Download or read book Three Essays in Macroeconomic Forecasting Using Bayesian Model Selection written by Dimitris Korompilis-Magkas and published by . This book was released on 2010 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis explores several aspects of Bayesian model selection in time series forecasting of macroeconomic variables. The contribution is provided in three essays. In the first essay (Chapter 2) I forecast quarterly US inflation based on the generalized Phillips curve using econometric methods which incorporate dynamic model averaging. These methods not only allow for coefficients to change over time, but also for the entire forecasting model to change over time. I find that dynamic model averaging leads to substantial forecasting improvements over simple benchmark regressions and more sophisticated approaches such as those using time varying coefficient models. I also provide evidence on which sets of predictors are relevant for forecasting in each period. In the second essay (Chapter 3) I address the issue of improving the forecasting performance of vector autoregressions (VARs) when the set of available predictors is inconveniently large to handle with methods and diagnostics used in traditional small-scale models. First, I summarize available information from a large dataset into a considerably smaller set of variables through factors estimated using standard principal components. However, even in the case of reducing the dimension of the data the true number of factors may still be large. For that reason I introduce in my analysis simple and efficient Bayesian model selction methods. I conduct model estimation and selection of predictors automatically through a stochastic search variable selection (SSVS) algorithm which requires minimal input by the user. I apply these methods to forecast 8 main U.S. macroeconomic variables using 124 potential predictors. I find improved out of sample fit in high dimensional specifications that would otherwise suffer from the proliferation of parameters. Finally, in the third essay (Chapter 4) I develop methods for automatic selection of variables in forecasting Bayesian vector autoregressions (VARs) using the Gibbs sampler. In particular, I extend the algorithms of Chapter 3 and provide computationally efficient algorithms for stochastic variable selection in generic (linear and nonlinear) VARs. The performance of the proposed variable selection method is assessed in a small Monte Carlo experiment, and in forecasting four short macroeconmic series for the UK using time-varying parameters vector autoregressions (TVP-VARs). I find that restricted models consistently improve upon their unrestricted counterparts in forecasting, showing the merits of variable selection in selecting parsimonious models.

Download or read book Essays in Forecasting written by Nii Ayi Christian Armah and published by . This book was released on 2009 with total page 123 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation comprises three essays in macroeconomic forecasting. The first essay discusses model selection and predictive accuracy tests in the context of parameter and model uncertainty under recursive and rolling estimation schemes. Particular emphasis is placed on the construction of valid bootstrap procedures for calculating the impact of parameter estimation error on the class of test statistics with limiting distributions that are functionals of Gaussian processes. Results of an empirical investigation of the marginal predictive content of money for income are also presented. The second essay outlines a number of approaches to the selection of factor proxies (observed variables that proxy unobserved estimated factors) using statistics based on large sample datasets. This approach to factor proxy selection is examined via a small Monte Carlo experiment and a set of prediction experiments, where evidence supporting our proposed methodology is presented. The third essay compares the predictive content of a set of macroeconomic indicators with that of various other observable variables that act as proxies to factors constructed using diffusion index methodology. The analysis suggests that certain spreads constructed as the difference between short or long term debt instruments and the federal funds rate are found to be useful indicators. Surprisingly, traditional spreads, such as the yield curve slope and the reverse yield gap are not found to provide additional predictive power.

