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Vector Autoregression (vector + autoregression)
Selected AbstractsVector Autoregression (Var) , An Approach to Dynamic analysis of Geographic ProcessesGEOGRAFISKA ANNALER SERIES B: HUMAN GEOGRAPHY, Issue 2 2001Max Lu Vector autoregression (VAR) is a widely used econometric technique for multivariate time series modelling. This paper shows that with several very attractive features, VAR may also provide a valuable tool for analysing the dynamics among geographic processes and for spatial autoregressive modelling. After a brief discussion of the VAR approach, a VAR model for the dynamics of the US population between 1910 and 1990 is estimated and interpreted to illustrate the techniques. The VAR makes it possible to view the interactions among the four variables used in the model (total population, birth rate, immigration and per capita GNP) more adequately. The paper then discusses recent developments in the VAR methodology such as Bayesian vector autoregression (BVAR), spatial prior for regional modelling and cointegration, as well as the limitations and problems that arise from the application of VARs. [source] Marketing Category Forecasting: An Alternative of BVAR-Artificial Neural Networks¶DECISION SCIENCES, Issue 4 2000James J. Jiang ABSTRACT Analyzing scanner data in brand management activities presents unique difficulties due to the vast quantity of the data. Time series methods that are able to handle the volume effectively often are inappropriate due to the violation of many statistical assumptions in the data characteristics. We examine scanner data sets for three brand categories and examine properties associated with many time series forecasting methods. Many violations are found with respect to linearity, normality, autocorrelation, and heteroscedasticity. With this in mind we compare the forecasting ability of neural networks that require no assumptions to two of the more robust time series techniques. Neural networks provide similar forecasts to Bayesian vector autoregression (BVAR), and both outperform generalized autoregressive conditional herteroscedasticty (GARCH) models. [source] Modeling and Forecasting Realized VolatilityECONOMETRICA, Issue 2 2003Torben G. Andersen We provide a framework for integration of high,frequency intraday data into the measurement, modeling, and forecasting of daily and lower frequency return volatilities and return distributions. Building on the theory of continuous,time arbitrage,free price processes and the theory of quadratic variation, we develop formal links between realized volatility and the conditional covariance matrix. Next, using continuously recorded observations for the Deutschemark/Dollar and Yen/Dollar spot exchange rates, we find that forecasts from a simple long,memory Gaussian vector autoregression for the logarithmic daily realized volatilities perform admirably. Moreover, the vector autoregressive volatility forecast, coupled with a parametric lognormal,normal mixture distribution produces well,calibrated density forecasts of future returns, and correspondingly accurate quantile predictions. Our results hold promise for practical modeling and forecasting of the large covariance matrices relevant in asset pricing, asset allocation, and financial risk management applications. [source] Twin deficits: squaring theory, evidence and common senseECONOMIC POLICY, Issue 48 2006Giancarlo Corsetti SUMMARY Budget deficits and current accounts OPENNESS AND FISCAL PERSISTENCE Simple accounting suggests that shocks to the government budget move the current account in the same direction, and this ,twin deficits' intuition leads many observers to call for fiscal consolidation in the US as a necessary measure to reduce the large external imbalance of this country. The response of other macroeconomic variables to budget developments, however, has important implications for ,twin deficits' and for this policy prescription. Focusing on the international transmission of fiscal policy shocks via terms of trade changes, we show that the likelihood and magnitude of twin deficits increases with the degree of openness of an economy, and decreases with the persistence of fiscal shocks. We take this insight to the data and investigate the transmission of fiscal shocks in a vector autoregression (VAR) model estimated for Australia, Canada, the UK and the US. We find that in less open countries the external impact of shocks to either government spending or budget deficits is limited, while private investment responds in line with our theoretical prediction. These results suggest that a fiscal retrenchment in the US may have a limited impact on its current external deficit. , Giancarlo Corsetti and Gernot J. Müller [source] Long-Term Effects of Fiscal Policy on the Size and Distribution of the Pie in the UK,FISCAL STUDIES, Issue 3 2008Xavier Ramos C5; E6; H3 Abstract. This paper provides a joint analysis of the output and distributional long-term effects of various fiscal policies in the UK, using a vector autoregression (VAR) approach. Our findings suggest that the long-term impact on GDP of increasing public spending and taxes is negative, and especially strong in the case of current expenditure. We also find significant distributional effects associated with fiscal policies, indicating that an increase in public spending reduces inequality while a