregates. The performance of "aggregate" and "disaggregate" predictors in forecasting contemporaneously aggregated vector ARMA (VARMA) processes is compared. An aggregate predictor is built by forecasting directly the aggregate process, as it results from contemporaneous aggregation of the data generating vector process. A disaggregate predictor is a predictor obtained from aggregation of univariate forecasts for the individual components of the data generating vector process.

The econometric framework is broadly based on Lütkepohl (1987). The necessary and sufficient condition for the equality of mean squared errors associated with the two competing methods in the bivariate VMA(1) case is provided. It is argued that the condition of equality of predictors as stated in Lütkepohl (1987), although necessary and sufficient for the equality of the predictors, is sufficient (but not necessary) for the equality of mean squared errors.

Furthermore, it is shown that the same forecasting accuracy for the two predictors can be achieved using specific assumptions on the parameters of the VMA(1) structure.

Finally, an empirical application that involves the problem of forecasting the Italian monetary aggregate M1 on the basis of annual time series ranging from 1948 until 1998, prior to the creation of the European Economic and Monetary Union (EMU), is presented to show the relevance of the topic. In the empirical application, the framework is further generalized to deal with heteroskedastic and cross-correlated innovations.

Chapter 4 deals with a cointegration analysis applied to the empirical investigation of fiscal sustainability. The focus is on a particular country: Poland. The choice of Poland is not random. First, the motivation stems from the fact that fiscal sustainability is a central topic for most of the economies of Eastern Europe. Second, this is one of the first countries to start the transition process to a market economy (since 1989), providing a relatively favorable institutional setting within which to study fiscal sustainability (see Green, Holmes and Kowalski, 2001). The emphasis is on the feasibility of a permanent deficit in the long-run, meaning whether a government can continue to operate under its current fiscal policy indefinitely.

The empirical analysis to examine debt stabilization is made up by two steps.

First, a Bayesian methodology is applied to conduct inference about the cointegrating relationship between budget revenues and (inclusive of interest) expenditures and to select the cointegrating rank. This task is complicated by the conceptual difficulty linked to the choice of the prior distributions for the parameters relevant to the economic problem under study (Villani, 2005).

Second, Bayesian inference is applied to the estimation of the normalized cointegrating vector between budget revenues and expenditures. With a single cointegrating equation, some known results concerning the posterior density of the cointegrating vector may be used (see Bauwens, Lubrano and Richard, 1999).

The priors used in the paper leads to straightforward posterior calculations which can be easily performed.

Moreover, the posterior analysis leads to a careful assessment of the magnitude of the cointegrating vector. Finally, it is shown to what extent the likelihood of the data is important in revising the available prior information, relying on numerical integration techniques based on deterministic methods.

Doctorat en Sciences économiques et de gestion
info:eu-repo/semantics/nonPublished

In the widely used over-identified econometric model, the two-step Generalized Methods of Moments (GMM) estimator and inference, first suggested by Hansen (1982), require the estimation of optimal weighting matrix at the initial stages. For time series data and clustered dependent data, which is our focus here, the optimal weighting matrix is usually referred to as the long run variance (LRV) of the (scaled) sample moment conditions. To maintain generality and avoid misspecification, nowadays we do not model serial dependence and within-cluster dependence parametrically but use the heteroscedasticity and autocorrelation robust (HAR) variance estimator in standard practice. These estimators are nonparametric in nature with high variation in finite samples, but the conventional increasing smoothing asymptotics, so called small-bandwidth asymptotics, completely ignores the finite sample variation of the estimated GMM weighting matrix. As a consequence, empirical researchers are often in danger of making unreliable inferences and false assessments of the (efficient) two-step GMM methods. Motivated by this issue, my dissertation consists of three papers which explore the efficiency and approximation issues in the two-step GMM methods by developing new, more accurate, and easy-to-use approximations to the GMM weighting matrix.

