Essays In Empirical Macroeconomics Identification In Vector Autoregressive Models And Robust Inference In Early Warning Systems
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Essays In Empirical Macroeconomics Identification In Vector Autoregressive Models And Robust Inference In Early Warning Systems
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Author : Martin Bruns
language : en
Publisher:
Release Date : 2019
Essays In Empirical Macroeconomics Identification In Vector Autoregressive Models And Robust Inference In Early Warning Systems written by Martin Bruns and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
Three Essays In Time Series Econometrics
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Author : Christian Kascha
language : en
Publisher:
Release Date : 2007
Three Essays In Time Series Econometrics written by Christian Kascha and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with Econometrics categories.
Essays On Belief Updating Forecasting And Robust Policy Making Based On Macroeconomic Variables
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Author : Yizhou Kuang
language : en
Publisher:
Release Date : 2023
Essays On Belief Updating Forecasting And Robust Policy Making Based On Macroeconomic Variables written by Yizhou Kuang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with categories.
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.
Five Empirical Essays On Identifying Cointegrated Vector Autoregressive Systems
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Author : Thórarinn G. Pétursson
language : en
Publisher:
Release Date : 1998
Five Empirical Essays On Identifying Cointegrated Vector Autoregressive Systems written by Thórarinn G. Pétursson and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998 with categories.
Essays In Empirical Macroeconomics
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Author : Julian Felix Ludwig
language : en
Publisher:
Release Date : 2019
Essays In Empirical Macroeconomics written by Julian Felix Ludwig and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
This dissertation examines how expectations are formed and how they interact with economic activities. Beliefs about economic outcomes vary with timing and accuracy of information, which have important implications for macroeconomic dynamics. The importance of expectations has long been emphasized in rational expectations (RE) models (see e.g. Lucas 1972, 1976; Kydland and Prescott 1982), and diffusion of information has been modeled in many ways (see e.g. Beaudry and Portier 2004, 2006; Mankiw and Reis 2002; Woodford 2003; Sims 2003). My work builds on this literature and aims to improve the understanding of information structure, formation of beliefs, and decision-making, and how they contribute to macro business cycles. In the first chapter, I point out how identification of full information rational expectations (FIRE) models suffers from Manski's (1993) reflection problem. I extend the standard rational expectations (RE) model to allow for a more general information structure and introduce a new framework to identify the generalized model with forecaster data. Identification is no longer subject to the reflection problem when two changes are made to the information structure: the addition of news shocks and imperfect information. News shocks provide additional variation in expectations about the future. Imperfect information provides changes in beliefs about past states, through which the feedback between expectations and decisions goes only in one direction. Expectations data are consistent with both. An application to Greenbook forecasts illustrates the importance of both news shocks and learning about the past. When I apply this framework to a Blanchard and Quah (1989) decomposition, I reach qualitatively new results. For example, expansionary supply shocks decrease unemployment. Supply shocks are also particularly subject to both news and information rigidities, so relaxing the information structure is key to correctly identifying these shocks. In the second chapter, I discover how both good and bad news shocks coincide with higher uncertainty on impact. This new stylized fact is robust to different empirical models of the news shocks literature and different proxies for U.S. macro uncertainty. The new stylized fact has implications in three fields. First, bad news shocks produce the dynamics discovered in the uncertainty literature: spikes in uncertainty are followed by drops in output. I show that there is indeed some overlap between bad news and uncertainty shocks, as the effect of an uncertainty shock gets weaker when controlling for bad news shocks. Second, I show that the close relationship between news shocks and uncertainty seems to be also responsible for the close relationship between quarterly stock returns and stock market volatility - a proxy for uncertainty. This contributes to the finance literature that works on this relationship. Third, introducing a non-linear empirical model, I find additional asymmetries in the responses to news shocks due to the asymmetric response of uncertainty. This contributes directly to the news shocks literature. An important conclusion of chapters one and two is that economic shocks vary with availability of information. The third chapter deals with such heterogeneity. I relax the assumption that economic shocks of the same type are homogeneous, respectively, always have the same effect. Instead, I argue that economists identify a shock that consists of a variety of heterogeneous components. For example, a technology shock is the sum of all disaggregate technology shocks, from innovations in marketing up to inventions in the manufacturing process, which all have different effects on the economy. I discuss how standard identification methods can identify the shocks of interest despite this heterogeneity. I find that the weights on the shock components depend on the identification strategy so that different identification strategies produce different effects. This could explain why different macro papers often identify different responses to the same shock, in the same country, and over the same time period
Robust Inference For Non Gaussian Svar Models
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Author : Lukas Hoesch
language : en
Publisher:
Release Date : 2022
Robust Inference For Non Gaussian Svar Models written by Lukas Hoesch and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
All parameters in structural vector autoregressive (SVAR) models are locally identified when the structural shocks are independent and follow non-Gaussian distributions. Unfortunately, standard inference methods that exploit such features of the data for identification fail to yield correct coverage for structural functions of the model parameters when deviations from Gaussianity are small. To this extent, we propose a robust semi-parametric approach to conduct hypothesis tests and construct confidence sets for structural functions in SVAR models. The methodology fully exploits non-Gaussianity when it is present, but yields correct size / coverage regardless of the distance to the Gaussian distribution. Empirically we revisit two macroeconomic SVAR studies where we document mixed results. For the oil price model of Kilian and Murphy (2012) we find that non-Gaussianity can robustly identify reasonable confidence sets, whereas for the labour supply-demand model of Baumeister and Hamilton (2015) this is not the case. Moreover, these exercises highlight the importance of using weak identification robust methods to assess estimation uncertainty when using non-Gaussianity for identification.
