scholarly journals Applying Bayesian methods for macroeconomic modeling of business cycle phases

2021 ◽  
Vol 37 (2) ◽  
pp. 298-317
Author(s):  
Maria Guseva ◽  
◽  
Andrey Silaev ◽  
Keyword(s):  
Vector Autoregression ◽  
A Priori ◽  
Markov Switching ◽  
Wishart Distribution ◽  
Model Parameters ◽  
Impulse Response Functions ◽  
Us Economy ◽  
Gdp Growth Rate ◽  
The Usa ◽  
A Priori Distribution

In the present research, the features of applying two models for estimating macroeconomic dynamic in the USA are investigated: Bayesian vector autoregression and Bayesian vector autoregression with Markov switching. The research goal is to identify periods, structure of fluctuations and the main directions of interaction of the variables (real US GDP and employment) using Bayesian vector autoregression models. Models with Markov chains include many equations (structures). The switching mechanisms between these structures are controlled by an unobservable variable that follows a first-order Markov process. The analyzed variables were taken from the first quarter of 1953 to the third quarter of 2015. The model parameters were estimated on the basis of a prior for the multivariate normal distribution — the inverse Wishart distribution (a generalization of the Minnesota a priori distribution). Basing on the results of the estimation of the two-dimensional model with Markov Switching the average GDP growth rate and expected duration of phases was calculated. The estimated model is acceptable for describing the US economy and with high accuracy describes the probability of being in a particular phase in different periods of time. On the basis of medium-term forecasts, root mean squared errors of the forecast are calculated and a conclusion is made about the most appropriate model. Within the framework of this paper, impulse response functions are built allowing to evaluate how variables in the model react on fluctuations, shocks.

Multivariate Markov-switching score-driven models: an application to the global crude oil market

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Szabolcs Blazsek ◽  
Alvaro Escribano ◽  
Adrian Licht
Keyword(s):  
Crude Oil ◽  
Moving Average ◽  
Local Level ◽  
Markov Switching ◽  
Impulse Response Functions ◽  
Common Trends ◽  
Vector Autoregressive ◽  
Oil Market ◽  
Long Run ◽  
Special Cases

Abstract A new class of multivariate nonlinear quasi-vector autoregressive (QVAR) models is introduced. It is a Markov switching score-driven model with stochastic seasonality for the multivariate t-distribution (MS-Seasonal-t-QVAR). As an extension, we allow for the possibility of having common-trends and nonlinear co-integration. Score-driven nonlinear updates of local level and seasonality are used, which are robust to outliers within each regime. We show that VAR integrated moving average (VARIMA) type filters are special cases of QVAR filters. Using exclusion, sign, and elasticity identification restrictions in MS-Seasonal-t-QVAR with common-trends, we provide short-run and long-run impulse response functions for the global crude oil market.

Generalised DOPs with Consideration of the Influence Function of Signal-in-Space Errors

2011 ◽  
Vol 64 (S1) ◽  
pp. S3-S18 ◽  
Author(s):  
Yuanxi Yang ◽  
Jinlong Li ◽  
Junyi Xu ◽  
Jing Tang
Keyword(s):  
Least Squares ◽  
Stochastic Models ◽  
Influence Function ◽  
A Priori ◽  
Functional Model ◽  
Global Navigation Satellite Systems ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Integrated Navigation ◽  
Satellite Systems

Integrated navigation using multiple Global Navigation Satellite Systems (GNSS) is beneficial to increase the number of observable satellites, alleviate the effects of systematic errors and improve the accuracy of positioning, navigation and timing (PNT). When multiple constellations and multiple frequency measurements are employed, the functional and stochastic models as well as the estimation principle for PNT may be different. Therefore, the commonly used definition of “dilution of precision (DOP)” based on the least squares (LS) estimation and unified functional and stochastic models will be not applicable anymore. In this paper, three types of generalised DOPs are defined. The first type of generalised DOP is based on the error influence function (IF) of pseudo-ranges that reflects the geometry strength of the measurements, error magnitude and the estimation risk criteria. When the least squares estimation is used, the first type of generalised DOP is identical to the one commonly used. In order to define the first type of generalised DOP, an IF of signal–in-space (SIS) errors on the parameter estimates of PNT is derived. The second type of generalised DOP is defined based on the functional model with additional systematic parameters induced by the compatibility and interoperability problems among different GNSS systems. The third type of generalised DOP is defined based on Bayesian estimation in which the a priori information of the model parameters is taken into account. This is suitable for evaluating the precision of kinematic positioning or navigation. Different types of generalised DOPs are suitable for different PNT scenarios and an example for the calculation of these DOPs for multi-GNSS systems including GPS, GLONASS, Compass and Galileo is given. New observation equations of Compass and GLONASS that may contain additional parameters for interoperability are specifically investigated. It shows that if the interoperability of multi-GNSS is not fulfilled, the increased number of satellites will not significantly reduce the generalised DOP value. Furthermore, the outlying measurements will not change the original DOP, but will change the first type of generalised DOP which includes a robust error IF. A priori information of the model parameters will also reduce the DOP.

Macroeconomic conditions are only an illusion, not the solution, to firm sustainability: unveiling the new dimension of stakeholder well-being

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Zahid Irshad Younas ◽  
Mahvesh Khan ◽  
Mamdouh Abdulaziz Saleh Al-Faryan
Keyword(s):  
Well Being ◽  
Developed Countries ◽  
Macroeconomic Performance ◽  
Content Type ◽  
Macroeconomic Conditions ◽  
Us Economy ◽  
The Us ◽  
The Usa ◽  
Negative Impacts ◽  
The One

Purpose The purpose of the study is to explore the misconception that in developed countries, macroeconomic performance lead to sustainable firms or improves stakeholder well-being. The results may be the opposite or even worse. Design/methodology/approach This study examined this misconception using balanced panel data from 1,122 firms from different sectors of the US economy and data on macroeconomic performance from the World Bank. Findings The results of the one-step generalised method of moments indicate that most macroeconomic performance indicators had significant and negative impacts on firm sustainability and stakeholder well-being. Practical implications From a societal perspective, the results illustrate that the fruits of macroeconomic performance of the US economy do not reach stakeholders through firms’ sustainability. Thus, linking the economy’s macroeconomic performance with firm sustainability is vital for sustainably uplifting society and for stakeholder well-being. Originality/value From a policy perspective, this study reveals that the greater focus on macroeconomic performance in the USA over the past decades has resulted in lower firm sustainability because of the malfunctioning of social, economic, environmental and governance factors. This has negatively influenced stakeholder well-being in the country.

A Comparison of Approaches to Select the Informativeness of Priors in BVARs

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Jan Prüser ◽  
Christoph Hanck
Keyword(s):  
Impulse Response ◽  
Prior Information ◽  
Hierarchical Modeling ◽  
Small Samples ◽  
Model Parameters ◽  
Impulse Response Functions ◽  
Response Functions ◽  
Vector Autoregressions ◽  
The Rich ◽  
Bayesian Vars

Abstract Vector autoregressions (VARs) are richly parameterized time series models that can capture complex dynamic interrelationships among macroeconomic variables. However, in small samples the rich parametrization of VAR models may come at the cost of overfitting the data, possibly leading to imprecise inference for key quantities of interest such as impulse response functions (IRFs). Bayesian VARs (BVARs) can use prior information to shrink the model parameters, potentially avoiding such overfitting. We provide a simulation study to compare, in terms of the frequentist properties of the estimates of the IRFs, useful strategies to select the informativeness of the prior. The study reveals that prior information may help to obtain more precise estimates of impulse response functions than classical OLS-estimated VARs and more accurate coverage rates of error bands in small samples. Strategies based on selecting the prior hyperparameters of the BVAR building on empirical or hierarchical modeling perform particularly well.

scholarly journals Forecasting Cattle Prices in the Presence of Structural Change

1992 ◽  
Vol 24 (2) ◽  
pp. 11-22 ◽  
Author(s):  
Barry K. Goodwin
Keyword(s):  
Structural Change ◽  
A Priori ◽  
Arima Model ◽  
Var Model ◽  
Impulse Response Functions ◽  
Cattle Industry ◽  
Vector Autoregressive ◽  
Economic Relationships ◽  
New Information ◽  
Out Of Sample

AbstractRecent empirical research and developments in the cattle industry suggest several reasons to suspect structural change in economic relationships determining cattle prices. Standard forecasting models may ignore structural change and may produce biased and misleading forecasts. Vector autoregressive (VAR) models that allow parameters to vary with time are used to forecast quarterly cattle prices. The VAR procedures are flexible in that they allow the identification of structural change that begins at an a priori unknown point and occurs gradually. The results indicate that the lowest RMSE for out-of-sample forecasts of cattle prices is obtained using a gradually switching VAR model. However, differences between the gradually switching VAR model and a univariate ARIMA model are not strongly significant. Impulse response functions indicate that adjustments of cattle prices to new information have become faster in recent years.

scholarly journals Technical Note: Improved sampling of behavioral subsurface flow model parameters using active subspaces

2020 ◽  
Vol 24 (9) ◽  
pp. 4567-4574
Author(s):  
Daniel Erdal ◽  
Olaf A. Cirpka
Keyword(s):  
A Priori ◽  
Behavioral Model ◽  
Technical Note ◽  
Sampling Scheme ◽  
Model Parameters ◽  
Test Case ◽  
Sampling Efficiency ◽  
Subspace Decomposition ◽  
Plausible Model ◽  
Active Subspaces

Abstract. In global sensitivity analysis and ensemble-based model calibration, it is essential to create a large enough sample of model simulations with different parameters that all yield plausible model results. This can be difficult if a priori plausible parameter combinations frequently yield non-behavioral model results. In a previous study (Erdal and Cirpka, 2019), we developed and tested a parameter-sampling scheme based on active-subspace decomposition. While in principle this scheme worked well, it still implied testing a substantial fraction of parameter combinations that ultimately had to be discarded because of implausible model results. This technical note presents an improved sampling scheme and illustrates its simplicity and efficiency by a small test case. The new sampling scheme can be tuned to either outperform the original implementation by improving the sampling efficiency while maintaining the accuracy of the result or by improving the accuracy of the result while maintaining the sampling efficiency.

Inversion and uncertainty estimation of gravity data using simulated annealing: An application over Lake Vostok, East Antarctica

Geophysics ◽  
2005 ◽  
Vol 70 (1) ◽  
pp. J1-J12 ◽  
Author(s):  
Lopamudra Roy ◽  
Mrinal K. Sen ◽  
Donald D. Blankenship ◽  
Paul L. Stoffa ◽  
Thomas G. Richter
Keyword(s):  
Simulated Annealing ◽  
Gravity Data ◽  
A Priori ◽  
Model Space ◽  
Uncertainty Estimation ◽  
East Antarctica ◽  
Airborne Gravity ◽  
Model Parameters ◽  
Data Set ◽  
Lake Vostok

Interpretation of gravity data warrants uncertainty estimation because of its inherent nonuniqueness. Although the uncertainties in model parameters cannot be completely reduced, they can aid in the meaningful interpretation of results. Here we have employed a simulated annealing (SA)–based technique in the inversion of gravity data to derive multilayered earth models consisting of two and three dimensional bodies. In our approach, we assume that the density contrast is known, and we solve for the coordinates or shapes of the causative bodies, resulting in a nonlinear inverse problem. We attempt to sample the model space extensively so as to estimate several equally likely models. We then use all the models sampled by SA to construct an approximate, marginal posterior probability density function (PPD) in model space and several orders of moments. The correlation matrix clearly shows the interdependence of different model parameters and the corresponding trade-offs. Such correlation plots are used to study the effect of a priori information in reducing the uncertainty in the solutions. We also investigate the use of derivative information to obtain better depth resolution and to reduce underlying uncertainties. We applied the technique on two synthetic data sets and an airborne-gravity data set collected over Lake Vostok, East Antarctica, for which a priori constraints were derived from available seismic and radar profiles. The inversion results produced depths of the lake in the survey area along with the thickness of sediments. The resulting uncertainties are interpreted in terms of the experimental geometry and data error.

scholarly journals Financialization at a watershed in the USA

2018 ◽  
Vol 22 (5) ◽  
pp. 488-508 ◽  
Author(s):  
Costas Lapavitsas ◽  
Ivan Mendieta-Muñoz
Keyword(s):  
Great Recession ◽  
Structural Breaks ◽  
Financial Regulation ◽  
Financial Sector ◽  
State Intervention ◽  
Mortgage Debt ◽  
The Great Recession ◽  
Us Economy ◽  
Macroeconomic Indicators ◽  
The Usa

In the period following the Great Recession of 2007–2009 the financialization of the US economy reached a watershed characterized by stagnant financial profits, falling proportions of financial sector and mortgage debt, and rising proportion of public debt. The main macroeconomic indicators of financialization in the USA show structural breaks that can be dated around the period of the Great Recession. The reliance of households on the formal financial system appears to have weakened for the first time since the early 1980s. The financial sector has lacked the dynamism of the previous three decades, becoming more reliant on government. The state has increased its own indebtedness and supported large financial institutions via unconventional monetary policy measures. At the same time, state intervention has tightened the regulatory framework for big banks. The future path of financialization in the USA will depend heavily on government policy with regard to state debt and financial regulation, although the scope for boosting financialization is narrow.

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