Autoregression

Distribution by Scientific Domains
Distribution within Business, Economics, Finance and Accounting

Kinds of Autoregression

  • vector autoregression


  • Selected Abstracts


    ESTIMATING PHYLOGENETIC INERTIA IN TITHONIA (ASTERACEAE): A COMPARATIVE APPROACH

    EVOLUTION, Issue 2 2000
    Eduardo Morales
    Abstract., Phylogenetic inertia is a difficult issue in evolutionary biology because we have yet to reach a consensus about how to measure it. In this study a comparative approach is used to evaluate phylogenetic inertia in 14 demographic and morphological characters in 10 species and one subspecies of the genus Tithonia (Asteraceae). Three different methods, autocorrelational analysis, phylogenetic correlograms, and ancestor-state reconstruction, were used to evaluate phylogenetic inertia in these traits. Results were highly dependent on the method applied. Autoregression and phylogenetic eigenvector regression (PVR) methods found more inertia in morphological traits. In contrast, phylogenetic correlograms and ancestor-state reconstruction suggest that morphological characters exhibit less phylogenetic inertia than demographic ones. The differences between results are discussed and methods are compared in an effort to understand phylogenetic inertia more thoroughly. [source]


    Vector Autoregression (Var) , An Approach to Dynamic analysis of Geographic Processes

    GEOGRAFISKA ANNALER SERIES B: HUMAN GEOGRAPHY, Issue 2 2001
    Max 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]


    Properties of Predictors in Overdifferenced Nearly Nonstationary Autoregression

    JOURNAL OF TIME SERIES ANALYSIS, Issue 1 2001
    Ismael Sanchez
    We analyze the effect of overdifferencing a stationary AR(p+1) process whose largest root is near unity. It is found that, if the process is nearly nonstationary, the estimators of the overdifferenced model ARIMA(p,1,0) are root- T consistent. It is also found that this misspecified ARIMA(p,1,0) has lower predictive mean squared error, to terms of small order, than the properly specified AR(p+1) model due to its parsimony. The advantage of the overdifferenced predictor depends on the remaining roots, the prediction horizon and the mean of the process. [source]


    Bootstrapping Autoregression under Non-stationary Volatility

    THE ECONOMETRICS JOURNAL, Issue 1 2008
    Ke-Li Xu
    Summary This paper studies robust inference in autoregression around a polynomial trend with stable autoregressive roots under non-stationary volatility. The formulation of the volatility process is quite general including many existing deterministic and stochastic non-stationary volatility specifications. The aim of the paper is two-fold. First, it develops a limit theory for least squares estimators and shows how non-stationary volatility affects the consistency, convergence rates and asymptotic distributions of the slope and trend coefficients estimators in different ways. This complements the results recently obtained by Chung and Park (2007, Journal of Econometrics 137, 230,59. Second, it studies the recursive wild bootstrap procedure of Gonçalves and Kilian (2004, Journal of Econometrics 123, 89,120) in the presence of non-stationary volatility, and shows its validity when the estimates are asymptotically mixed Gaussian. Simulations are performed to compare favourably the recursive wild bootstrap with other inference procedures under non-stationary volatility. [source]


    Marketing Category Forecasting: An Alternative of BVAR-Artificial Neural Networks¶

    DECISION SCIENCES, Issue 4 2000
    James 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 Volatility

    ECONOMETRICA, Issue 2 2003
    Torben 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 sense

    ECONOMIC POLICY, Issue 48 2006
    Giancarlo 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]


