This essay is about Bayesian econometrics with a purpose. Specifically, six societal challenges and research opportunities that confront twenty first century Bayesian econometricians are discussed using an important feature of modern Bayesian econometrics: conditional probabilities of a wide range of economic events of interest can be evaluated by using simulation-based Bayesian inference. The enormous advances in hardware and software have made this Bayesian computational approach a very attractive vehicle of research in many subfields in economics where novel data patterns and substantial model complexity are predominant. In this essay the following challenges and opportunities are briefly discussed, including the scientific results obtained in the twentieth century leading up to these challenges: Posterior and predictive analysis of everything: connecting micro-economic causality with macro-economic issues; the need for speed: model complexity and the golden age of algorithms; learning about models, forecasts and policies including their uncertainty; temporal distributional change due to polarisation, imbalances and shocks; climate change and the macroeconomy; finally and most importantly, widespread, accessible, advanced high-level training.
{"title":"Challenges and Opportunities for Twenty First Century Bayesian Econometricians: A Personal View","authors":"Herman K. van Dijk","doi":"10.1515/snde-2024-0003","DOIUrl":"https://doi.org/10.1515/snde-2024-0003","url":null,"abstract":"This essay is about <jats:italic>Bayesian econometrics with a purpose</jats:italic>. Specifically, six societal challenges and research opportunities that confront twenty first century Bayesian econometricians are discussed using an important feature of modern Bayesian econometrics: conditional probabilities of a wide range of economic events of interest can be evaluated by using simulation-based Bayesian inference. The enormous advances in hardware and software have made this Bayesian computational approach a very attractive vehicle of research in many subfields in economics where novel data patterns and substantial model complexity are predominant. In this essay the following challenges and opportunities are briefly discussed, including the scientific results obtained in the twentieth century leading up to these challenges: Posterior and predictive analysis of everything: connecting micro-economic causality with macro-economic issues; the need for speed: model complexity and the golden age of algorithms; learning about models, forecasts and policies including their uncertainty; temporal distributional change due to polarisation, imbalances and shocks; climate change and the macroeconomy; finally and most importantly, widespread, accessible, advanced high-level training.","PeriodicalId":501448,"journal":{"name":"Studies in Nonlinear Dynamics & Econometrics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140115746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we extend the recent Gaussian autoregressive conditional beta (Gaussian-ACB) model from the literature on score-driven models. In the new asset pricing model, named the t generalized ACB (t-GACB) model, a multivariate score-driven filter for the t-distribution drives dynamic interaction effects among the beta coefficients. We present the econometric formulation and statistical inference for the t-GACB model, which we apply to 15 stocks from the United States (US) from 1999 to 2022. In our empirical application, we use the three Fama–French factors as asset pricing factors, and we also use the Volatility Index, TED Spread, and ICE BofA US High Yield Index Option-Adjusted Spread as exogenous explanatory variables that influence the beta coefficients. We compare the statistical and realized tracking error performances of the Gaussian-ACB, t-ACB, and t-GACB models, and we find that the t-GACB model improves the Gaussian-ACB model.