Download or read book Three Essays on Macroeconomic Forecasting written by Mark Duffield Hutson and published by . This book was released on 2015 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Essays in Macroeconomic Forecasting with Linear and Non linear Time Series Models Using Bayesian Techniques written by Georgios Kontogeorgos and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Three Essays in Macroeconomic Forecasting Using Dimensionality Reduction Methods written by Yu Guo and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Three Essays in Macroeconomics and Monetary Economics Using Bayesian Multivariate Smooth Transition Approaches written by Fang Ge and published by . This book was released on 2009 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Essays on Belief Updating Forecasting and Robust Policy Making Based on Macroeconomic Variables written by Yizhou Kuang and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation consists of three essays that delve into the intersection of econometrics and macroeconomics. The essays employ econometric tools to investigate various topics related to macroeconomic forecasting and policy-making. The first essay aims to help policy-makers conduct robust inference on parameters that may suffer identification issues from DSGE models, and perform reliable counterfactual analysis based on available macroeconomic indicators. The second essay from a non-structural perspective, explores how to optimally forecast these variables in real-time utilizing available macroeconomic variables under model uncertainty. The last essay looks at Survey of Professional Forecasters and studies how agents update their beliefs based on common and private signals during business cycles.The first chapter introduces a new algorithm to conduct robust Bayesian estimation and inference in dynamic stochastic general equilibrium models. The algorithm combines standard Bayesian methods with an equivalence characterization of model solutions. This algorithm allows researchers to perform the following analysis: First, find the complete range of posterior means of both the deep parameters and any parameters of interest robust to the choice of priors in a sense I make precise. Second, derive the robust Bayesian credible region for these parameters. I prove the validity of this algorithm and apply this method to the models in Cochrane (2011) and An and Schorfheide (2007) to achieve robust estimations for structural parameters and impulse responses. In addition, I conduct a sensitivity analysis of optimal monetary policy rules with respect to the choice of priors and provide bounds to the optimal Taylor rule parameters.In the second chapter, my coauthors Yongmiao Hong, Yuying Sun and I focus on real-time monitoring of economic activities, also known as nowcasting. Nowcasting can be particularly challenging in the era of Big Data because it requires the management of a substantial amount of time series data that exhibit different frequencies and release dates. In this paper, we propose a novel now-casting strategy that utilizes dynamic factor models, which we call leave-b-out forward validation model averaging with penalization (LboFVMA). We demonstrate that the selected weight converges asymptotically to an optimal and consistent estimator, even in cases where all candidate models are misspecified. Further-more, the proposed estimator is consistent and follows an asymptotic Gaussian distribution if the true model is included among the candidate models. Our simulation results demonstrate that the LboFVMA approach performs well, as it generates low mean square forecast errors. This highlights its effectiveness and accuracy in the field of nowcasting.In the third chapter, my coauthors Nathan Mislang, Kristoffer Nimark and I propose a method to empirically decompose a cross-section of observed belief revisions into components driven by private and common signals under weak assumptions. We define a common signal as the single signal that if observed by all agents can explain the maximum amount of belief revisions across agents. Private signals are defined to explain the residual belief revisions unaccounted for by the common signal. When applied to probability forecasts from the Survey of Professional Forecasters we find that private signals account for more of the observed belief revisions than common signals. There is a large cross-sectional heterogeneity in signal precision across forecasters, with about 1/2 of them observing private signals that are less precise than the common signal. Unconditionally, the precision of private and common signals are positively correlated, suggesting that private and common information are complements. Inflation volatility, perceived stock market volatility and a high risk of recession are all factors associated with increased informativeness and precision of both private and common signals. Disagreement between the private and common signals can partly explain increases in uncertainty about macro variables. We discuss the implications of our findings for theoretical models of information acquisition.

Download or read book Three Essays in Bayesian Financial Econometrics written by Xin Jin and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Forecasting Macroeconomic Variables Using Neural Network Models and Three Automated Model Selection Techniques written by Anders Bredahl Kock and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Three Essays in Macroeconomics Forecasting and Monetary Policy written by Qian Li and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Forecasting in Large Macroeconomic Panels Using Bayesian Model Averaging written by and published by . This book was released on 2003 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: "This paper considers the problem of forecasting in large macroeconomic panels using Bayesian model averaging. Practical methods for implementing Bayesian model averaging with factor models are described. These methods involve algorithms that simulate from the space defined by all possible models. We explain how these simulation algorithms can also be used to select the model with the highest marginal likelihood (or highest value of an information criterion) in an efficient manner. We apply these methods to the problem of forecasting GDP and inflation using quarterly U.S. data on 162 time series. Our analysis indicates that models containing factors do outperform autoregressive models in forecasting both GDP and inflation, but only narrowly and at short horizons. We attribute these findings to the presence of structural instability and the fact that lags of the dependent variable seem to contain most of the information relevant for forecasting"--Federal Reserve Bank of New York web site.

