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Vector Autoregressive (vector + autoregressive)

Distribution by Scientific Domains
Business, Economics, Finance and Accounting83%

Terms modified by Vector Autoregressive

  • vector autoregressive model
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  • vector autoregressive models
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    Selected Abstracts

    Performance evaluation of the New Connecticut Leading Employment Index using lead profiles and BVAR models

    JOURNAL OF FORECASTING, Issue 6 2006
    Anirvan Banerji
    Abstract The leading and coincident employment indexes for the state of Connecticut developed following the recession of the early 1990s fell short of expectations. This paper performs two tasks. First, it describes the process of revising the Connecticut Coincident and Leading Employment Indexes. Second, it analyzes the statistical properties and performance of the new indexes by comparing the lead profiles of the new and old indexes as well as their out-of-sample forecasting performance, using the Bayesian Vector Autoregressive (BVAR) method. The new coincident index shows improved performance in dating employment cycle chronologies. The lead profile test demonstrates that superiority in a rigorous, non-parametric statistic fashion. The mixed evidence on the BVAR forecasting experiments illustrates that leading indexes properly predict cycle turning points and do not necessarily provide accurate forecasts except at turning points, a view that our results support.,,Copyright © 2006 John Wiley & Sons, Ltd. [source]

    Expectations Formation and Business Cycle Fluctuations: An Empirical Analysis of Actual and Expected Output in UK Manufacturing, 1975,1996

    OXFORD BULLETIN OF ECONOMICS & STATISTICS, Issue 4 2000
    Kevin Lee
    Direct measures of expectations, derived from survey data, are used in a Vector Autoregressive (VAR) model of actual and expected output in eight industries in the UK manufacturing sector. No evidence is found with which to reject rationality in the derived expectations series when measurement error is appropriately taken into account. The VAR analysis illustrates the importance of intersectoral interactions and business confidence in explaining the time profile of industrial outputs, examines the mechanisms by which shocks are propagated across sectors and over time and investigates the relative importance of sectoral and aggregate shocks of different types. [source]

    Estimation and forecasting in first-order vector autoregressions with near to unit roots and conditional heteroscedasticity

    JOURNAL OF FORECASTING, Issue 7 2009
    Theologos Pantelidis
    Abstract This paper investigates the effects of imposing invalid cointegration restrictions or ignoring valid ones on the estimation, testing and forecasting properties of the bivariate, first-order, vector autoregressive (VAR(1)) model. We first consider nearly cointegrated VARs, that is, stable systems whose largest root, lmax, lies in the neighborhood of unity, while the other root, lmin, is safely smaller than unity. In this context, we define the ,forecast cost of type I' to be the deterioration in the forecasting accuracy of the VAR model due to the imposition of invalid cointegration restrictions. However, there are cases where misspecification arises for the opposite reasons, namely from ignoring cointegration when the true process is, in fact, cointegrated. Such cases can arise when lmax equals unity and lmin is less than but near to unity. The effects of this type of misspecification on forecasting will be referred to as ,forecast cost of type II'. By means of Monte Carlo simulations, we measure both types of forecast cost in actual situations, where the researcher is led (or misled) by the usual unit root tests in choosing the unit root structure of the system. We consider VAR(1) processes driven by i.i.d. Gaussian or GARCH innovations. To distinguish between the effects of nonlinear dependence and those of leptokurtosis, we also consider processes driven by i.i.d. t(2) innovations. The simulation results reveal that the forecast cost of imposing invalid cointegration restrictions is substantial, especially for small samples. On the other hand, the forecast cost of ignoring valid cointegration restrictions is small but not negligible. In all the cases considered, both types of forecast cost increase with the intensity of GARCH effects. Copyright © 2009 John Wiley & Sons, Ltd. [source]

    Forecasting interest rate swap spreads using domestic and international risk factors: evidence from linear and non-linear models

    JOURNAL OF FORECASTING, Issue 8 2007
    Ilias Lekkos
    Abstract This paper explores the ability of factor models to predict the dynamics of US and UK interest rate swap spreads within a linear and a non-linear framework. We reject linearity for the US and UK swap spreads in favour of a regime-switching smooth transition vector autoregressive (STVAR) model, where the switching between regimes is controlled by the slope of the US term structure of interest rates. We compare the ability of the STVAR model to predict swap spreads with that of a non-linear nearest-neighbours model as well as that of linear AR and VAR models. We find some evidence that the non-linear models predict better than the linear ones. At short horizons, the nearest-neighbours (NN) model predicts better than the STVAR model US swap spreads in periods of increasing risk conditions and UK swap spreads in periods of decreasing risk conditions. At long horizons, the STVAR model increases its forecasting ability over the linear models, whereas the NN model does not outperform the rest of the models.,,Copyright © 2007 John Wiley & Sons, Ltd. [source]

    Beating the random walk in Central and Eastern Europe

    JOURNAL OF FORECASTING, Issue 3 2005
    Jesús Crespo Cuaresma
    Abstract We compare the accuracy of vector autoregressive (VAR), restricted vector autoregressive (RVAR), Bayesian vector autoregressive (BVAR), vector error correction (VEC) and Bayesian error correction (BVEC) models in forecasting the exchange rates of five Central and Eastern European currencies (Czech Koruna, Hungarian Forint, Slovak Koruna, Slovenian Tolar and Polish Zloty) against the US Dollar and the Euro. Although these models tend to outperform the random walk model for long-term predictions (6 months ahead and beyond), even the best models in terms of average prediction error fail to reject the test of equality of forecasting accuracy against the random walk model in short-term predictions. Copyright © 2005 John Wiley & Sons, Ltd. [source]

    A Generalized Portmanteau Test For Independence Of Two Infinite-Order Vector Autoregressive Series

