Macroeconomic Applications Research Articles

Measuring Causal Effect with ARDL-BART: A Macroeconomic Application

Measuring Causal Effect with ARDL-BART: A Macroeconomic Application

Non-Gaussian score-driven conditionally heteroskedastic models with a macroeconomic application

AbstractWe contribute to the literature on empirical macroeconomic models with time-varying conditional moments, by introducing a heteroskedastic score-driven model with Student’s t-distributed innovations, named the heteroskedastic score-driven $t$ -QVAR (quasi-vector autoregressive) model. The $t$ -QVAR model is a robust nonlinear extension of the VARMA (VAR moving average) model. As an illustration, we apply the heteroskedastic $t$ -QVAR model to a dynamic stochastic general equilibrium model, for which we estimate Gaussian-ABCD and $t$ -ABCD representations. We use data on economic output, inflation, interest rate, government spending, aggregate productivity, and consumption of the USA for the period of 1954 Q3 to 2022 Q1. Due to the robustness of the heteroskedastic $t$ -QVAR model, even including the period of the coronavirus disease of 2019 (COVID-19) pandemic and the start of the Russian invasion of Ukraine, we find a superior statistical performance, lower policy-relevant dynamic effects, and a higher estimation precision of the impulse response function for US gross domestic product growth and US inflation rate, for the heteroskedastic score-driven $t$ -ABCD representation rather than for the homoskedastic Gaussian-ABCD representation.

Designing Information Provision Experiments

Information provision experiments allow researchers to test economic theories and answer policy-relevant questions by varying the information set available to respondents. We survey the emerging literature using information provision experiments in economics and discuss applications in macroeconomics, finance, political economy, public economics, labor economics, and health economics. We also discuss design considerations and provide best-practice recommendations on how to (i) measure beliefs; (ii) design the information intervention; (iii) measure belief updating; (iv) deal with potential confounds, such as experimenter demand effects; and (v) recruit respondents using online panels. We finally discuss typical effect sizes and provide sample size recommendations.(JEL C90, D83, D91)

Measuring Causal Effect with ARDL-BART: A Macroeconomic Application

Measuring Causal Effect with ARDL-BART: A Macroeconomic Application

The EAS approach for graphical selection consistency in vector autoregression models

AbstractAs evidenced by various recent and significant papers within the frequentist literature, along with numerous applications in macroeconomics, genomics, and neuroscience, there continues to be substantial interest in understanding the theoretical estimation properties of high‐dimensional vector autoregression (VAR) models. To date, however, while Bayesian VAR (BVAR) models have been developed and studied empirically (primarily in the econometrics literature), there exist very few theoretical investigations of the repeated‐sampling properties for BVAR models in the literature, and there exist no generalized fiducial investigations of VAR models. In this direction, we construct methodology via the ‐admissible subsets (EAS) approach for inference based on a generalized fiducial distribution of relative model probabilities over all sets of active/inactive components (graphs) of the VAR transition matrix. We provide a mathematical proof of pairwise and strong graphical selection consistency for the EAS approach for stable VAR(1) models, and demonstrate empirically that it is an effective strategy in high‐dimensional settings.

Time Inconsistent Preferences with Time Dependent Relative Risk Aversion: A Model with Some Macroeconomic Applications

Time Inconsistent Preferences with Time Dependent Relative Risk Aversion: A Model with Some Macroeconomic Applications

Recent Advances of Dynamic Model Averaging Theory and Its Application in Econometrics

Dynamic Model Averaging (DMA) was first proposed by Raftery et al. (2010) when predicting the output strip thickness of cold rolling mills and is a recursive implementation of standard Bayesian model averaging, also known as recursive model averaging. Since Gary Koop and Dimitris Korobilis introduced DMA into the field of econometrics in 2012, dynamic model averaging has become a widely used estimation technique in macroeconomic applications because of its ability to adapt to the temporal change of parameters and the advantages of the specification of optimal prediction models, the method has good application prospects. This paper focuses on dynamic model averaging as a solution to model uncertainty problems, focusing on recent theoretical developments and their applications in econometrics. Discussions focused on uncertainties contained in covariates in regression models, such as normal linear regression and its extensions, and on advances in designing models to handle more challenging situations, such as time-dependent, spatially dependent, or endogenous data. The results show that the DMA method has good prediction accuracy, is a powerful tool for actual prediction, and provides important technical support for risk avoidance in management.

Scalable inference for a full multivariate stochastic volatility model

We introduce a multivariate stochastic volatility model that imposes no restrictions on the structure of the volatility matrix and treats all its elements as functions of latent stochastic processes. Inference is achieved via a carefully designed feasible and scalable MCMC that has quadratic, rather than cubic, computational complexity for evaluating the multivariate normal densities required. We illustrate how our model can be applied on macroeconomic applications through a stochastic volatility VAR model, comparing it to competing approaches in the literature. We also demonstrate how our approach can be applied to a large dataset containing 571 stock daily returns of Euro STOXX index.