{"title":"Generalized Autoregressive Conditional Betas: A New Multivariate Score-Driven Filter","authors":"Szabolcs Blazsek, August Jörding, Simran Rai","doi":"10.1515/snde-2023-0019","DOIUrl":"https://doi.org/10.1515/snde-2023-0019","url":null,"abstract":"In this paper, we extend the recent Gaussian autoregressive conditional beta (Gaussian-ACB) model from the literature on score-driven models. In the new asset pricing model, named the <jats:italic>t</jats:italic> generalized ACB (<jats:italic>t</jats:italic>-GACB) model, a multivariate score-driven filter for the <jats:italic>t</jats:italic>-distribution drives dynamic interaction effects among the beta coefficients. We present the econometric formulation and statistical inference for the <jats:italic>t</jats:italic>-GACB model, which we apply to 15 stocks from the United States (US) from 1999 to 2022. In our empirical application, we use the three Fama–French factors as asset pricing factors, and we also use the Volatility Index, TED Spread, and ICE BofA US High Yield Index Option-Adjusted Spread as exogenous explanatory variables that influence the beta coefficients. We compare the statistical and realized tracking error performances of the Gaussian-ACB, <jats:italic>t</jats:italic>-ACB, and <jats:italic>t</jats:italic>-GACB models, and we find that the <jats:italic>t</jats:italic>-GACB model improves the Gaussian-ACB model.","PeriodicalId":501448,"journal":{"name":"Studies in Nonlinear Dynamics & Econometrics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140071088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper introduces a holistic framework that integrates copula modeling and information-theoretic measures to examine the information content of inflation expectations. Copulas are used to capture the dynamic dependence between inflation and expectations, accounting for extreme events and tail dependence. Information-theoretic measures are employed to quantify the information that expectations provide about inflation. Theoretical results establish a link between copula entropy and mutual information, propose a lower bound for copula entropy, and provide a practical tool for central banks to anchor expectations to achieve inflation targets. Empirical findings reveal higher uncertainty in the tails of the joint distribution and underscore the meaningful information carried by expected inflation for forecasting inflation, particularly with shorter-term expectations.
{"title":"Information Content of Inflation Expectations: A Copula-Based Model","authors":"Omid M. Ardakani","doi":"10.1515/snde-2023-0075","DOIUrl":"https://doi.org/10.1515/snde-2023-0075","url":null,"abstract":"This paper introduces a holistic framework that integrates copula modeling and information-theoretic measures to examine the information content of inflation expectations. Copulas are used to capture the dynamic dependence between inflation and expectations, accounting for extreme events and tail dependence. Information-theoretic measures are employed to quantify the information that expectations provide about inflation. Theoretical results establish a link between copula entropy and mutual information, propose a lower bound for copula entropy, and provide a practical tool for central banks to anchor expectations to achieve inflation targets. Empirical findings reveal higher uncertainty in the tails of the joint distribution and underscore the meaningful information carried by expected inflation for forecasting inflation, particularly with shorter-term expectations.","PeriodicalId":501448,"journal":{"name":"Studies in Nonlinear Dynamics & Econometrics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140019449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper applies the chaos control method (the OGY method) proposed by Ott, E., C. Grebogi, and J. A. Yorke. (1990. “Controlling Chaos.” Physical Review Letters 64: 1196–9) to policy-making in macroeconomics. This paper demonstrates that the monetary equilibrium paths in a discrete-time, two-dimensional overlapping generations model exhibit chaotic fluctuations depending on the money supply rate and the elasticity of substitution between capital and labor under the assumption of the constant elasticity of substitution (CES) production function. We also show that the chaotic fluctuations can be stabilized by controlling the money supply rate by using the OGY method and that even when the OGY method does not work due to periodic attractors, adding moderate stochastic shocks to the model can successfully stabilize the economy.
本文应用了 Ott, E., C. Grebogi 和 J. A. Yorke 提出的混沌控制方法（OGY 方法）。(1990. "Controlling Chaos." Physical Review Letters 64: 1196-9) 提出的混沌控制法（OGY 法）应用于宏观经济决策。本文证明，在恒定替代弹性（CES）生产函数的假设下，离散时间二维世代重叠模型中的货币均衡路径表现出混沌波动，它取决于货币供应率以及资本和劳动力之间的替代弹性。我们还证明，利用 OGY 方法控制货币供应率可以稳定混沌波动，而且即使 OGY 方法因周期性吸引子而失效，在模型中添加适度的随机冲击也能成功稳定经济。
{"title":"Controlling Chaotic Fluctuations through Monetary Policy","authors":"Takao Asano, Akihisa Shibata, Masanori Yokoo","doi":"10.1515/snde-2023-0015","DOIUrl":"https://doi.org/10.1515/snde-2023-0015","url":null,"abstract":"This paper applies the chaos control method (the OGY method) proposed by Ott, E., C. Grebogi, and J. A. Yorke. (1990. “Controlling Chaos.” <jats:italic>Physical Review Letters</jats:italic> 64: 1196–9) to policy-making in macroeconomics. This paper demonstrates that the monetary equilibrium paths in a discrete-time, two-dimensional overlapping generations model exhibit chaotic fluctuations depending on the money supply rate and the elasticity of substitution between capital and labor under the assumption of the constant elasticity of substitution (CES) production function. We also show that the chaotic fluctuations can be stabilized by controlling the money supply rate by using the OGY method and that even when the OGY method does not work due to periodic attractors, adding moderate stochastic shocks to the model can successfully stabilize the economy.","PeriodicalId":501448,"journal":{"name":"Studies in Nonlinear Dynamics & Econometrics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139415541","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Using quantile maximization decision theory, this paper considers a quantile-based Euler equation that states that the asset price is a function of the quantiles of the payoff, consumption growth, the stochastic discount factor, risk aversion, and the distribution of the consumption growth rate. We use a more general distribution assumption (log-elliptical distributions) than the log-normality of the consumption growth rate assumed in the literature. The simulation results show that: (1) the higher the downside risk aversion, the lower the constant relative risk aversion; (2) the heavier the tails of the Student-t distribution, the higher the risk aversion for each level of downside risk aversion; and (3) the curve of the relationship between risk aversion and downside risk aversion shifts upward when the normality assumption is dropped, and the magnitude of this shift is high even for high degrees of freedom of the Student-t distribution. Our results suggest that using normally distributed errors to model stock returns and consumption growth rates could lead to an underestimation of the risk aversion coefficient.
{"title":"Investigating the Impact of Consumption Distribution on CRRA Estimation: Quantile-CCAPM-Based Approach","authors":"Sofia B. Ramos, A. Taamouti, Helena Veiga","doi":"10.1515/snde-2023-0005","DOIUrl":"https://doi.org/10.1515/snde-2023-0005","url":null,"abstract":"Abstract Using quantile maximization decision theory, this paper considers a quantile-based Euler equation that states that the asset price is a function of the quantiles of the payoff, consumption growth, the stochastic discount factor, risk aversion, and the distribution of the consumption growth rate. We use a more general distribution assumption (log-elliptical distributions) than the log-normality of the consumption growth rate assumed in the literature. The simulation results show that: (1) the higher the downside risk aversion, the lower the constant relative risk aversion; (2) the heavier the tails of the Student-t distribution, the higher the risk aversion for each level of downside risk aversion; and (3) the curve of the relationship between risk aversion and downside risk aversion shifts upward when the normality assumption is dropped, and the magnitude of this shift is high even for high degrees of freedom of the Student-t distribution. Our results suggest that using normally distributed errors to model stock returns and consumption growth rates could lead to an underestimation of the risk aversion coefficient.","PeriodicalId":501448,"journal":{"name":"Studies in Nonlinear Dynamics & Econometrics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139380024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mei Xue, Daniela Mihai, Madalina Brutu, Luigi Popescu, Crenguta Ileana Sinisi, Ajay Bansal, Mady A. A. Mohammad, Taseer Muhammad, Malik Shahzad Shabbir
The world today presents significant environmental concerns for humans, such as smog and warmer temperatures, but we also need to think about how to accomplish economic growth that is sustainable. Therefore, this exploration researches the asymmetric effect of renewable energy consumption, economic growth and financial development on carbon emanation in the emerging economies. For this reason, this investigation uses Panel ARDL and PMG estimator. The consequences of PMG estimator demonstrate that information and communication technologies reduce the carbon emanations in the sample region. Additionally, renewable energy consumption also impedes the carbon emanations. The results also suggest that financial development additionally builds the carbon emissions but the impact is very minor. Finally, economic growth and population are also contributing toward carbon emissions. The power effective recommendation is vital to present the ICT assistance to confine the utilization of obsolete machinery for power generation.
{"title":"Examining the Impact of Energy Policies on CO2 Emissions with Information and Communication Technologies and Renewable Energy","authors":"Mei Xue, Daniela Mihai, Madalina Brutu, Luigi Popescu, Crenguta Ileana Sinisi, Ajay Bansal, Mady A. A. Mohammad, Taseer Muhammad, Malik Shahzad Shabbir","doi":"10.1515/snde-2022-0065","DOIUrl":"https://doi.org/10.1515/snde-2022-0065","url":null,"abstract":"The world today presents significant environmental concerns for humans, such as smog and warmer temperatures, but we also need to think about how to accomplish economic growth that is sustainable. Therefore, this exploration researches the asymmetric effect of renewable energy consumption, economic growth and financial development on carbon emanation in the emerging economies. For this reason, this investigation uses Panel ARDL and PMG estimator. The consequences of PMG estimator demonstrate that information and communication technologies reduce the carbon emanations in the sample region. Additionally, renewable energy consumption also impedes the carbon emanations. The results also suggest that financial development additionally builds the carbon emissions but the impact is very minor. Finally, economic growth and population are also contributing toward carbon emissions. The power effective recommendation is vital to present the ICT assistance to confine the utilization of obsolete machinery for power generation.","PeriodicalId":501448,"journal":{"name":"Studies in Nonlinear Dynamics & Econometrics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139373563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Szabolcs Blazsek, Adrián Licht, A. Ayala, Su-Ping Liu
Abstract We use a score-driven minimum mean-squared error (MSE) signal extraction method and perform inflation smoothing for China and the ASEAN-10 countries. Our focus on China and ASEAN-10 countries is motivated by the significant historical variation in inflation rates, e.g. during the 1997 Asian Financial Crisis, the 2007–2008 Financial Crisis, the COVID-19 Pandemic, and the Russian Invasion of Ukraine. Some advantages of the score-driven signal extraction method are that it uses dynamic mean and volatility filters, it considers stationary or non-stationary mean dynamics, it is computationally fast, it is robust to extreme observations, it uses information-theoretically optimal updating mechanisms for both mean and volatility, it uses closed-form formulas for smoothed signals, and parameters are estimated by using the maximum likelihood (ML) method for which the asymptotic properties of estimates are known. In the empirical application, we present the political and economic conditions for each country and analyze the evolution and determinants of the core inflation rate.
{"title":"Core Inflation Rate for China and the ASEAN-10 Countries: Smoothed Signal for Score-Driven Local Level Plus Scale Models","authors":"Szabolcs Blazsek, Adrián Licht, A. Ayala, Su-Ping Liu","doi":"10.2139/ssrn.4644236","DOIUrl":"https://doi.org/10.2139/ssrn.4644236","url":null,"abstract":"Abstract We use a score-driven minimum mean-squared error (MSE) signal extraction method and perform inflation smoothing for China and the ASEAN-10 countries. Our focus on China and ASEAN-10 countries is motivated by the significant historical variation in inflation rates, e.g. during the 1997 Asian Financial Crisis, the 2007–2008 Financial Crisis, the COVID-19 Pandemic, and the Russian Invasion of Ukraine. Some advantages of the score-driven signal extraction method are that it uses dynamic mean and volatility filters, it considers stationary or non-stationary mean dynamics, it is computationally fast, it is robust to extreme observations, it uses information-theoretically optimal updating mechanisms for both mean and volatility, it uses closed-form formulas for smoothed signals, and parameters are estimated by using the maximum likelihood (ML) method for which the asymptotic properties of estimates are known. In the empirical application, we present the political and economic conditions for each country and analyze the evolution and determinants of the core inflation rate.","PeriodicalId":501448,"journal":{"name":"Studies in Nonlinear Dynamics & Econometrics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139392057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Borys Koval, Sylvia Frühwirth-Schnatter, Leopold Sögner
This article considers a stable vector autoregressive (VAR) model and investigates return predictability in a Bayesian context. The bivariate VAR system comprises asset returns and a further prediction variable, such as the dividend-price ratio, and allows pinning down the question of return predictability to the value of one particular model parameter. We develop a new shrinkage type prior for this parameter and compare our Bayesian approach to ordinary least squares estimation and to the reduced-bias estimator proposed in Amihud and Hurvich (2004. “Predictive Regressions: A Reduced-Bias Estimation Method.” Journal of Financial and Quantitative Analysis 39: 813–41). A simulation study shows that the Bayesian approach dominates the reduced-bias estimator in terms of observed size (false positive) and power (false negative). We apply our methodology to a system comprising annual CRSP value-weighted returns running, respectively, from 1926 to 2004 and from 1953 to 2021, and the logarithmic dividend-price ratio. For the first sample, the Bayesian approach supports the hypothesis of no return predictability, while for the second data set weak evidence for predictability is observed. Then, instead of the dividend-price ratio, some prediction variables proposed in Welch and Goyal (2008. “A Comprehensive Look at the Empirical Performance of Equity Premium Prediction.” Review of Financial Studies 21: 1455–508) are used. Also with these prediction variables, only weak evidence for return predictability is supported by Bayesian testing. These results are corroborated with an out-of-sample forecasting analysis.
{"title":"Bayesian Reconciliation of Return Predictability","authors":"Borys Koval, Sylvia Frühwirth-Schnatter, Leopold Sögner","doi":"10.1515/snde-2022-0110","DOIUrl":"https://doi.org/10.1515/snde-2022-0110","url":null,"abstract":"This article considers a stable vector autoregressive (VAR) model and investigates return predictability in a Bayesian context. The bivariate VAR system comprises asset returns and a further prediction variable, such as the dividend-price ratio, and allows pinning down the question of return predictability to the value of one particular model parameter. We develop a new shrinkage type prior for this parameter and compare our Bayesian approach to ordinary least squares estimation and to the reduced-bias estimator proposed in Amihud and Hurvich (2004. “Predictive Regressions: A Reduced-Bias Estimation Method.” <jats:italic>Journal of Financial and Quantitative Analysis</jats:italic> 39: 813–41). A simulation study shows that the Bayesian approach dominates the reduced-bias estimator in terms of observed size (false positive) and power (false negative). We apply our methodology to a system comprising annual CRSP value-weighted returns running, respectively, from 1926 to 2004 and from 1953 to 2021, and the logarithmic dividend-price ratio. For the first sample, the Bayesian approach supports the hypothesis of no return predictability, while for the second data set weak evidence for predictability is observed. Then, instead of the dividend-price ratio, some prediction variables proposed in Welch and Goyal (2008. “A Comprehensive Look at the Empirical Performance of Equity Premium Prediction.” <jats:italic>Review of Financial Studies</jats:italic> 21: 1455–508) are used. Also with these prediction variables, only weak evidence for return predictability is supported by Bayesian testing. These results are corroborated with an out-of-sample forecasting analysis.","PeriodicalId":501448,"journal":{"name":"Studies in Nonlinear Dynamics & Econometrics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139056095","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Liana Jacobi, C. Kwok, A. Ramírez‐Hassan, N. Nghiem
Abstract Increases in the use of Bayesian inference in applied analysis, the complexity of estimated models, and the popularity of efficient Markov chain Monte Carlo (MCMC) inference under conjugate priors have led to more scrutiny regarding the specification of the parameters in prior distributions. Impact of prior parameter assumptions on posterior statistics is commonly investigated in terms of local or pointwise assessments, in the form of derivatives or more often multiple evaluations under a set of alternative prior parameter specifications. This paper expands upon these localized strategies and introduces a new approach based on the graph of posterior statistics over prior parameter regions (sensitivity manifolds) that offers additional measures and graphical assessments of prior parameter dependence. Estimation is based on multiple point evaluations with Gaussian processes, with efficient selection of evaluation points via active learning, and is further complemented with derivative information. The application introduces a strategy to assess prior parameter dependence in a multivariate demand model with a high dimensional prior parameter space, where complex prior-posterior dependence arises from model parameter constraints. The new measures uncover a considerable prior dependence beyond parameters suggested by theory, and reveal novel interactions between the prior parameters and the elasticities.
{"title":"Posterior Manifolds over Prior Parameter Regions: Beyond Pointwise Sensitivity Assessments for Posterior Statistics from MCMC Inference","authors":"Liana Jacobi, C. Kwok, A. Ramírez‐Hassan, N. Nghiem","doi":"10.1515/snde-2022-0116","DOIUrl":"https://doi.org/10.1515/snde-2022-0116","url":null,"abstract":"Abstract Increases in the use of Bayesian inference in applied analysis, the complexity of estimated models, and the popularity of efficient Markov chain Monte Carlo (MCMC) inference under conjugate priors have led to more scrutiny regarding the specification of the parameters in prior distributions. Impact of prior parameter assumptions on posterior statistics is commonly investigated in terms of local or pointwise assessments, in the form of derivatives or more often multiple evaluations under a set of alternative prior parameter specifications. This paper expands upon these localized strategies and introduces a new approach based on the graph of posterior statistics over prior parameter regions (sensitivity manifolds) that offers additional measures and graphical assessments of prior parameter dependence. Estimation is based on multiple point evaluations with Gaussian processes, with efficient selection of evaluation points via active learning, and is further complemented with derivative information. The application introduces a strategy to assess prior parameter dependence in a multivariate demand model with a high dimensional prior parameter space, where complex prior-posterior dependence arises from model parameter constraints. The new measures uncover a considerable prior dependence beyond parameters suggested by theory, and reveal novel interactions between the prior parameters and the elasticities.","PeriodicalId":501448,"journal":{"name":"Studies in Nonlinear Dynamics & Econometrics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138944714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We construct a Financial Conditions Index (FCI) for the United States using a dataset that features many missing observations. The novel combination of probabilistic principal component techniques and a Bayesian factor-augmented VAR model resolves the challenges posed by data points being unavailable within a high-frequency dataset. Even with up to 62 % of the data missing, the new approach yields a less noisy FCI that tracks the movement of 22 underlying financial variables more accurately both in-sample and out-of-sample.
我们利用具有大量缺失观测数据的数据集构建了美国金融状况指数（FCI）。概率主成分技术与贝叶斯因子增强 VAR 模型的新颖结合，解决了高频数据集中数据点缺失所带来的挑战。即使有多达 62% 的数据缺失，新方法也能产生噪声较小的 FCI，在样本内和样本外都能更准确地跟踪 22 个基础金融变量的变动。
{"title":"Financial Condition Indices in an Incomplete Data Environment","authors":"Miguel C. Herculano, Punnoose Jacob","doi":"10.1515/snde-2022-0115","DOIUrl":"https://doi.org/10.1515/snde-2022-0115","url":null,"abstract":"We construct a Financial Conditions Index (FCI) for the United States using a dataset that features many missing observations. The novel combination of probabilistic principal component techniques and a Bayesian factor-augmented VAR model resolves the challenges posed by data points being unavailable within a high-frequency dataset. Even with up to 62 % of the data missing, the new approach yields a less noisy FCI that tracks the movement of 22 underlying financial variables more accurately both in-sample and out-of-sample.","PeriodicalId":501448,"journal":{"name":"Studies in Nonlinear Dynamics & Econometrics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138825099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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