    JOURNAL OF TIME SERIES ANALYSIS, Issue 4 2006
    Chafik Bouhaddioui
    Primary 62M10; secondary 62M15 Abstract., In many situations, we want to verify the existence of a relationship between multivariate time series. Here, we propose a semiparametric approach for testing the independence between two infinite-order vector autoregressive (VAR(,)) series, which is an extension of Hong's [Biometrika (1996c) vol. 83, 615,625] univariate results. We first filter each series by a finite-order autoregression and the test statistic is a standardized version of a weighted sum of quadratic forms in the residual cross-correlation matrices at all possible lags. The weights depend on a kernel function and on a truncation parameter. Using a result of Lewis and Reinsel [Journal of Multivariate Analysis (1985) Vol. 16, pp. 393,411], the asymptotic distribution of the test statistic is derived under the null hypothesis and its consistency is also established for a fixed alternative of serial cross-correlation of unknown form. Apart from standardization factors, the multivariate portmanteau statistic proposed by Bouhaddioui and Roy [Statistics and Probability Letters (2006) vol. 76, pp. 58,68] that takes into account a fixed number of lags can be viewed as a special case by using the truncated uniform kernel. However, many kernels lead to a greater power, as shown in an asymptotic power analysis and by a small simulation study in finite samples. A numerical example with real data is also presented. [source]

    A Vector Autoregressive Analysis Of An Oil-Dependent Emerging Economy , Nigeria

    OPEC ENERGY REVIEW, Issue 4 2000
    O. Felix Ayadi
    This paper models the interrelationship among a variety of macroeconomic variables representing the financial, as well as the energy, sectors of the Nigerian economy from 1975 through 1994. The attempt is to investigate the impact of the energy sector on the functioning of the Nigerian economy, including the financial markets. The investigation is explored within a vector autoregressive (VAR) system. The results reveal that the energy sector exerts a significant influence on the Nigerian economy by acting as a prime mover. More importantly, Nigeria seems to find itself in a vicious circle, because of its inability to exercise control over the price of its main export and its imports. Thus, the strength and autonomy exhibited by Nigerias macroeconomic managers during the oil boom era appears to have been barren. [source]

    Testing the New Keynesian Phillips Curve Through Vector Autoregressive Models: Results from the Euro Area,

    OXFORD BULLETIN OF ECONOMICS & STATISTICS, Issue 1 2008
    Luca Fanelli
    Abstract This paper addresses the issue of testing the ,hybrid' New Keynesian Phillips curve (NKPC) through vector autoregressive (VAR) systems and likelihood methods, giving special emphasis to the case where the variables are non-stationary. The idea is to use a VAR for both the inflation rate and the explanatory variable(s) to approximate the dynamics of the system and derive testable restrictions. Attention is focused on the ,inexact' formulation of the NKPC. Empirical results over the period 1971,98 show that the NKPC is far from providing a ,good first approximation' of inflation dynamics in the Euro area. [source]

    On the Effects of Inflation Shocks in a Small Open Economy

    THE AUSTRALIAN ECONOMIC REVIEW, Issue 3 2007
    Sushanta K. Mallick
    The effects of monetary policies remain always an important topic in macroeconomics. In the literature (closed and open economy), there is no theoretical as well as empirical consensus regarding the effects of monetary policies. In this paper we examine the real effects of inflation in an open economy. Australia is a classic example of a small open economy and is known to exercise inflation targeting. Using quarterly data from Australia and employing vector autoregressive (VAR) analysis, we provide evidence that inflation, both in the short and long run, negatively affects durable and non-durable consumption and investment, and has a positive effect on the current account. Further, we show that consumption of durable goods is more sensitive than the consumption of non-durables during the initial periods following inflationary shocks. [source]

    SHORT-RUN AND LONG-RUN DETERMINANTS OF THE REAL EXCHANGE RATE IN MEXICO

    THE DEVELOPING ECONOMIES, Issue 1 2008
    Antonia LÓPEZ VILLAVICENCIO
    C32; F31; F41; F49 This paper explores the real exchange rate behavior in Mexico from 1960 until 2005. Since the empirical analysis reveals that the real exchange rate is not mean reverting, we propose that economic fundamental variables affect its evolution in the long run. Therefore, based on equilibrium exchange rate paradigms, we propose a simple model of real exchange rate determination, which includes the relative GDP per capita, the real interest rates, and the net foreign assets over a long period of time. Our analysis also considers the dynamic adjustment in response to shocks through impulse response functions derived from the multivariate vector autoregressive (VAR) model. [source]

    Determinants of Japanese Yen interest rate swap spreads: Evidence from a smooth transition vector autoregressive model

    THE JOURNAL OF FUTURES MARKETS, Issue 1 2008
    Ying Huang
    This study investigates the determinants of variations in the yield spreads between Japanese yen interest rate swaps and Japan government bonds for a period from 1997 to 2005. A smooth transition vector autoregressive (STVAR) model and generalized impulse response functions are used to analyze the impact of various economic shocks on swap spreads. The volatility based on a GARCH (generalized autoregressive conditional heteroskedasticity) model of the government bond rate is identified as the transition variable that controls the smooth transition from a high volatility regime to a low volatility regime. The break point of the regime shift occurs around the end of the Japanese banking crisis. The impact of economic shocks on swap spreads varies across the maturity of swap spreads as well as regimes. Overall, swap spreads are more responsive to the economic shocks in the high volatility regime. Moreover, a volatility shock has profound effects on shorter maturity spreads, whereas the term structure shock plays an important role in impacting longer maturity spreads. Results of this study also show noticeable differences between the nonlinear and linear impulse response functions. © 2008 Wiley Periodicals, Inc. Jrl Fut Mark 28:82,107, 2008 [source]