Robust energy-to-peak filter design for a class of unstable polytopic systems with a macroeconomic application

This paper deals with the robust energy-to-peak filtering problem for linear continuous- and discrete-time systems with polytopic uncertainties. Motivated by a macroeconomic application, the notion of the robust energy-to-peak filter is extended to a class of unstable systems by making use of the results on stability with respect to noncompact sets. Parameter-dependent Lyapunov approach and Finsler’s lemma are used to establish sufficient conditions for the existence of energy-to-peak filter such that the error system is asymptotically stable with respect to a subspace and a prescribed energy-to-peak performance is guaranteed. The conditions are formulated in terms of linear matrix inequalities (LMIs). Two numerical examples modified from the literature are provided to demonstrate the effectiveness of the proposed method. The results are also successfully applied to the estimation of the so called potential GDP using real data.

Practical Methods for Modeling Weak VARMA Processes: Identification, Estimation and Specification With a Macroeconomic Application

We consider the problem of developing practical methods for modelling weak VARMA processes. We first propose new identified VARMA representations, the diagonal MA equation form and the final MA equation form, where the MA operator is either diagonal or scalar. Both these representations have the important feature that they constitute relatively simple modifications of a VAR model (in contrast with the echelon representation). Second, for estimating VARMA models, we develop computationally simple methods which only require linear regressions. The asymptotic properties of the estimator are derived under weak hypotheses on the innovations (uncorrelated and strong mixing), in order to broaden the class of models to which it can be applied. Third, we present a modified information criterion which yields consistent estimates of the orders under the proposed representations. The estimation methods are studied by simulation. To demonstrate the importance of using VARMA models to study multivariate time series, we compare the impulse-response functions and the out-of-sample forecasts generated by VARMA and VAR models. The proposed methodology is applied to a six-variable macroeconomic model of monetary policy, based on the U.S. monthly data over the period 1962–1996. The results demonstrate the advantages of using the VARMA methodology for impulse response estimation and forecasting, in contrast with standard VAR models.

Factor extraction using Kalman filter and smoothing: This is not just another survey

Dynamic factor models have been the main “big data” tool used by empirical macroeconomists during the last 30 years. In this context, Kalman filter and smoothing (KFS) procedures can cope with missing data, mixed frequency data, time-varying parameters, non-linearities, non-stationarity, and many other characteristics often observed in real systems of economic variables. The main contribution of this paper is to provide a comprehensive updated summary of the literature on latent common factors extracted using KFS procedures in the context of dynamic factor models, pointing out their potential limitations. Signal extraction and parameter estimation issues are separately analyzed. Identification issues are also tackled in both stationary and non-stationary models. Finally, empirical applications are surveyed in both cases. This survey is relevant to researchers and practitioners interested not only in the theory of KFS procedures for factor extraction in dynamic factor models but also in their empirical application in macroeconomics and finance.

AN OVERVIEW OF DYNAMIC MODEL AVERAGING TECHNIQUES IN TIME‐SERIES ECONOMETRICS

AbstractDynamic model averaging (DMA) has become a widely used estimation technique in macroeconomic applications. Since its introduction in econom(etr)ics by Gary Koop and Dimitris Korobilis in 2009, applications of DMA have increased in unimaginable ways. Besides applying the original (univariate) framework suggested by Koop and Korobilis on the data of interest, for example, the inflation rate of the country of choice or return on the rate of equity, practitioners have been able to use DMA‐based techniques to extend current models, thereby further improving out‐of‐sample forecast accuracy, overcome computational bottlenecks, and even help improve our understanding of economic phenomena by introducing new models. These include using Google search data in combination with the predictive likelihood to govern switching between different predictive regressions in the model set or specifying large time‐varying parameter vector autoregressions that can be estimated without resorting to simulation‐based techniques. This study provides an overview of DMA techniques and the ways in which they have evolved since the contribution of Koop and Korobilis.

Svars: An R Package for Data-Driven Identification in Multivariate Time Series Analysis

Structural vector autoregressive (SVAR) models are frequently applied to trace the contemporaneous linkages among (macroeconomic) variables back to an interplay of orthogonal structural shocks. Under Gaussianity the structural parameters are unidentified without additional (often external and not data-based) information. In contrast, the often reasonable assumption of heteroskedastic and/or non-Gaussian model disturbances offers the possibility to identify unique structural shocks. We describe the R package svars which implements statistical identification techniques that can be both heteroskedasticity-based or independence-based. Moreover, it includes a rich variety of analysis tools that are well known in the SVAR literature. Next to a comprehensive review of the theoretical background, we provide a detailed description of the associated R functions. Furthermore, a macroeconomic application serves as a step-by-step guide on how to apply these functions to the identification and interpretation of structural VAR models.