rise in indirect taxes increases income inequality. [source] Vector Autoregression (Var) , An Approach to Dynamic analysis of Geographic ProcessesGEOGRAFISKA ANNALER SERIES B: HUMAN GEOGRAPHY, Issue 2 2001Max Lu Vector autoregression (VAR) is a widely used econometric technique for multivariate time series modelling. This paper shows that with several very attractive features, VAR may also provide a valuable tool for analysing the dynamics among geographic processes and for spatial autoregressive modelling. After a brief discussion of the VAR approach, a VAR model for the dynamics of the US population between 1910 and 1990 is estimated and interpreted to illustrate the techniques. The VAR makes it possible to view the interactions among the four variables used in the model (total population, birth rate, immigration and per capita GNP) more adequately. The paper then discusses recent developments in the VAR methodology such as Bayesian vector autoregression (BVAR), spatial prior for regional modelling and cointegration, as well as the limitations and problems that arise from the application of VARs. [source] Firm Size, Industry Mix and the Regional Transmission of Monetary Policy in GermanyGERMAN ECONOMIC REVIEW, Issue 1 2004Ivo J. M. Arnold Monetary transmission; regional effects; industry effects; firm size Abstract. This paper estimates the impact of interest rate shocks on regional output in Germany over the period from 1970 to 2000. We use a vector autoregression (VAR) model to obtain impulse responses, which reveal differences in the output responses to monetary policy shocks across ten German provinces. Next, we investigate whether these differences can be related to structural features of the regional economies, such as industry mix, firm size, bank size and openness. An additional analysis of the volatility of real GDP growth for the period 1992,2000 includes the Eastern provinces. We also present evidence on the interrelationship between firm size and industry, and compare our measure of firm size with those used in previous studies. We conclude that the differential regional effects of monetary policy are related to industrial composition, but not to firm size or bank size. [source] TECHNOLOGY SHOCKS AND ROBUST SIGN RESTRICTIONS IN A EURO AREA SVAR,INTERNATIONAL ECONOMIC REVIEW, Issue 3 2009Gert Peersman We use a model-based identification strategy to estimate the impact of technology shocks on hours worked and employment in the euro area. The sign restrictions applied in the vector autoregression (VAR) analysis are consistent with a large class of dynamic stochastic general equilibrium (DSGE) models and are robust to parameter uncertainty. The results are in line with the conventional Real Business Cycle (RBC) interpretation that hours worked rise as a result of a positive technology shock. By comparing the sign restrictions method to the long-run restriction approach of Galí (Quaterly Journal of Economics,(1992) 709,38), we show that the results do not depend on the stochastic specification of the hours worked series or the data sample but only on the identification scheme. [source] The comovements of stock markets in Hungary, Poland and the Czech RepublicINTERNATIONAL JOURNAL OF FINANCE & ECONOMICS, Issue 1 2001Martin Scheicher C53; G15 Abstract In this paper, we study the regional and global integration of stock markets in Hungary, Poland and the Czech Republic. We estimate a vector autoregression with a multivariate GARCH component and perform a variety of diagnostic tests. Our main empirical result is the existence of limited interaction: in returns we identify both regional and global shocks, but innovations to volatility have a primarily regional character. We document low correlations to international markets and discuss the economic significance of the inter-market dynamics. Copyright © 2001 John Wiley & Sons, Ltd. [source] A New-Keynesian DSGE model for forecasting the South African economyJOURNAL OF FORECASTING, Issue 5 2009Dave' Liu, Guangling Abstract This paper develops a New-Keynesian Dynamic Stochastic General Equilibrium (NKDSGE) model for forecasting the growth rate of output, inflation, and the nominal short-term interest rate (91 days Treasury Bill rate) for the South African economy. The model is estimated via maximum likelihood technique for quarterly data over the period of 1970:1,2000:4. Based on a recursive estimation using the Kalman filter algorithm, out-of-sample forecasts from the NKDSGE model are compared with forecasts generated from the classical and Bayesian variants of vector autoregression (VAR) models for the period 2001:1,2006:4. The results indicate that in terms of out-of-sample forecasting, the NKDSGE model outperforms both the classical and Bayesian VARs for inflation, but not for output growth and nominal short-term interest rate. However, differences in RMSEs are not significant across the models. Copyright © 2008 John Wiley & Sons, Ltd. [source] BBVA-ARIES: a forecasting and simulation model for EMUJOURNAL OF FORECASTING, Issue 5 2003Fernando C. Ballabriga Abstract This paper describes the BBVA-ARIES, a Bayesian vector autoregression (BVAR) for the European Economic and Monetary Union (EMU). In addition to providing EMU-wide growth and inflation forecasts, the model provides an assessment of the interactions between key EMU macroeconomic variables and external ones, such as world GDP or