Essays on Bayesian Analysis of Time Varying Economic Patterns

__Abstract__ Knowing the history of your topic of interest is important: It teaches what happened in the past, helps to understand the present, and allows one to look ahead in the future. Given my interest in the development of Bayesian econometrics, this thesis starts with a description of its history since the early 1960s. My aim is to quantify the increasing popularity of Bayesian econometrics by performing a data analysis in the sense of measuring both publication and citation records in major journals. This will give a concrete idea about where Bayesian econometrics came from and in which journals its papers appeared. With this information, one will be able to predict some future patterns. Indeed, the analysis indicates that Bayesian econometrics has a bright future. I also look at how the topics and authors of the papers in the data set are connected to each other using the bibliometric mapping technique. This analysis gives insight in the most important topics examined in the Bayesian econometrics literature. Among these, I find that a topic like unobserved components models and time varying patterns has shown tremendous progress. Finally, I explore some issues and debates about Bayesian econometrics. Given that the analysis of time varying patterns has become an important topic, I explore this issue in the following two chapters. The subject of Chapter 3 is twofold. First, I give a basic exposition of the technical issues that a Bayesian econometrician faces in terms of modeling and inference when she is interested in forecasting US real GDP growth by using a time varying parameter model using simulation based Bayesian inference. Having observed particular time varying patterns in the level and volatility of the series, I propose a time varying parameter model that incorporates both level shifts and stochastic volatility components. I further try to explain the GDP growth series using survey data on expectations. Doing posterior and predictive analyses, the forecasting performances of several models are compared. The results of this chapter may become an input for more policy oriented models on growth and stability. In addition to output growth stability, price stability is also an important policy objective. Both households and businesses are interested in the behavior of prices over time and follow the decisions of policymakers in order to be able to make sound decisions. Moreover, policymakers are interested in making inflation forecasts to be able to make sound policy decisions and guide households and businesses. Therefore, inflation forecasting is important for everybody. I deal with this topic in Chapter 4. In this chapter, I explore forecasting of US inflation via the class of New Keynesian Phillips Curve (NKPC) models using original data. I propose various extended versions of the NKPC models and make a comparative study based on posterior and predictive analyses. I also show results from using models that are misspecified and from using survey inflation expectations data. The latter is done since most macroeconomic series do not contain strong data evidence on typical patterns and using survey data may help strengthening the information in the likelihood. The results indicate that inflation forecasts are better described by the proposed class of extended NKPC models and this information may be useful for policies such as inflation targeting. Section 1.2 summarizes the contributions of this thesis. Section 1.3 presents an outline of the thesis and summarizes each chapter

Bayesian Forecasting of US Growth using Basic Time Varying Parameter Models and Expectations Data

__Abstract__ Time varying patterns in US growth are analyzed using various univariate model structures, starting from a naive model structure where all features change every period to a model where the slow variation in the conditional mean and changes in the conditional variance are specified together with their interaction, including survey data on expected growth in order to strengthen the information in the model. Use is made of a simulation based Bayesian inferential method to determine the forecasting performance of the various model specifications. The extension of a basic growth model with a constant mean to models including time variation in the mean and variance requires careful investigation of possible identification issues of the parameters and existence conditions of the posterior under a diffuse prior. The use of diffuse priors leads to a focus on the likelihood fu nction and it enables a researcher and policy adviser to evaluate the scientific information contained in model and data. Empirical results indicate that incorporating time variation in mean growth rates as well as in volatility are important in order to improve for the predictive performances of growth models. Furthermore, using data information on growth expectations is important for forecasting growth in specific periods, such as the the recession periods around 2000s and around 2008

Posterior-Predictive Evidence on US Inflation using Extended Phillips Curve Models with Non-filtered Data

Changing time series properties of US inflation and economic activity, measured as marginal costs, are modeled within a set of extended Phillips Curve (PC) models. It is shown that mechanical removal or modeling of simple low frequency movements in the data may yield poor predictive results which depend on the model specification used. Basic PC models are extended to include structural time series models that describe typical time varying patterns in levels and volatilities. Forward as well as backward looking expectation mechanisms for inflation are incorporated and their relative importance evaluated. Survey data on expected inflation are introduced to strengthen the information in the likelihood. Use is made of simulation based Bayesian techniques for the empirical analysis. No credible evidence is found on endogeneity and long run stability between inflation and marginal costs. Backward-looking inflation appears stronger than forward-looking one. Levels and volatilities of inflation are estimated more precisely using rich PC models. Estimated inflation expectations track nicely the observed long run inflation from the survey data. The extended PC structures compare favorably with existing basic Bayesian Vector Autoregressive and Stochastic Volatility models in terms of fit and prediction. Tails of the complete predictive distributions indicate an increase in the probability of disinflation in recent years