    ADAPTIVE CONSTRAINTS AND THE PHYLOGENETIC COMPARATIVE METHOD: A COMPUTER SIMULATION TEST

    EVOLUTION, Issue 1 2002
    Emilia P. Martins
    Abstract Recently, the utility of modern phylogenetic comparative methods (PCMs) has been questioned because of the seemingly restrictive assumptions required by these methods. Although most comparative analyses involve traits thought to be undergoing natural or sexual selection, most PCMs require an assumption that the traits be evolving by less directed random processes, such as Brownian motion (BM). In this study, we use computer simulation to generate data under more realistic evolutionary scenarios and consider the statistical abilities of a variety of PCMs to estimate correlation coefficients from these data. We found that correlations estimated without taking phylogeny into account were often quite poor and never substantially better than those produced by the other tested methods. In contrast, most PCMs performed quite well even when their assumptions were violated. Felsenstein's independent contrasts (FIC) method gave the best performance in many cases, even when weak constraints had been acting throughout phenotypic evolution. When strong constraints acted in opposition to variance-generating (i.e., BM) forces, however, FIC correlation coefficients were biased in the direction of those BM forces. In most cases, all other PCMs tested (phylogenetic generalized least squares, phylogenetic mixed model, spatial autoregression, and phylogenetic eigenvector regression) yielded good statistical performance, regardless of the details of the evolutionary model used to generate the data. Actual parameter estimates given by different PCMs for each dataset, however, were occasionally very different from one another, suggesting that the choice among them should depend on the types of traits and evolutionary processes being considered. [source]


    Long-Term Effects of Fiscal Policy on the Size and Distribution of the Pie in the UK,

    FISCAL STUDIES, Issue 3 2008
    Xavier 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 Processes

    GEOGRAFISKA ANNALER SERIES B: HUMAN GEOGRAPHY, Issue 2 2001
    Max 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 Germany

    GERMAN ECONOMIC REVIEW, Issue 1 2004
    Ivo 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]


    Towards an integrated computational tool for spatial analysis in macroecology and biogeography

    GLOBAL ECOLOGY, Issue 4 2006
    Thiago Fernando L. V. B. Rangel
    ABSTRACT Because most macroecological and biodiversity data are spatially autocorrelated, special tools for describing spatial structures and dealing with hypothesis testing are usually required. Unfortunately, most of these methods have not been available in a single statistical package. Consequently, using these tools is still a challenge for most ecologists and biogeographers. In this paper, we present sam (Spatial Analysis in Macroecology), a new, easy-to-use, freeware package for spatial analysis in macroecology and biogeography. Through an intuitive, fully graphical interface, this package allows the user to describe spatial patterns in variables and provides an explicit spatial framework for standard techniques of regression and correlation. Moran's I autocorrelation coefficient can be calculated based on a range of matrices describing spatial relationships, for original variables as well as for residuals of regression models, which can also include filtering components (obtained by standard trend surface analysis or by principal coordinates of neighbour matrices). sam also offers tools for correcting the number of degrees of freedom when calculating the significance of correlation coefficients. Explicit spatial modelling using several forms of autoregression and generalized least-squares models are also available. We believe this new tool will provide researchers with the basic statistical tools to resolve autocorrelation problems and, simultaneously, to explore spatial components in macroecological and biogeographical data. Although the program was designed primarily for the applications in macroecology and biogeography, most of sam's statistical tools will be useful for all kinds of surface pattern spatial analysis. The program is freely available at http://www.ecoevol.ufg.br/sam (permanent URL at http://purl.oclc.org/sam/). [source]


    TECHNOLOGY SHOCKS AND ROBUST SIGN RESTRICTIONS IN A EURO AREA SVAR,

    INTERNATIONAL ECONOMIC REVIEW, Issue 3 2009
    Gert 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 Republic

    INTERNATIONAL JOURNAL OF FINANCE & ECONOMICS, Issue 1 2001
    Martin 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]


    Estimating Long-term Trends in Tropospheric Ozone Levels

    INTERNATIONAL STATISTICAL REVIEW, Issue 1 2002
    Michael Smith
    Summary This paper develops Bayesian methodology for estimating long-term trends in the daily maxima of tropospheric ozone. The methods are then applied to study long-term trends in ozone at six monitoring sites in the state of Texas. The methodology controls for the effects of meteorological variables because it is known that variables such as temperature, wind speed and humidity substantially affect the formation of tropospheric ozone. A semiparametric regression model is estimated in which a nonparametric trivariate surface is used to model the relationship between ozone and these meteorological variables because, while it is known that the relatinship is a complex nonlinear one, its functional form is unknown. The model also allows for the effects of wind direction and seasonality. The errors are modeled as an autoregression, which is methodologically challenging because the observations are unequally spaced over time. Each function in the model is represented as a linear combination of basis functions located at all of the design points. We also estimate an appropriate data transformation simulataneously with the functions. The functions are estimated nonparametrically by a Bayesian hierarchical model that uses indicator variables to allow a non-zero probability that the coefficient of each basis term is zero. The entire model, including the nonparametric surfaces, data transformation and autoregression for the unequally spaced errors, is estimated using a Markov chain Monte Carlo sampling scheme with a computationally efficient transition kernel for generating the indicator variables. The empirical results indicate that key meteorological variables explain most of the variation in daily ozone maxima through a nonlinear interaction and that their effects are consistent across the six sites. However, the estimated trends vary considerably from site to site, even within the same city. [source]