commodity prices. A comparison of the forecasts generated by the model and those of private analysts and public institutions reveals a very positive balance in favour of the model. For their part, the simulations allow us to assess the potential macroeconomic effects of macroeconomic developments in the EMU.,Copyright © 2003 John Wiley & Sons, Ltd. [source] A Coincident Index, Common Factors, and Monthly Real GDP,OXFORD BULLETIN OF ECONOMICS & STATISTICS, Issue 1 2010Roberto S. Mariano Abstract The Stock,Watson coincident index and its subsequent extensions assume a static linear one-factor model for the component indicators. This restrictive assumption is unnecessary if one defines a coincident index as an estimate of monthly real gross domestic products (GDP). This paper estimates Gaussian vector autoregression (VAR) and factor models for latent monthly real GDP and other coincident indicators using the observable mixed-frequency series. For maximum likelihood estimation of a VAR model, the expectation-maximization (EM) algorithm helps in finding a good starting value for a quasi-Newton method. The smoothed estimate of latent monthly real GDP is a natural extension of the Stock,Watson coincident index. [source] Leaning into the Wind: A Structural VAR Investigation of UK Monetary Policy,OXFORD BULLETIN OF ECONOMICS & STATISTICS, Issue 5 2005Andrew Mountford Abstract This paper adapts Uhlig's [Journal of Monetary Economics (2005) forthcoming] sign restriction identification methodology to investigate the effects of UK monetary policy using a structural vector autoregression (VAR). It shows that shocks which can reasonably be described as monetary policy shocks have played only a small role in the total variation of UK monetary and macroeconomic variables. Most of the variation in UK monetary variables has been due to their systematic reaction to other macroeconomic shocks, namely non-monetary aggregate demand, aggregate supply, and oil price shocks. We also find, without imposing any long run identifying restrictions, that aggregate supply shocks have permanent effects on output. [source] Panel vector autoregression under cross-sectional dependenceTHE ECONOMETRICS JOURNAL, Issue 2 2008Xiao Huang Summary, This paper studies estimation in panel vector autoregression (VAR) under cross-sectional dependence. The time series are allowed to be an unknown mixture of stationary and unit root processes with possible cointegrating relations. The cross-sectional dependence is modeled with a factor structure. We extend the factor analysis in Bai and Ng (2002, Econometrica 70, 91,221) to vector processes. The fully modified (FM) estimator in Phillips (1995) is used for estimation in panel VAR and we also propose a factor augmented FM estimator. Our simulation results show this factor augmented FM estimator performs well when sample size is large. [source] The response of volume and returns to the information shocks in China's commodity futures marketsTHE JOURNAL OF FUTURES MARKETS, Issue 9 2005Gongmeng Chen This study investigates the response of returns and volume to different information shocks in China's commodity futures markets using bivariate moving average representation (BMAR) and bivariate vector autoregression (BVAR) methodologies. Consistent with the conclusions from stock market studies that have used these methodologies, it is found that the informational/permanent components are the dominant components for returns movements, and the noninformational/transitory components are the dominant components for trading volume. It is also found that the market response of copper futures improved during the sample period, and the market responses of actively traded futures (copper and soybeans) are better than those of the less actively traded futures (aluminum and wheat). © 2005 Wiley Periodicals, Inc. Jrl Fut Mark 25:893,916, 2005 [source] SOURCES OF REAL EXCHANGE RATE FLUCTUATIONS: EMPIRICAL EVIDENCE FROM NINE AFRICAN COUNTRIESTHE MANCHESTER SCHOOL, Issue 2009A. H. AHMAD We investigate the sources of real exchange rate fluctuations in a sample of nine African countries from 1980:01 to 2005:04, using a trivariate structural vector autoregression. The analysis is motivated by a stochastic sticky-price model from which three shocks are identified; demand, supply and monetary shocks. The results indicate that demand shocks are the predominant source of real exchange rate movements in these countries, although nominal shocks have also played a small but significant role in South Africa and Botswana, and supply shocks seem to be of some relevance for Algeria, Egypt and Tanzania. [source] MACROECONOMETRIC MODELLING WITH A GLOBAL PERSPECTIVE,THE MANCHESTER SCHOOL, Issue 2006M. HASHEM PESARAN This paper provides a synthesis and further development of a global modelling approach introduced in Pesaran et al. (Journal of Business and Economic Statistics, Vol. 22 (2004), pp. 129,162), where country-specific models in the form of VARX* structures are estimated relating a vector of domestic variables, xit, to their foreign counterparts, , and then consistently combined to form a global vector autoregression. It is shown that the VARX* models can be derived as the solution to a dynamic stochastic general equilibrium model where overidentifying long-run theoretical relations can be tested and imposed if acceptable. This gives the system a transparent long-run theoretical structure. Similarly, short-run overidentifying theoretical restrictions can be tested and imposed if accepted. Alternatively, if one has less confidence in the short-run theory the dynamics can be left unrestricted. The assumption of the weak exogeneity of the foreign variables for the long-run parameters can be tested, where variables can be interpreted as proxies for regional and global factors. Rather than using deviations from ad hoc statistical trends, the equilibrium values of the variables reflecting the long-run theory embodied in the model can be calculated. The paper also provides some new results on the relative importance of external shocks for the UK and the Euro area economies. [source] Real and Nominal Shocks to Exchange Rates: Does the Regime Matter?THE MANCHESTER SCHOOL, Issue 5 2002Liam A. Gallagher In this paper we investigate the source of Irish real and nominal exchange rate movements during the Exchange Rate Mechanism period. A restricted vector autoregression is employed to decompose Irish pound exchange rate movements into changes due to real and nominal factors, for three bilateral exchange rates,sterling,Irish pound, mark,Irish pound and dollar,Irish pound. The pattern of nominal exchange rate overshooting in response to nominal shocks and the relative importance of nominal shocks as drivers of nominal exchange rates differ between the flexible regime (sterling,Irish pound and dollar,Irish pound) and the target zone arrangement (mark,Irish pound). In contrast real shocks predominantly explain variations in real exchange rates and are independent of the exchange rate regime. [source] From Great Depression to Great Credit Crisis: similarities, differences and lessonsECONOMIC POLICY, Issue 62 2010Miguel Almunia Summary The Great Depression of the 1930s and the Great Credit Crisis of the 2000s had similar causes but elicited strikingly different policy responses. While it remains too early to assess the effectiveness of current policy, it is possible to analyse monetary and fiscal responses in the 1930s as a natural experiment or counterfactual capable of shedding light on the impact of current policies. We employ vector autoregressions, instrumental variables, and qualitative evidence for 27 countries in the period 1925,39. The results suggest that monetary and fiscal stimulus was effective -- that where it did not make a difference it was not tried. They shed light on the debate over fiscal multipliers in episodes of financial crisis. They are consistent with multipliers at the higher end of those estimated in the recent literature, and with the argument that the impact of fiscal stimulus will be greater when banking systems are dysfunctional and monetary policy is constrained by the zero bound. --- Miguel Almunia, Agustín Bénétrix, Barry Eichengreen, Kevin H. O'Rourke and Gisela Rua [source] Estimation and forecasting in first-order vector autoregressions with near to unit roots and conditional heteroscedasticityJOURNAL OF FORECASTING, Issue 7 2009Theologos 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] A markup model for forecasting inflation for the euro areaJOURNAL OF FORECASTING, Issue 7 2006Bill Russell Abstract We develop a small model for forecasting inflation for the euro area using quarterly data over the period June 1973 to March 1999. The model is used to provide inflation forecasts from June 1999 to March 2002. We compare the forecasts from our model with those derived from six competing forecasting models, including autoregressions, vector autoregressions and Phillips-curve based models. A considerable gain in forecasting performance is demonstrated using a relative root mean squared error criterion and the Diebold,Mariano test to make forecast comparisons.,,Copyright © 2006 John Wiley & Sons, Ltd. [source] Temporal aggregation and spurious instantaneous causality in multiple time series modelsJOURNAL OF TIME SERIES ANALYSIS, Issue 6 2002JÖRG BREITUNG Large aggregation interval asymptotics are used to investigate the relation between Granger causality in disaggregated vector autoregressions (VARs) and associated contemporaneous correlation among innovations of the aggregated system. One of our main contributions is that we outline various conditions under which the informational content of error covariance matrices yields insight into the causal structure of the VAR. Monte Carlo results suggest that our asymptotic findings are applicable even when the aggregation interval is small, as long as the time series are not characterized by high levels of persistence. [source] A Direct Test for Cointegration Between a Pair of Time SeriesJOURNAL OF TIME SERIES ANALYSIS, Issue 2 2002STEPHEN J. LEYBOURNE In this paper we introduce a new test of the null hypothesis of no cointegration between a pair of time series. For a very simple generating model, our test compares favourably with the Engle,Granger/Dickey,Fuller test and the Johansen trace test. Indeed, shortcomings of the former motivated the development of our test. The applicability of our test is extended to series generated by low-order vector autoregressions. Again, we find evidence that this general version of our test is more powerful than the Johansen test. The paper concludes with an empirical example in which the new test finds strong evidence of cointegration, but the Johansen test does not. [source] | ||||||||||||||||||||||||||||