Download or read book Macroeconomic Predictions written by Sebastian Orbe and published by . This book was released on 2013 with total page 104 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Essays on Asymmetric Loss and Learning in Macroeconomic Forecasting written by Sagarika Mishra and published by . This book was released on 2008 with total page 170 pages. Available in PDF, EPUB and Kindle. Book excerpt: My dissertation consists of three essays that answer the questions whether agents have asymmetric loss, why agents have asymmetric loss and whether agents engage in least squares learning. In my first essay, I test the rationality of inflation forecasts from the Livingston survey using the Mincer-Zarnowitz (MZ) regression when agents have asymmetric loss. I show that the MZ regression is inappropriate when agents have asymmetric loss. I demonstrate how the MZ regression can be suitably modified to test forecast rationality when agents have asymmetric loss. When I augment the MZ regression with higher order moments of the forecasts, the rationality of the inflation forecasts can not be rejected for linex and linlin loss. In my second essay, I explain why agents have asymmetric loss using GDP growth rate forecasts from the SPF. Under asymmetric loss the bias can be explained by a time-varying asymmetry parameter or by time-varying higher order moments. However, in the absence of time-varying second order moments the bias can only be explained by the time-varying asymmetry parameter. Using linex and linlin loss, I estimate the time-varying asymmetry parameter and the bias by maximum likelihood estimation. I find that the factors which agents knowingly use to bias GDP growth rate forecasts are the lag growth rate of GDP, the duration of business cycle in the presence of recession, a Republican government in the presence of recession and uncertainty in the presence of recession. In my third essay, I test whether agents learn monetary policy by least squares when there are shifts in monetary policy, using the three month T-bill forecasts from the SPF. I derive the conditional mean, variance and covariance of the forecast errors when agents learn by least squares in the presence of structural shifts. I identify the structural break dates in the policy rule using the Bai and Perron (1998, 2001, and 2003) test. Using those dates, I estimate the mean and variance of the forecast error within each regime. When I correct the bias from the survey forecast error using the estimated mean, I find survey forecasts are consistent with least squares learning.

Download or read book Economic Analysis of the Digital Economy written by Avi Goldfarb and published by University of Chicago Press. This book was released on 2015-05-08 with total page 510 pages. Available in PDF, EPUB and Kindle. Book excerpt: There is a small and growing literature that explores the impact of digitization in a variety of contexts, but its economic consequences, surprisingly, remain poorly understood. This volume aims to set the agenda for research in the economics of digitization, with each chapter identifying a promising area of research. "Economics of Digitization "identifies urgent topics with research already underway that warrant further exploration from economists. In addition to the growing importance of digitization itself, digital technologies have some features that suggest that many well-studied economic models may not apply and, indeed, so many aspects of the digital economy throw normal economics in a loop. "Economics of Digitization" will be one of the first to focus on the economic implications of digitization and to bring together leading scholars in the economics of digitization to explore emerging research.

Download or read book Three Essays on Bayesian Nonparametric Modeling in Microeconometrics written by Markus Jochmann and published by . This book was released on 2008 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Three Essays in Macroeconomics and Finance written by Yang Li and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Chapter 1 develops a continuous-time, heterogeneous agents version of the Barro-Rietz rare disasters model. Following Gabaix (2012), the disaster probability is assumed to be time-varying. The economy consists of two types of agents: (1) a "rational" agent, who updates his beliefs using Bayes Rule, and (2) a "robust" agent, who updates his beliefs using a pessimistically distorted prior. Following Hansen and Sargent (2008), pessimism is disciplined using detection error probabilities. Disaster risk is assumed to be nontradeable. The model is calibrated to US data, and focuses on three disaster episodes: (1) The Great Depression of 1929-33, (2) The Financial Crisis of 2008-09, and (3) The Covid Pandemic of 2020. The key contribution of the paper is to show that the model can replicate the observed spike in trading volume that occurs during disasters. Trading produces endogenous low frequency dynamics in the distribution of wealth. The relative wealth of robust agents gradually declines during normal times, but rises sharply during disasters. These results sound a note of caution when interpreting short-run movements in the distribution of wealth. Chapter 2 examines the market selection hypothesis in a continuous time asset pricing model with jumps. It is shown that the hypothesis is valid when agents have log preferences. The result is robust as it does not depend on whether markets are incomplete. Jumps affect long-run wealth dynamics through a redistribution channel: Disasters lead to large wealth redistribution as agents with heterogeneous beliefs about disasters have different exposures to risky assets. Using tools from ergodic theory, I prove a novel result that generalizes the rationality concept in the existing literature: an agent endowed with the optimal filter will outperform other agents in complete financial markets asymptotically. Chapter 3, a joint paper with Xiaowen Lei, develops a continuous-time overlapping generations model with rare disasters and agents who learn from their own experiences. Using microdata about household finance in China, we establish that economic disasters such as the Great Leap Forward make investors distrustful of the market. Generations that experience disasters invest a lower fraction of their wealth in risky assets, even if similar disasters are not likely to occur again during their lifetimes. "Fearing to attempt" therefore inhibits wealth accumulation by these "depression babies" relative to other generations.

Download or read book Model Identification and Forecasting Under Structural Break written by Titus O. Awokuse and published by . This book was released on 1998 with total page 394 pages. Available in PDF, EPUB and Kindle. Book excerpt: