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Public Debt and Economic Growth: A Panel Kink Regression Latent Group Structures Approach Econometrics Pub Date : 2024-03-05 Chaoyi Chen, Thanasis Stengos, Jianhan Zhang
This paper investigates the relationship between public debt and economic growth in the context of a panel kink regression with latent group structures. The proposed model allows us to explore the heterogeneous threshold effects of public debt on economic growth based on unknown group patterns. We propose a least squares estimator and demonstrate the consistency of estimating group structures. The
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Introduction to the Special Issue “High-Dimensional Time Series in Macroeconomics and Finance” Econometrics Pub Date : 2024-02-22 Benedikt M. Pötscher, Leopold Sögner, Martin Wagner
This Special Issue was organized in relation to the fifth Vienna Workshop on High-Dimensional Time Series in Macroeconomics and Finance, which took place at the Institute for Advanced Studies in Vienna on 9 June and 10 June 2022 [...]
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Multivariate Stochastic Volatility Modeling via Integrated Nested Laplace Approximations: A Multifactor Extension Econometrics Pub Date : 2024-02-19 João Pedro Coli de Souza Monteneri Nacinben, Márcio Laurini
This study introduces a multivariate extension to the class of stochastic volatility models, employing integrated nested Laplace approximations (INLA) for estimation. Bayesian methods for estimating stochastic volatility models through Markov Chain Monte Carlo (MCMC) can become computationally burdensome or inefficient as the dataset size and problem complexity increase. Furthermore, issues related
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Influence of Digitalisation on Business Success in Austrian Traded Prime Market Companies—A Longitudinal Study Econometrics Pub Date : 2024-02-09 Christa Hangl
Software investments can significantly contribute to corporate success by optimising productivity, stimulating creativity, elevating customer satisfaction, and equipping organisations with the essential resources to adapt and thrive in a rapidly changing market. This paper examines whether software investments have an impact on the economic success of the companies listed on the Austrian Traded Prime
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Estimating Linear Dynamic Panels with Recentered Moments Econometrics Pub Date : 2024-01-17 Yong Bao
This paper proposes estimating linear dynamic panels by explicitly exploiting the endogeneity of lagged dependent variables and expressing the cross moments between the endogenous lagged dependent variables and disturbances in terms of model parameters. These moments, when recentered, form the basis for model estimation. The resulting estimator's asymptotic properties are derived under different asymptotic
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Is Monetary Policy a Driver of Cryptocurrencies? Evidence from a Structural Break GARCH-MIDAS Approach Econometrics Pub Date : 2024-01-05 Md Samsul Alam, Alessandra Amendola, Vincenzo Candila, Shahram Dehghan Jabarabadi
The introduction of Bitcoin as a distributed peer-to-peer digital cash in 2008 and its first recorded real transaction in 2010 served the function of a medium of exchange, transforming the financial landscape by offering a decentralized, peer-to-peer alternative to conventional monetary systems. This study investigates the intricate relationship between cryptocurrencies and monetary policy, with a
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Publisher’s Note: Econometrics—A New Era for a Well-Established Journal Econometrics Pub Date : 2023-12-28 Peter Roth
Throughout its lifespan, a journal goes through many phases—and Econometrics (Econometrics Homepage n [...]
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Multistep Forecast Averaging with Stochastic and Deterministic Trends Econometrics Pub Date : 2023-12-15 Mohitosh Kejriwal, Linh Nguyen, Xuewen Yu
This paper presents a new approach to constructing multistep combination forecasts in a nonstationary framework with stochastic and deterministic trends. Existing forecast combination approaches in the stationary setup typically target the in-sample asymptotic mean squared error (AMSE), relying on its approximate equivalence with the asymptotic forecast risk (AFR). Such equivalence, however, breaks
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Liquidity and Business Cycles—With Occasional Disruptions Econometrics Pub Date : 2023-12-12 Willi Semmler, Gabriel R. Padró Rosario, Levent Koçkesen
Some financial disruptions that started in California, U.S., in March 2023, resulting in the closure of several medium-size U.S. banks, shed new light on the role of liquidity in business cycle dynamics. In the normal path of the business cycle, liquidity and output mutually interact. Small shocks generally lead to mean reversion through market forces, as a low degree of liquidity dissipation does
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When It Counts—Econometric Identification of the Basic Factor Model Based on GLT Structures Econometrics Pub Date : 2023-11-20 Sylvia Frühwirth-Schnatter, Darjus Hosszejni, Hedibert Freitas Lopes
Despite the popularity of factor models with simple loading matrices, little attention has been given to formally address the identifiability of these models beyond standard rotation-based identification such as the positive lower triangular (PLT) constraint. To fill this gap, we review the advantages of variance identification in simple factor analysis and introduce the generalized lower triangular
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A New Matrix Statistic for the Hausman Endogeneity Test under Heteroskedasticity Econometrics Pub Date : 2023-10-10 Alecos Papadopoulos
We derive a new matrix statistic for the Hausman test for endogeneity in cross-sectional Instrumental Variables estimation, that incorporates heteroskedasticity in a natural way and does not use a generalized inverse. A Monte Carlo study examines the performance of the statistic for different heteroskedasticity-robust variance estimators and different skedastic situations. We find that the test statistic
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Detecting Pump-and-Dumps with Crypto-Assets: Dealing with Imbalanced Datasets and Insiders’ Anticipated Purchases Econometrics Pub Date : 2023-08-30 Dean Fantazzini, Yufeng Xiao
Detecting pump-and-dump schemes involving cryptoassets with high-frequency data is challenging due to imbalanced datasets and the early occurrence of unusual trading volumes. To address these issues, we propose constructing synthetic balanced datasets using resampling methods and flagging a pump-and-dump from the moment of public announcement up to 60 min beforehand. We validated our proposals using
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Competition–Innovation Nexus: Product vs. Process, Does It Matter? Econometrics Pub Date : 2023-08-25 Emil Palikot
I study the relationship between competition and innovation, focusing on the distinction between product and process innovations. By considering product innovation, I expand upon earlier research on the topic of the relationship between competition and innovation, which focused on process innovations. New products allow firms to differentiate themselves from one another. I demonstrate that the competition
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Locationally Varying Production Technology and Productivity: The Case of Norwegian Farming Econometrics Pub Date : 2023-08-18 Subal C. Kumbhakar, Jingfang Zhang, Gudbrand Lien
In this study, we leverage geographical coordinates and firm-level panel data to uncover variations in production across different locations. Our approach involves using a semiparametric proxy variable regression estimator, which allows us to define and estimate a customized production function for each firm and its corresponding location. By employing kernel methods, we estimate the nonparametric
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Tracking ‘Pure’ Systematic Risk with Realized Betas for Bitcoin and Ethereum Econometrics Pub Date : 2023-08-10 Bilel Sanhaji, Julien Chevallier
Using the capital asset pricing model, this article critically assesses the relative importance of computing ‘realized’ betas from high-frequency returns for Bitcoin and Ethereum—the two major cryptocurrencies—against their classic counterparts using the 1-day and 5-day return-based betas. The sample includes intraday data from 15 May 2018 until 17 January 2023. The microstructure noise is present
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Estimation of Realized Asymmetric Stochastic Volatility Models Using Kalman Filter Econometrics Pub Date : 2023-07-31 Manabu Asai
Despite the growing interest in realized stochastic volatility models, their estimation techniques, such as simulated maximum likelihood (SML), are computationally intensive. Based on the realized volatility equation, this study demonstrates that, in a finite sample, the quasi-maximum likelihood estimator based on the Kalman filter is competitive with the two-step SML estimator, which is less efficient
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Socio-Economic and Demographic Factors Associated with COVID-19 Mortality in European Regions: Spatial Econometric Analysis Econometrics Pub Date : 2023-06-20 Mateusz Szysz, Andrzej Torój
In some NUTS 2 (Nomenclature of Territorial Units for Statistics) regions of Europe, the COVID-19 pandemic has triggered an increase in mortality by several dozen percent and only a few percent in others. Based on the data on 189 regions from 19 European countries, we identified factors responsible for these differences, both intra- and internationally. Due to the spatial nature of the virus diffusion
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Parameter Estimation of the Heston Volatility Model with Jumps in the Asset Prices Econometrics Pub Date : 2023-06-02 Jarosław Gruszka , Janusz Szwabiński
The parametric estimation of stochastic differential equations (SDEs) has been the subject of intense studies already for several decades. The Heston model, for instance, is based on two coupled SDEs and is often used in financial mathematics for the dynamics of asset prices and their volatility. Calibrating it to real data would be very useful in many practical scenarios. It is very challenging, however
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Factorization of a Spectral Density with Smooth Eigenvalues of a Multidimensional Stationary Time Series Econometrics Pub Date : 2023-05-31 Tamás Szabados
The aim of this paper to give a multidimensional version of the classical one-dimensional case of smooth spectral density. A spectral density with smooth eigenvalues and H∞ eigenvectors gives an explicit method to factorize the spectral density and compute the Wold representation of a weakly stationary time series. A formula, similar to the Kolmogorov–Szego formula, is given for the covariance matrix
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Local Gaussian Cross-Spectrum Analysis Econometrics Pub Date : 2023-04-21 Lars Arne Jordanger, Dag Tjøstheim
The ordinary spectrum is restricted in its applications, since it is based on the second-order moments (auto- and cross-covariances). Alternative approaches to spectrum analysis have been investigated based on other measures of dependence. One such approach was developed for univariate time series by the authors of this paper using the local Gaussian auto-spectrum based on the local Gaussian auto-correlations
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Information-Criterion-Based Lag Length Selection in Vector Autoregressive Approximations for I(2) Processes Econometrics Pub Date : 2023-04-20 Dietmar Bauer
When using vector autoregressive (VAR) models for approximating time series, a key step is the selection of the lag length. Often this is performed using information criteria, even if a theoretical justification is lacking in some cases. For stationary processes, the asymptotic properties of the corresponding estimators are well documented in great generality in the book Hannan and Deistler (1988)
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Modeling COVID-19 Infection Rates by Regime-Switching Unobserved Components Models Econometrics Pub Date : 2023-04-03 Paul Haimerl, Tobias Hartl
The COVID-19 pandemic is characterized by a recurring sequence of peaks and troughs. This article proposes a regime-switching unobserved components (UC) approach to model the trend of COVID-19 infections as a function of this ebb and flow pattern. Estimated regime probabilities indicate the prevalence of either an infection up- or down-turning regime for every day of the observational period. This
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On the Bayesian Mixture of Generalized Linear Models with Gamma-Distributed Responses Econometrics Pub Date : 2022-10-04 Irwan Susanto, Nur Iriawan, Heri Kuswanto
This paper proposes enhanced studies on a model consisting of a finite mixture framework of generalized linear models (GLMs) with gamma-distributed responses estimated using the Bayesian approach coupled with the Markov Chain Monte Carlo (MCMC) method. The log-link function, which relates the mean and linear predictors of the model, is implemented to ensure non-negative values of the predicted gamma-distributed
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Modelling and Diagnostics of Spatially Autocorrelated Counts Econometrics Pub Date : 2022-09-13 Robert C. Jung, Stephanie Glaser
This paper proposes a new spatial lag regression model which addresses global spatial autocorrelation arising from cross-sectional dependence between counts. Our approach offers an intuitive interpretation of the spatial correlation parameter as a measurement of the impact of neighbouring observations on the conditional expectation of the counts. It allows for flexible likelihood-based inference based
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A Parsimonious Test of Constancy of a Positive Definite Correlation Matrix in a Multivariate Time-Varying GARCH Model Econometrics Pub Date : 2022-08-24 Jian Kang, Johan Stax Jakobsen, Annastiina Silvennoinen, Timo Teräsvirta, Glen Wade
We construct a parsimonious test of constancy of the correlation matrix in the multivariate conditional correlation GARCH model, where the GARCH equations are time-varying. The alternative to constancy is that the correlations change deterministically as a function of time. The alternative is a covariance matrix, not a correlation matrix, so the test may be viewed as a general test of stability of
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Common Correlated Effects Estimation for Dynamic Heterogeneous Panels with Non-Stationary Multi-Factor Error Structures Econometrics Pub Date : 2022-08-11 Shiyun Cao, Qiankun Zhou
In this paper, we consider the estimation of a dynamic panel data model with non-stationary multi-factor error structures. We adopted the common correlated effect (CCE) estimation and established the asymptotic properties of the CCE and common correlated effects mean group (CCEMG) estimators, as N and T tend to infinity. The results show that both the CCE and CCEMG estimators are consistent and the
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Structural Compressed Panel VAR with Stochastic Volatility: A Robust Bayesian Model Averaging Procedure Econometrics Pub Date : 2022-07-12 Antonio Pacifico
This paper improves the existing literature on the shrinkage of high dimensional model and parameter spaces through Bayesian priors and Markov Chains algorithms. A hierarchical semiparametric Bayes approach is developed to overtake limits and misspecificity involved in compressed regression models. Methodologically, a multicountry large structural Panel Vector Autoregression is compressed through a
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Forecasting Industrial Production Using Its Aggregated and Disaggregated Series or a Combination of Both: Evidence from One Emerging Market Economy Econometrics Pub Date : 2022-06-15 Diogo de Prince, Emerson Fernandes Marçal, Pedro L. Valls Pereira
In this paper, we address whether using a disaggregated series or combining an aggregated and disaggregated series improves the forecasting of the aggregated series compared to using the aggregated series alone. We used econometric techniques, such as the weighted lag adaptive least absolute shrinkage and selection operator, and Exponential Triple Smoothing (ETS), as well as the Autometrics algorithm
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Impact of COVID-19 Pandemic News on the Cryptocurrency Market and Gold Returns: A Quantile-on-Quantile Regression Analysis Econometrics Pub Date : 2022-06-02 Esam Mahdi, Ameena Al-Abdulla
In this paper, we investigate the relationship between the RavenPack news-based index associated with coronavirus outbreak (Panic, Sentiment, Infodemic, and Media Coverage) and returns of two commodities—Bitcoin and gold. We utilized the novel quantile-on-quantile approach to uncover the dependence between the news-based index associated with coronavirus outbreak and Bitcoin and gold returns. Our results
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Are Vaccinations Alone Enough to Curb the Dynamics of the COVID-19 Pandemic in the European Union? Econometrics Pub Date : 2022-05-26 Paweł Miłobędzki
I use the data on the COVID-19 pandemic maintained by Our Word in Data to estimate a nonstationary dynamic panel exhibiting the dynamics of confirmed deaths, infections and vaccinations per million population in the European Union countries in the period of January–July 2021. Having the data aggregated on a weekly basis I demonstrate that a model which allows for heterogeneous short-run dynamics and
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Celebrated Econometricians: Katarina Juselius and Søren Johansen Econometrics Pub Date : 2022-05-16 Rocco Mosconi, Paolo Paruolo
Note: In lieu of an abstract, this is an excerpt from the first page. This Special Issue collects contributions related to the advances in the theory and practice of Econometrics induced by the research of Katarina Juselius and Søren Johansen, whom this Special Issue aims to celebrate [...]
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An Alternative Estimation Method for Time-Varying Parameter Models Econometrics Pub Date : 2022-04-27 Mikio Ito, Akihiko Noda, Tatsuma Wada
A multivariate, non-Bayesian, regression-based, or feasible generalized least squares (GLS)-based approach is proposed to estimate time-varying VAR parameter models. Although it has been known that the Kalman-smoothed estimate can be alternatively estimated using GLS for univariate models, we assess the accuracy of the feasible GLS estimator compared with commonly used Bayesian estimators. Unlike the
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Algorithmic Modelling of Financial Conditions for Macro Predictive Purposes: Pilot Application to USA Data Econometrics Pub Date : 2022-04-19 Duo Qin, Sophie van Huellen, Qing Chao Wang, Thanos Moraitis
Aggregate financial conditions indices (FCIs) are constructed to fulfil two aims: (i) The FCIs should resemble non-model-based composite indices in that their composition is adequately invariant for concatenation during regular updates; (ii) the concatenated FCIs should outperform financial variables conventionally used as leading indicators in macro models. Both aims are shown to be attainable once
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A Conversation with Søren Johansen Econometrics Pub Date : 2022-04-13 Rocco Mosconi, Paolo Paruolo
This article was prepared for the Special Issue “Celebrated Econometricians: Katarina Juselius and Søren Johansen” of Econometrics. It is based on material recorded on 30 October 2018 in Copenhagen. It explores Søren Johansen’s research, and discusses inter alia the following issues: estimation and inference for nonstationary time series of the I(1), I(2) and fractional cointegration types; survival
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A Conversation with Katarina Juselius Econometrics Pub Date : 2022-04-13 Rocco Mosconi, Paolo Paruolo
This article was prepared for the Special Issue ‘Celebrated Econometricians: Katarina Juselius and Søren Johansen’ of Econometrics. It is based on material recorded on 30–31 October 2018 in Copenhagen. It explores Katarina Juselius’ research, and discusses inter alia the following issues: equilibrium; short and long-run behaviour; common trends; adjustment; integral and proportional control mechanisms;
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Combining Predictions of Auto Insurance Claims Econometrics Pub Date : 2022-04-11 Chenglong Ye, Lin Zhang, Mingxuan Han, Yanjia Yu, Bingxin Zhao, Yuhong Yang
This paper aims to better predict highly skewed auto insurance claims by combining candidate predictions. We analyze a version of the Kangaroo Auto Insurance company data and study the effects of combining different methods using five measures of prediction accuracy. The results show the following. First, when there is an outstanding (in terms of Gini Index) prediction among the candidates, the “forecast
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Using the SARIMA Model to Forecast the Fourth Global Wave of Cumulative Deaths from COVID-19: Evidence from 12 Hard-Hit Big Countries Econometrics Pub Date : 2022-04-09 Gaetano Perone
The COVID-19 pandemic is a serious threat to all of us. It has caused an unprecedented shock to the world’s economy, and it has interrupted the lives and livelihood of millions of people. In the last two years, a large body of literature has attempted to forecast the main dimensions of the COVID-19 outbreak using a wide set of models. In this paper, I forecast the short- to mid-term cumulative deaths
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Model Validation and DSGE Modeling Econometrics Pub Date : 2022-04-07 Niraj Poudyal, Aris Spanos
The primary objective of this paper is to revisit DSGE models with a view to bringing out their key weaknesses, including statistical misspecification, non-identification of deep parameters, substantive inadequacy, weak forecasting performance, and potentially misleading policy analysis. It is argued that most of these weaknesses stem from failing to distinguish between statistical and substantive
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A Theory-Consistent CVAR Scenario for a Monetary Model with Forward-Looking Expectations Econometrics Pub Date : 2022-04-06 Katarina Juselius
A theory-consistent CVAR scenario describes a set of testable regularities capturing basic assumptions of the theoretical model. Using this concept, the paper considers a standard model for exchange rate determination with forward-looking expectations and shows that all assumptions about the model’s shock structure and steady-state behavior can be formulated as testable hypotheses on common stochastic
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Learning Forecast-Efficient Yield Curve Factor Decompositions with Neural Networks Econometrics Pub Date : 2022-03-25 Piero C. Kauffmann, Hellinton H. Takada, Ana T. Terada, Julio M. Stern
Most factor-based forecasting models for the term structure of interest rates depend on a fixed number of factor loading functions that have to be specified in advance. In this study, we relax this assumption by building a yield curve forecasting model that learns new factor decompositions directly from data for an arbitrary number of factors, combining a Gaussian linear state-space model with a neural
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Causal Transmission in Reduced-Form Models Econometrics Pub Date : 2022-03-24 Vassilios Bazinas, Bent Nielsen
We propose a method to explore the causal transmission of an intervention through two endogenous variables of interest. We refer to the intervention as a catalyst variable. The method is based on the reduced-form system formed from the conditional distribution of the two endogenous variables given the catalyst. The method combines elements from instrumental variable analysis and Cholesky decomposition
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A Binary Choice Model with Sample Selection and Covariate-Related Misclassification Econometrics Pub Date : 2022-03-23 Jorge González Chapela
Misclassification of a binary response variable and nonrandom sample selection are data issues frequently encountered by empirical researchers. For cases in which both issues feature simultaneously in a data set, we formulate a sample selection model for a misclassified binary outcome in which the conditional probabilities of misclassification are allowed to depend on covariates. Assuming the availability
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Missing Values in Panel Data Unit Root Tests Econometrics Pub Date : 2022-03-16 Yiannis Karavias, Elias Tzavalis, Haotian Zhang
Missing data or missing values are a common phenomenon in applied panel data research and of great interest for panel data unit root testing. The standard approach in the literature is to balance the panel by removing units and/or trimming a common time period for all units. However, this approach can be costly in terms of lost information. Instead, existing panel unit root tests could be extended
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Green Bonds for the Transition to a Low-Carbon Economy Econometrics Pub Date : 2022-03-02 Andreas Lichtenberger, Joao Paulo Braga, Willi Semmler
The green bond market is emerging as an impactful financing mechanism in climate change mitigation efforts. The effectiveness of the financial market for this transition to a low-carbon economy depends on attracting investors and removing financial market roadblocks. This paper investigates the differential bond performance of green vs non-green bonds with (1) a dynamic portfolio model that integrates
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Identification in Parametric Models: The Minimum Hellinger Distance Criterion Econometrics Pub Date : 2022-02-21 David Pacini
This note studies the criterion for identifiability in parametric models based on the minimization of the Hellinger distance and exhibits its relationship to the identifiability criterion based on the Fisher matrix. It shows that the Hellinger distance criterion serves to establish identifiability of parameters of interest, or lack of it, in situations where the criterion based on the Fisher matrix
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The Impact of COVID-19 on Airfares—A Machine Learning Counterfactual Analysis Econometrics Pub Date : 2022-02-16 Florian Wozny
This paper studies the performance of machine learning predictions for the counterfactual analysis of air transport. It is motivated by the dynamic and universally regulated international air transport market, where ex post policy evaluations usually lack counterfactual control scenarios. As an empirical example, this paper studies the impact of the COVID-19 pandemic on airfares in 2020 as the difference
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Robust Estimation and Forecasting of Climate Change Using Score-Driven Ice-Age Models Econometrics Pub Date : 2022-02-16 Szabolcs Blazsek, Alvaro Escribano
We use data on the following climate variables for the period of the last 798 thousand years: global ice volume (Icet), atmospheric carbon dioxide level (CO2,t), and Antarctic land surface temperature (Tempt). Those variables are cyclical and are driven by the following strongly exogenous orbital variables: eccentricity of the Earth’s orbit, obliquity, and precession of the equinox. We introduce score-driven
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Acknowledgment to Reviewers of Econometrics in 2021 Econometrics Pub Date : 2022-01-31 Econometrics Editorial Office
Note: In lieu of an abstract, this is an excerpt from the first page. Rigorous peer-reviews are the basis of high-quality academic publishing [...]
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A New Estimator for Standard Errors with Few Unbalanced Clusters Econometrics Pub Date : 2022-01-21 Gianmaria Niccodemi, Tom Wansbeek
In linear regression analysis, the estimator of the variance of the estimator of the regression coefficients should take into account the clustered nature of the data, if present, since using the standard textbook formula will in that case lead to a severe downward bias in the standard errors. This idea of a cluster-robust variance estimator (CRVE) generalizes to clusters the classical heteroskedasticity-robust
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An Entropy-Based Approach for Nonparametrically Testing Simple Probability Distribution Hypotheses Econometrics Pub Date : 2022-01-14 Ron Mittelhammer, George Judge, Miguel Henry
In this paper, we introduce a flexible and widely applicable nonparametric entropy-based testing procedure that can be used to assess the validity of simple hypotheses about a specific parametric population distribution. The testing methodology relies on the characteristic function of the population probability distribution being tested and is attractive in that, regardless of the null hypothesis being
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The Age–Period–Cohort Problem in Hedonic House Prices Models Econometrics Pub Date : 2022-01-10 Chung-Yim Yiu, Ka-Shing Cheung
The age–period–cohort problem has been studied for decades but without resolution. There have been many suggested solutions to make the three effects estimable, but these solutions mostly exploit non-linear specifications. Yet, these approaches may suffer from misspecification or omitted variable bias. This paper is a practical-oriented study with an aim to empirically disentangle age–period–cohort
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Forecasting Real GDP Growth for Africa Econometrics Pub Date : 2022-01-06 Philip Hans Franses, Max Welz
We propose a simple and reproducible methodology to create a single equation forecasting model (SEFM) for low-frequency macroeconomic variables. Our methodology is illustrated by forecasting annual real GDP growth rates for 52 African countries, where the data are obtained from the World Bank and start in 1960. The models include lagged growth rates of other countries, as well as a cointegration relationship
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Forecasting Facing Economic Shifts, Climate Change and Evolving Pandemics Econometrics Pub Date : 2021-12-22 Jennifer L. Castle, Jurgen A. Doornik, David F. Hendry
By its emissions of greenhouse gases, economic activity is the source of climate change which affects pandemics that in turn can impact badly on economies. Across the three highly interacting disciplines in our title, time-series observations are measured at vastly different data frequencies: very low frequency at 1000-year intervals for paleoclimate, through annual, monthly to intra-daily for current
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An Exponential Endogenous Switching Regression with Correlated Random Coefficients Econometrics Pub Date : 2021-12-21 Myoung-Jin Keay
This paper presents a method for estimating the average treatment effects (ATE) of an exponential endogenous switching model where the coefficients of covariates in the structural equation are random and correlated with the binary treatment variable. The estimating equations are derived under some mild identifying assumptions. We find that the ATE is identified, although each coefficient in the structural
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On the Plausibility of the Latent Ignorability Assumption Econometrics Pub Date : 2021-12-09 Martin Huber
The estimation of the causal effect of an endogenous treatment based on an instrumental variable (IV) is often complicated by the non-observability of the outcome of interest due to attrition, sample selection, or survey non-response. To tackle the latter problem, the latent ignorability (LI) assumption imposes that attrition/sample selection is independent of the outcome conditional on the treatment
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Jointly Modeling Male and Female Labor Participation and Unemployment Econometrics Pub Date : 2021-12-07 David H. Bernstein, Andrew B. Martinez
The COVID-19 pandemic resulted in the most abrupt changes in U.S. labor force participation and unemployment since the Second World War, with different consequences for men and women. This paper models the U.S. labor market to help to interpret the pandemic’s effects. After replicating and extending Emerson’s (2011) model of the labor market, we formulate a joint model of male and female unemployment
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Interdependency Pattern Recognition in Econometrics: A Penalized Regularization Antidote Econometrics Pub Date : 2021-12-06 Kimon Ntotsis, Alex Karagrigoriou, Andreas Artemiou
When it comes to variable interpretation, multicollinearity is among the biggest issues that must be surmounted, especially in this new era of Big Data Analytics. Since even moderate size multicollinearity can prevent proper interpretation, special diagnostics must be recommended and implemented for identification purposes. Nonetheless, in the areas of econometrics and statistics, among other fields
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Does the Choice of Realized Covariance Measures Empirically Matter? A Bayesian Density Prediction Approach Econometrics Pub Date : 2021-12-06 Xin Jin, Jia Liu, Qiao Yang
This paper suggests a new approach to evaluate realized covariance (RCOV) estimators via their predictive power on return density. By jointly modeling returns and RCOV measures under a Bayesian framework, the predictive density of returns and ex-post covariance measures are bridged. The forecast performance of a covariance estimator can be assessed according to its improvement in return density forecasting
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Children’s Health Capital Investment: Effects of U.S. Infant Breastfeeding on Teenage Obesity Econometrics Pub Date : 2021-11-29 Albert Okunade, Ahmad Reshad Osmani, Toluwalope Ayangbayi, Adeyinka Kevin Okunade
Obesity, as a health and social problem with rising prevalence and soaring economic cost, is increasingly drawing scholarly and public policy attention. While many studies have suggested that infant breastfeeding protects against childhood obesity, empirical evidence on this causal relationship is fragile. Using the health capital development theory, this study exploited multiple data sources from
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Second-Order Least Squares Estimation in Nonlinear Time Series Models with ARCH Errors Econometrics Pub Date : 2021-11-27 Mustafa Salamh, Liqun Wang
Many financial and economic time series exhibit nonlinear patterns or relationships. However, most statistical methods for time series analysis are developed for mean-stationary processes that require transformation, such as differencing of the data. In this paper, we study a dynamic regression model with nonlinear, time-varying mean function, and autoregressive conditionally heteroscedastic errors