A BVAR model for the connecticut economy

Dua, Pami; Ray, Subhash C.

doi:10.1002/for.3980140303

Cited by 70 publications

(87 citation statements)

References 19 publications

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“…Even though these models lack information about the possible fundamentals that might be affecting the housing market, they can be very handy when it comes to providing preliminary indication about where the variables of interest might be heading and Das et al (2008)). The fact that VARs, especially BVARs, are quite well-suited in predicting turning points of macroeconomic variables have recently been substantiated by Dua and Ray (1995), Dua and Miller (1996), Del Negro (1999), Gupta and Sichei (2006), Gupta (2006Gupta ( , 2007a, Zita and Gupta (2007) and Banerji et al (2008), amongst others. Moreover, as indicated by Strauss (2007, 2008), Gupta and Das (2008) and Das et al (2008), it is also important to account for the effects of the house price of neighbouring states in predicting the house price of a specific state and herein lies the rationale for using spatial BVAR (SBVAR) models, over and above the standard VAR and BVAR models based on the Minnesota priors 2 , for forecasting house price.…”

Section: Introductionmentioning

confidence: 97%

Predicting Downturns in the US Housing Market: A Bayesian Approach

Gupta

Das²

2008

J Real Estate Finan Econ

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This paper estimates Bayesian Vector Autoregressive (BVAR) models, both spatial and non-spatial (univariate and multivariate), for the twenty largest states of the US economy, using quarterly data over the period 1976:Q1 to 1994:Q4; and then forecasts one-to-four quarters ahead real house price growth over the out-of-sample horizon of 1995:Q1 to 2006:Q4. The forecasts are then evaluated by comparing them with the ones generated from an unrestricted classical Vector Autoregressive (VAR) model and the corresponding univariate variant the same. Finally, the models that produce the minimum average Root Mean Square Errors (RMSEs), are used to predict the downturns in the real house price growth over the recent period of 2007:Q1 to 2008:Q1. The results show that the BVARs, in whatever form they might be, are the best performing models in 19 of the 20 states. Moreover, these models do a fair job in predicting the downturn in 18 of the 19 states, however, they always under-predict the size of the decline in the real house price growth rate -an indication of the need to incorporate the role of fundamentals in the models. JEL Classification: E17, E27, E37, E47.

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Section: Introductionmentioning

confidence: 97%

Predicting Downturns in the US Housing Market: A Bayesian Approach

Gupta

Das²

2008

J Real Estate Finan Econ

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“…The TVP-VAR can be expressed as y t = 0;t + 1;t y t 1 + + p;t y t p + u t (25) in which 0;t is a k 1 vector of time-varying intercepts, i;t (i = 1; : : : ; p) are k k matrices of time-varying coe¢ cients and u t are homoscedastic or heteroscedastic reduced-form residuals with a covariance matrix t . This could be transformed into a multivariate state-space form.…”

Section: State Space Time-varying Parameter Var Modelmentioning

confidence: 99%

On the predictability of time-varying VAR and DSGE models

Bekiros

Paccagnini

2012

Empir Econ

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Over the last few years, there has been a growing interest in DSGE modelling for predicting macroeconomic ‡uctuations and conducting quantitative policy analysis. Hybrid DSGE models have become popular for dealing with some of the DSGE misspeci…cations as they are able to solve the tradeo¤ between theoretical coherence and empirical …t. However, these models are still linear and they do not consider time-variation for parameters. The time-varying properties in VAR or DSGE models capture the inherent nonlinearities and the adaptive underlying structure of the economy in a robust manner. In this paper, we present a state space time-varying parameter VAR model. Moreover, we focus on the DSGE-VAR that combines a micro-founded DSGE model with the ‡exibility of a VAR framework. All the aforementioned models as well simple DSGEs and Bayesian VARs are used in a comparative investigation of their out-of-sample predictive performance regarding the US economy. The results indicate that while in general the classical VAR and BVARs provide with good forecasting results, in many cases the TVP-VAR and the DSGE-VAR outperform the other models.

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“…The β j coefficients, however, that associate with lessimportant variables receive a coefficient in the weighting matrix (F) that imposes the prior means 6 For an illustration, see Dua and Ray (1995).…”

Section: Var Vec Bvar Bvec Sbvar and Sbvec Specification And Estmentioning

confidence: 99%

“…In addition, Dua and Ray (1995) propose a prior with less restrictions on the other variables in the VAR model, specifically with w = 0.30 and d = 0.50.…”

Section: Var Vec Bvar Bvec Sbvar and Sbvec Specification And Estmentioning

confidence: 99%

“…In addition, as in Dua and Ray (1995), Gupta and Sichei (2006), and Gupta (2006), we also estimate a BVAR model with w = 0.3 and d = 0.5. We also introduce d = 2 to increase the tightness on lag m. Finally, we specify σ c =0.1, η=8, θ=0.5 for the random-walk models with the two different specifications for causality and spatial priors.…”

Section: One-to Four-quarter-ahead Forecast Accuracymentioning

confidence: 99%

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“Ripple effects” and forecasting home prices in Los Angeles, Las Vegas, and Phoenix

Gupta

Miller

2010

Ann Reg Sci

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We examine the time-series relationship between housing prices in Los Angeles, Las Vegas, and Phoenix. First, temporal Granger causality tests reveal that Los Angeles housing prices cause housing prices in Las Vegas (directly) and Phoenix (indirectly). In addition, Las Vegas housing prices cause housing prices in Phoenix. Los Angeles housing prices prove exogenous in a temporal sense and Phoenix housing prices do not cause prices in the other two markets. Second, we calculate out-of-sample forecasts in each market, using various vector autoregessive (VAR) and vector error-correction (VEC) models, as well as Bayesian, spatial, and causality versions of these models with various priors. Different specifications provide superior forecasts in the different cities. Finally, we consider the ability of theses time-series models to provide accurate out-of-sample predictions of turning points in housing prices that occurred in 2006:Q4. Recursive forecasts, where the sample is updated each quarter, provide reasonably good forecasts of turning points. Journal of Economic Literature Classification: C32, R31

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A BVAR model for the connecticut economy

Cited by 70 publications

References 19 publications

Predicting Downturns in the US Housing Market: A Bayesian Approach

Predicting Downturns in the US Housing Market: A Bayesian Approach

On the predictability of time-varying VAR and DSGE models

“Ripple effects” and forecasting home prices in Los Angeles, Las Vegas, and Phoenix

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