VAR, SVAR models, data, and specification tests
Impact of Agriculture and Industrialization on GDP in Nigeria: Evidence from VAR and SVAR Models
... transform data on Agriculture, industry and GDP from 1960 to 2011 extracted from the CBN ...using VAR and SVAR models. The results from the VAR model revealed that agriculture ...
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Identification of monetary policy in SVAR models: A data-oriented perspective
... small-scale VAR of the US economy and finds that the application of such a data-based tool give rise to identifying restrictions consistent with the “alternative” ...
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Specification tests for time-varying parameter models with stochastic volatility
... time-varying models, an emerging literature has highlighted concerns about their potential ...the data prefer time-variation in that ...that data favor a VAR where some coefficients are ...
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Model-Based Stress Tests: Linking Stress Tests to VaR for Market Risk
... previous VaR studies by first examining the ability of a wide variety of risk models to forecast extreme percentiles of returns distributions over multi-day horizons and then incorporating stress ...
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VAR, SVAR and SVEC Models: Implementation Within R Package vars
... In the package vars functions for diagnostic testing are arch.test(), normality.test(), serial.test() and stability(). The former three functions return a list object with class attribute varcheck for which plot and ...
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Convenient Specification Tests for Logit and Probit Models
... By and large, the asymptotic results do not appear to be seriously misleading when the alternative which generated the data is β 2 6= 0 or β 3 6= 0, but when β 4 6= 0, they can be misleading. In almost all cases ...
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Alternative estimation methods and specification tests for moment condition models
... associated tests may have actual sizes substantially different from the nominal ...using data generated from artificial nonlinear consumption-based asset pricing ...financial data. They concluded ...
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Nonparametric specification tests for stochastic volatility models based on volatility density
... The Milestein scheme is used to simulate from all the stochastic differential equations. The kernel K 1 is used in all the volatility based test statistics. For the return density based test, the bandwidth is selected ...
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Nonparametric specification tests for stochastic volatility models based on volatility density
... The Milestein scheme is used to simulate from all the stochastic differential equations. The kernel K 1 is used in all the volatility based test statistics. For the return density based test, the bandwidth is selected ...
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Consistent nonparametric specification tests for stochastic volatility models based on the return distribution
... Based on 1000 bootstrap samples, the estimated p-values for T 1 , T 2 and T 3 are 0.009, 0.014 and 0.000 respectively. The p-values of all the tests provide strong evidence of rejection of the Heston model. The ...
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Consistent nonparametric specification tests for stochastic volatility models based on the return distribution
... our tests are based on the density function and the distribution function of the return data, we first give a plot of the empirical density function estimated and all the model implied return densities in ...
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Tests of the co integration rank in VAR models in the presence of a possible break in trend at an unknown point
... the data. It is known that un-modelled trend breaks can result in tests which are incorrectly sized under the null hypoth- esis and inconsistent under the alternative ...the tests that a trend break ...
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Tests of the co-integration rank in VAR models in the presence of a possible break in trend at an unknown point
... in VAR processes of unknown lag order when a break in the deterministic trend component may be present at an unknown point in the ...break models, using a consistent estimate of the break fraction in the ...
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Tests of the Co-integration Rank in VAR Models in the Presence of a Possible Break in Trend at an Unknown Point
... the data. It is known that un-modelled trend breaks can result in tests which are incorrectly sized under the null hypoth- esis and inconsistent under the alternative ...the tests that a trend break ...
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Tests for conditional heteroscedasticity with functional data and goodness of fit tests for FGARCH models
... portmanteau tests for the purpose of identifying conditional heteroscedasticity in functional time ...posed tests to the model residuals from a fitted FGARCH model that can be used to evaluate the model’s ...
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Tests for conditional heteroscedasticity with functional data and goodness-of-fit tests for FGARCH models
... portmanteau tests for the purpose of identifying conditional heteroscedasticity in functional time ...posed tests to the model residuals from a fitted FGARCH model that can be used to evaluate the model’s ...
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The effects of fiscal shocks in SVAR models: a graphical modelling approach
... 6.3 Subsample stability We rst employ forecast Chow tests to check the overall stability of the parameters of the estimated models. Once the sample has been split into two parts, this test allows us to ...
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Specification test for panel data models with interactive fixed effects
... model. Models derived from first-principles such as utility or production functions only have linear dynamics under some narrow functional form ...Linear models are usually adopted for ...result, ...
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GMM Gradient Tests for Spatial Dynamic Panel Data Models
... spatial time lag terms and the contemporaneous spatial lag terms. In the literature, the model specifications and estimation strategies, including the ML, GMM 26 and Bayesian methods, receive considerably more attention ...
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GMM Gradient Tests for Spatial Dynamic Panel Data Models
... panel data specification, where the whole period T is divided into equal time-spans of τ ...panel data framework, because unobservable factors may loom large and the disturbance terms over short ...
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