Factor Based Identification Robust Inference In Iv Regressions
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Author : George Kapetanios
language : en
Publisher:
Release Date : 2015
Factor Based Identification Robust Inference In Iv Regressions written by George Kapetanios and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with Econometric models categories.
Robust methods for IV inference have received considerable attention recently. Their analysis has raised a variety of problematic issues such as size/power trade-offs resulting from weak or many instruments. We show that information-reduction methods provide a useful and practical solution to this and related problems. Formally, we propose factor-based modifications to three popular weak-instrument-robust statistics, and illustrate their validity asymptotically and in finite samples. Results are derived using asymptotic settings that are commonly used in both the factor and weak instrument literatures. For the Anderson-Rubin statistic, we also provide analytical finite sample results that do not require any underlying factor structure. An illustrative Monte Carlo study reveals the following. Factor based tests control size regardless of instruments and factor quality. All factor based tests are systematically more powerful than standard counterparts. With informative instruments and in contrast with standard tests: (i) power of factor-based tests is not affected by k even when large, and (ii) weak factor structure does not cost power. An empirical study on a New Keynesian macroeconomic model suggests that our factor-based methods can bridge a number of gaps between structural and statistical modeling
Essays In Financial And Macro Econometrics
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Author : Paul Karapanagiotidis
language : en
Publisher:
Release Date : 2014
Essays In Financial And Macro Econometrics written by Paul Karapanagiotidis and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with categories.
Identification And Inference For Econometric Models
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Author : Donald W. K. Andrews
language : en
Publisher:
Release Date : 2005
Identification And Inference For Econometric Models written by Donald W. K. Andrews and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with Econometric models categories.
The chapters cover four themes: identification and efficient estimation in econometrics, asymptotic approximations to the distributions of econometric estimators and tests, inference involving potentially nonstationary time series, such as processes that might have a unit autoregressive root, and nonparametric and semiparametric inference.
Recent Advances And Future Directions In Causality Prediction And Specification Analysis
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Author : Xiaohong Chen
language : en
Publisher: Springer
Release Date : 2014-09-19
Recent Advances And Future Directions In Causality Prediction And Specification Analysis written by Xiaohong Chen and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-09-19 with Business & Economics categories.
This book is a collection of articles that present the most recent cutting edge results on specification and estimation of economic models written by a number of the world’s foremost leaders in the fields of theoretical and methodological econometrics. Recent advances in asymptotic approximation theory, including the use of higher order asymptotics for things like estimator bias correction, and the use of various expansion and other theoretical tools for the development of bootstrap techniques designed for implementation when carrying out inference are at the forefront of theoretical development in the field of econometrics. One important feature of these advances in the theory of econometrics is that they are being seamlessly and almost immediately incorporated into the “empirical toolbox” that applied practitioners use when actually constructing models using data, for the purposes of both prediction and policy analysis and the more theoretically targeted chapters in the book will discuss these developments. Turning now to empirical methodology, chapters on prediction methodology will focus on macroeconomic and financial applications, such as the construction of diffusion index models for forecasting with very large numbers of variables, and the construction of data samples that result in optimal predictive accuracy tests when comparing alternative prediction models. Chapters carefully outline how applied practitioners can correctly implement the latest theoretical refinements in model specification in order to “build” the best models using large-scale and traditional datasets, making the book of interest to a broad readership of economists from theoretical econometricians to applied economic practitioners.