Generalizing the Bayesian Vector Autoregression Approach for Regional Interindustry Employment Forecasting

Partridge, Mark D.; Rickman, Dan S.

doi:10.1080/07350015.1998.10524735

Cited by 18 publications

(15 citation statements)

References 27 publications

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“…In fact, researchers have applied Bayesian approaches to regional input‐output models to derive stochastic interindustry‐based results. ( 44‐47 ) Of course, results from systems econometric time‐series models can be interpreted in a stochastic vein as well, albeit their stochastic character is implicit and requires substantially more time‐series data.…”

Section: Tools and Data For Estimating Local State Regional Andmentioning

confidence: 99%

Understanding the Economic Costs and Benefits of Catastrophes and Their Aftermath: A Review and Suggestions for the U.S. Federal Government

Greenberg¹,

Lahr²,

Mantell³

2007

Risk Analysis

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The number and magnitude of devastating natural and human events make it imperative that we actively and systematically estimate the costs and benefits of policy decisions in affected localities, regions, states, and nations. Such strategic risk management preparedness efforts should forecast well into the future and include scenarios with and without enhanced engineered structures; with reduced vulnerability through land-use planning and design; with the impact of resiliency and mitigation; with evacuation and relocation; and with the costs and benefits of recovery and restoration. We describe different kinds of regional economic models that can be used in these preparedness planning efforts, explore critical data needs, and advocate a shared federal-state-local strategic planning effort to accomplish the objective.

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Section: Tools and Data For Estimating Local State Regional Andmentioning

confidence: 99%

Understanding the Economic Costs and Benefits of Catastrophes and Their Aftermath: A Review and Suggestions for the U.S. Federal Government

Greenberg¹,

Lahr²,

Mantell³

2007

Risk Analysis

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“…The third thrust of this paper is a focus on the performance of the Bayesian perspective (in relation to ML/FDT) as a function of the specific prior implemented. Using nontrivial priors in time series analysis has a long history in economics, where Bayesian methods are used to estimate coefficients (often) of vector auto-regressive (VAR) models (Litterman 1986;Giannone et al 2015;Partridge and Rickman 1998;Doan et al 1983). There have been various attempts to introduce Bayesian learning of parameters in stochastic differential equations, but usually require some assumptions or approximations of the underlying likelihood distribution (Eraker 2001;Singer 2004;Beskos et al 2006;Karimi and McAuley 2016;Batz et al 2018;Tian et al 2014;Møller et al 2011).…”

Section: Introductionmentioning

confidence: 99%

A Bayesian Approach to Regional Decadal Predictability: Sparse Parameter Estimation in High-Dimensional Linear Inverse Models of High-Latitude Sea Surface Temperature Variability

2020

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Stochastic reduced models are an important tool in climate systems whose many spatial and temporal scales cannot be fully discretized or underlying physics may not be fully accounted for. One form of reduced model, the linear inverse model (LIM), has been widely used for regional climate predictability studies - typically focusing more on tropical or mid-latitude studies. However, most LIM fitting techniques rely on point estimation techniques deriving from fluctuation-dissipation theory. In this methodological study we explore the use of Bayesian inference techniques for LIM parameter estimation of sea surface temperature (SST), to quantify the skillful decadal predictability of Bayesian LIM models at high latitudes. We show that Bayesian methods, when compared to traditional point estimation methods for LIM-type models, provide better calibrated probabilistic skill, while simultaneously providing better point estimates due to the regularization effect of the prior distribution in high-dimensional problems. We compare the effect of several priors, as well as maximum likelihood estimates, on (1) estimating parameter values on a perfect model experiment and (2) producing calibrated 1-year SST anomaly forecast distributions using a pre-industrial control run of the Community Earth System Model (CESM). Finally, we employ a host of probabilistic skill metrics to determine the extent to which a LIM can forecast SST anomalies at high latitudes. We find that the choice of prior distribution has an appreciable impact on estimation outcomes, and priors that emphasize physically relevant properties enhance the model’s ability to capture variability of SST anomalies.

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“…Since Doan, Litterman, and Sims (1984) first used the Bayesian vector autoregressive (BVAR) approach to forecast macroeconomic variables, numerous studies have been conducted for national macroeconomic time series studies (e.g., Todd, 1984;Litterman, 1986;LeSage and Magura, 1991) or regional time-series studies (e.g., Amirizadeh and Todd, 1984;Magura, 1990;Partridge and Rickman, 1998;Puri and Soydemir, 2000;Rickman, 2001;Rickman, 2002). For a national-level analysis, Litterman (1986), for example, used a BVAR model to show that prior means and variances can improve the forecasting accuracies for macroeconomic variables.…”

Section: Introductionmentioning

confidence: 99%

“…For a national-level analysis, Litterman (1986), for example, used a BVAR model to show that prior means and variances can improve the forecasting accuracies for macroeconomic variables. For an example of a regional analysis, Partridge and Rickman (1998) used the approach to forecast industry employment for the state of Georgia. In their study, the authors incorporated regional employment-based input-output (I-O) coefficients to specify prior means in one model and to weight the variances of a Minnesota-type prior in another model.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Industry Employment for a Resource - based Economy Using Bayesian Vector Autoregressive Models

Seung¹,

Ahn²

2010

Review of Regional Studies

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Bayesian vector autoregressive (BVAR) models are developed to forecast industry employment for a resource-based economy. Two different types of input-output (I-O) information are used as priors: (i) a reduced-form I-O relationship and (ii) an economic-base version of the I-O information. Out-ofsample forecasts from these two I-O-based BVAR models are compared with forecasts from an autoregressive model, an unconstrained VAR model, and a BVAR model with a Minnesota prior. Results indicate most importantly that overall the model version with economic base information performs the best in the long run.

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Generalizing the Bayesian Vector Autoregression Approach for Regional Interindustry Employment Forecasting

Cited by 18 publications

References 27 publications

Understanding the Economic Costs and Benefits of Catastrophes and Their Aftermath: A Review and Suggestions for the U.S. Federal Government

Understanding the Economic Costs and Benefits of Catastrophes and Their Aftermath: A Review and Suggestions for the U.S. Federal Government

A Bayesian Approach to Regional Decadal Predictability: Sparse Parameter Estimation in High-Dimensional Linear Inverse Models of High-Latitude Sea Surface Temperature Variability

Forecasting Industry Employment for a Resource - based Economy Using Bayesian Vector Autoregressive Models

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