The first chapter, "Simple and Trustworthy Cluster-Robust GMM Inference" explores new asymptotic theory for two-step GMM estimation and inference in the presence of clustered dependence. Clustering is a common phenomenon for many cross-sectional and panel data sets in applied economics, where individuals in the same cluster will be interdependent while those from different clusters are more likely to be independent. The core of new approximation scheme here is that we treat the number of clusters G fixed as the sample size increases. Under the new fixed-G asymptotics, the centered two-step GMM estimator and two continuously-updating estimators have the same asymptotic mixed normal distribution. Also, the t statistic, J statistic, as well as the trinity of two-step GMM statistics (QLR, LM and Wald) are all asymptotically pivotal, and each can be modified to have an asymptotic standard F distribution or t distribution. We also suggest a finite sample variance correction further to improve the accuracy of the F or t approximation. Our proposed asymptotic F and t tests are very appealing to practitioners, as test statistics are simple modifications of the usual test statistics, and the F or t critical values are readily available from standard statistical tables. We also apply our methods to an empirical study on the causal effect of access to domestic and international markets on household consumption in rural China.

The second paper "Should we go one step further? An Accurate Comparison of One-step and Two-step procedures in a Generalized Method of Moments Framework” (coauthored with Yixiao Sun) focuses on GMM procedure in time-series setting and provides an accurate comparison of one-step and two-step GMM procedures in a fixed-smoothing asymptotics framework. The theory developed in this paper shows that the two-step procedure outperforms the one-step method only when the benefit of using the optimal weighting matrix outweighs the cost of estimating it. We also provide clear guidance on how to choose a more efficient (or powerful) GMM estimator (or test) in practice.

While our fixed smoothing asymptotic theory accurately describes sampling distribution of two-step GMM test statistic, the limiting distribution of conventional GMM statistics is non-standard, and its critical values need to be simulated or approximated by standard distributions in practice. In the last chapter, "Asymptotic F and t Tests in an Efficient GMM Setting" (coauthored with Yixiao Sun), we propose a simple and easy-to-implement modification to the trinity (QLM, LM, and Wald) of two-step GMM statistics and show that the modified test statistics are all asymptotically F distributed under the fixed-smoothing asymptotics. The modification is multiplicative and only involves the J statistic for testing over-identifying restrictions. In fact, what we propose can be regarded as the multiplicative variance correction for two-step GMM statistics that takes into account the additional asymptotic variance term under the fixed-smoothing asymptotics. The results in this paper can be immediately generalized to the GMM setting in the presence of clustered dependence.

Accurate estimation of alcohol price elasticity is important for policy analysis – e.g.., determining optimal taxes and projecting revenues generated from proposed tax changes. Several approaches to specifying and estimating the price elasticity of demand for alcohol can be found in the literature. There are two keys to policy-relevant specification and estimation of alcohol price elasticity. First, the underlying demand model should take account of alcohol consumption decisions at the extensive margin – i.e., individuals’ decisions to drink or not – because the price of alcohol may impact the drinking initiation decision and one’s decision to drink is likely to be structurally different from how much they drink if they decide to do so (the intensive margin). Secondly, the modeling of alcohol demand elasticity should yield both theoretical and empirical results that are causally interpretable. The elasticity estimates obtained from the existing two-part model takes into account the extensive margin, but are not causally interpretable.

The elasticity estimates obtained using aggregate-level models, however, are causally interpretable, but do not explicitly take into account the extensive margin. There currently exists no specification and estimation method for alcohol price elasticity that both accommodates the extensive margin and is causally interpretable. I explore additional sources of bias in the extant approaches to elasticity specification and estimation: 1) the use of logged (vs. nominal) alcohol prices; and 2) implementation of unnecessarily restrictive assumptions underlying the conventional two-part model. I propose a new approach to elasticity specification and estimation that covers the two key requirements for policy relevance and remedies all such biases. I find evidence of substantial divergence between the new and extant methods using both simulated and the real data. Such differences are profound when placed in the context of alcohol tax revenue generation.