On the solution of the nonsymmetric T-Riccati equation

The nonsymmetric T-Riccati equation is a quadratic matrix equation where the linear part corresponds to the so-called T-Sylvester or T-Lyapunov operator that has previously been studied in the literature. It has applications in macroeconomics and policy dynamics. So far, it presents an unexplored problem in numerical analysis, and both, theoretical results and computational methods, are lacking in the literature. In this paper we provide some sufficient conditions for the existence and uniqueness of a nonnegative minimal solution and its efficient computation is deeply analyzed. Both the small-scale and the large-scale setting are addressed and Newton-Kleinman-like methods are derived. The convergence of these procedures to the minimal solution is proved and several numerical results illustrate the computational efficiency of the proposed methods.

Recovery of simultaneous low rank and two-way sparse coefficient matrices, a nonconvex approach

We study the problem of recovery of matrices that are simultaneously low rank and row and/or column sparse. Such matrices appear in recent applications in cognitive neuroscience, imaging, computer vision, macroeconomics, and genetics. We propose a GDT (Gradient Descent with hard Thresholding) algorithm to efficiently recover matrices with such structure, by minimizing a bi-convex function over a nonconvex set of constraints. We show linear convergence of the iterates obtained by GDT to a region within statistical error of an optimal solution. As an application of our method, we consider multi-task learning problems and show that the statistical error rate obtained by GDT is near optimal compared to minimax rate. Experiments demonstrate competitive performance and much faster running speed compared to existing methods, on both simulations and real data sets.

Determinants of return-on-equity in USA, German and Japanese manufacturing firms

PurposeDuPont financial analysis is generally used in micro-economic studies to compare an individual firm’s financial performance with industry averages. The purpose of this paper is to undertake a macro-economic cross-sectional analysis of the determinants of return-on-equity (ROE) in USA, German and Japanese manufacturing firms.Design/methodology/approachThe authors use cross-sectional log-linear multivariate regression analysis to determine the elasticity of ROE to changes in net profit margin (NPM), total assets turnover (TAT) and equity multiplier (EQM) in USA, German and Japanese manufacturing firms. The authors obtain the data for the analysis from the COMPUSTAT Research Insight/Global Vintage database.FindingsWith data for all manufacturing firms, the authors find that the most important determinant of ROE is NPM in all three countries. The least important determinant of ROE is TAT in the USA and Germany, and EQM in Japan. Electronics is the most important manufacturing industry in all three countries, the authors also apply the analysis to data for the electronics manufacturing firms in the three countries. The authors find that an increase of 10 percent in NPM increases ROE by about 9.8 percent in Germany, by about 8.3 percent in the USA, and by about 6.9 percent in Japan. An increase of 10 percent in TAT increases ROE by about 2.2 percent in Germany and by about 1.5 percent in Japan. An increase of 10 percent in EQM increases ROE by about 1.9 percent in Germany and by about 1.5 percent in the USA.Practical implicationsThe empirical findings of this study can provide useful insights for financial managers regarding the determinants of ROE they should focus on to achieve the greatest impact on ROE.Originality/valueDuPont analysis is generally used as a micro-economic tool at the firm level. This study is a macro-economic application of the tool to study the cross-sectional determinants of ROE at the industry level.

Analyzing multiple vector autoregressions through matrix-variate normal distribution with two covariance matrices

This article proposes a new approach to analyze multiple vector autoregressive (VAR) models that render us a newly constructed matrix autoregressive (MtAR) model based on a matrix-variate normal distribution with two covariance matrices. The MtAR is a generalization of VAR models where the two covariance matrices allow the extension of MtAR to a structural MtAR analysis. The proposed MtAR can also incorporate different lag orders across VAR systems that provide more flexibility to the model. The estimation results from a simulation study and an empirical study on macroeconomic application show favorable performance of our proposed models and method.

Bayesian compressed vector autoregressions

Macroeconomists are increasingly working with large Vector Autoregressions (VARs) where the number of parameters vastly exceeds the number of observations. Existing approaches either involve prior shrinkage or the use of factor methods. In this paper, we develop an alternative based on ideas from the compressed regression literature. It involves randomly compressing the explanatory variables prior to analysis. A huge dimensional problem is thus turned into a much smaller, more computationally tractable one. Bayesian model averaging can be done over various compressions, attaching greater weight to compressions which forecast well. In a macroeconomic application involving up to 129 variables, we find compressed VAR methods to forecast as well or better than either factor methods or large VAR methods involving prior shrinkage.

The Macroeconomics of Rational Bubbles: A User's Guide

This review provides a guide to macroeconomic applications of the theory of rational bubbles. It shows that rational bubbles can be easily incorporated into standard macroeconomic models and illustrates how they can be used to account for important macroeconomic phenomena. It also discusses the welfare implications of rational bubbles and the role of policy in managing them. Finally, it provides a detailed review of the literature.

Time-Varying Parameters: New Test Tailored to Applications in Finance and Macroeconomics

Many economic theories imply a linear relationship with constant parameters between financial or macroeconomic variables. While the linear model with constant parameters is often disputed in the literature, this model specification is rarely tested. This paper proposes a new and intuitively appealing test for model specification tailored for applications in finance and macroeconomics. Importantly, the test allows for autocorrelation, which is often present in these applications. We demonstrate impressive properties of the test in a realistic simulation study and obtain important insights from empirical applications.