    Measuring perceived community support: Factorial structure, longitudinal invariance, and predictive validity of the PCSQ (perceived community support questionnaire)

    JOURNAL OF COMMUNITY PSYCHOLOGY, Issue 2 2007
    Juan Herrero
    Social support from intimate and confiding relationships has received a great deal of attention; however, the study of the community as a relevant source of support has been comparatively lacking. In this article, we present a multidimensional measure of community support (Perceived Community Support Questionnaire, PCSQ). Through exploratory and confirmatory factor analyses on data from three samples of adult population (two-wave panel: sample 1, N = 1009 and sample 2, N = 780; and an independent sample 3, N = 440), results show that community integration, community participation, and use of community organizations are reliable indicators of the underlying construct of perceived community support. Also, community support is associated with a reduction of depressive symptoms after 6 months, once autoregression is controlled for. © 2007 Wiley Periodicals, Inc. [source]


    The growth,mortality relationship in larval cohorts of Sardinops melanostictus, revealed by using two new approaches to analyse longitudinal data from otoliths

    JOURNAL OF FISH BIOLOGY, Issue 7 2008
    G. Plaza
    The growth,mortality relationship was assessed for larval cohorts of the Japanese sardine Sardinops melanostictus using two new approaches: (1) repeat measures in general linear model (RM-GLMs) and (2) the autoregressive-individual method (AIM). Both methods were compared to the traditional approach in which repeat-measure ANOVA was used to compare the changes in increment width (WI) at age and otolith radii (RO) at age between individuals from an original population and survivors. In RM-GLMs, both the WI at age and RO at age (i.e. at 5, 10, 15 and 20 days) were used as the dependent variables, and the standardized residuals of both regressions RO and age and RO and total length (LT), age class, and day of the year as independent variables. A significant increase in WI at age and RO at age from younger to older age classes was seen as indicative of growth-dependent selection. In AIM, the RO -at-age relationship for each fish was fitted for the first 20 days, using autoregression, and then the growth traits (i.e. slopes) between the original cohorts and survivors were compared using ANOVA. In the traditional approach, the WI at age and RO at age for the first 20 days of an original population were compared with those of survivors sampled in later stages. GLMs and traditional approaches supported the growth rate (i.e. the faster an individual grows, the higher its probability of survival) and bigger is better (i.e. larger individuals at any given age will have lower probability of mortality than smaller individuals of the same age) mechanisms. Furthermore, AIM showed that individuals from original cohorts had lower otolith growth rates than those from survivors, giving further support for the growth,mortality hypothesis for the larval stage of this clupeid. [source]


    A New-Keynesian DSGE model for forecasting the South African economy

    JOURNAL OF FORECASTING, Issue 5 2009
    Dave' 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]


    Combining forecasts using optimal combination weight and generalized autoregression,

    JOURNAL OF FORECASTING, Issue 5 2008
    Jeong-Ryeol Kurz-Kim
    Abstract In this paper, we consider a combined forecast using an optimal combination weight in a generalized autoregression framework. The generalized autoregression provides not only a combined forecast but also an optimal combination weight for combining forecasts. By simulation, we find that short- and medium-horizon (as well as partly long-horizon) forecasts from the generalized autoregression using the optimal combination weight are more efficient than those from the usual autoregression in terms of the mean-squared forecast error. An empirical application with US gross domestic product confirms the simulation result. Copyright © 2008 John Wiley & Sons, Ltd. [source]


    BBVA-ARIES: a forecasting and simulation model for EMU

    JOURNAL OF FORECASTING, Issue 5 2003
    Fernando 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]


    GENETIC PROGRAMMING AND ITS APPLICATION IN REAL-TIME RUNOFF FORECASTING,

    JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION, Issue 2 2001
    Soon Thiam Khu
    ABSTRACT: Genetic programming (GP), a relatively new evolutionary technique, is demonstrated in this study to evolve codes for the solution of problems. First, a simple example in the area of symbolic regression is considered. GP is then applied to real-time runoff forecasting for the Orgeval catchment in France. In this study, GP functions as an error updating scheme to complement a rainfall-runoff model, MIKE11/NAM. Hourly runoff forecasts of different updating intervals are performed for forecast horizons of up to nine hours. The results show that the proposed updating scheme is able to predict the runoff quite accurately for all updating intervals considered and particularly for updating intervals not exceeding the time of concentration of the catchment. The results are also compared with those of an earlier study, by the World Meteorological Organization, in which autoregression and Kalman filter were used as the updating methods. Comparisons show that GP is a better updating tool for real-time flow forecasting. Another important finding from this study is that nondimensionalizing the variables enhances the symbolic regression process significantly. [source]


    Stability of nonlinear AR-GARCH models

    JOURNAL OF TIME SERIES ANALYSIS, Issue 3 2008
    Mika Meitz
    Abstract., This article studies the stability of nonlinear autoregressive models with conditionally heteroskedastic errors. We consider a nonlinear autoregression of order p [AR(p)] with the conditional variance specified as a nonlinear first-order generalized autoregressive conditional heteroskedasticity [GARCH(1,1)] model. Conditions under which the model is stable in the sense that its Markov chain representation is geometrically ergodic are provided. This implies the existence of an initial distribution such that the process is strictly stationary and , -mixing. Conditions under which the stationary distribution has finite moments are also given. The results cover several nonlinear specifications recently proposed for both the conditional mean and conditional variance, and only require mild moment conditions. [source]


    Properties of the Sieve Bootstrap for Fractionally Integrated and Non-Invertible Processes

    JOURNAL OF TIME SERIES ANALYSIS, Issue 2 2008
    D. S. Poskitt
    Abstract., In this article, we investigate the consequences of applying the sieve bootstrap under regularity conditions that are sufficiently general to encompass both fractionally integrated and non-invertible processes. The sieve bootstrap is obtained by approximating the data-generating process by an autoregression, whose order h increases with the sample size T. The sieve bootstrap may be particularly useful in the analysis of fractionally integrated processes since the statistics of interest can often be non-pivotal with distributions that depend on the fractional index d. The validity of the sieve bootstrap is established for |d|<1/2 and it is shown that when the sieve bootstrap is used to approximate the distribution of a general class of statistics then the error rate will be of an order smaller than , ,>0. Practical implementation of the sieve bootstrap is considered and the results are illustrated using a canonical example. [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]


    Large Sample Properties of Parameter Estimates for Periodic ARMA Models

    JOURNAL OF TIME SERIES ANALYSIS, Issue 6 2001
    I. V. Basawa
    This paper studies the asymptotic properties of parameter estimates for causal and invertible periodic autoregressive moving-average (PARMA) time series models. A general limit result for PARMA parameter estimates with a moving-average component is derived. The paper presents examples that explicitly identify the limiting covariance matrix for parameter estimates from a general periodic autoregression (PAR), a first-order periodic moving average (PMA(1)), and the mixed PARMA(1,1) model. Some comparisons and contrasts to univariate and vector autoregressive moving-average sequences are made. [source]


    Fractional Bayesian Lag Length Inference in Multivariate Autoregressive Processes

    JOURNAL OF TIME SERIES ANALYSIS, Issue 1 2001
    Mattias Villani
    The posterior distribution of the number of lags in a multivariate autoregression is derived under an improper prior for the model parameters. The fractional Bayes approach is used to handle the indeterminacy in the model selection caused by the improper prior. An asymptotic equivalence between the fractional approach and the Schwarz Bayesian Criterion (SBC) is proved. Several priors and three loss functions are entertained in a simulation study which focuses on the choice of lag length. The fractional Bayes approach performs very well compared to the three most widely used information criteria, and it seems to be reasonably robust to changes in the prior distribution for the lag length, especially under the zero-one loss. [source]


    A Coincident Index, Common Factors, and Monthly Real GDP,

    OXFORD BULLETIN OF ECONOMICS & STATISTICS, Issue 1 2010
    Roberto 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 2005
    Andrew 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 dependence

    THE ECONOMETRICS JOURNAL, Issue 2 2008
    Xiao 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]


    Bootstrapping Autoregression under Non-stationary Volatility

    THE ECONOMETRICS JOURNAL, Issue 1 2008
    Ke-Li Xu
    Summary This paper studies robust inference in autoregression around a polynomial trend with stable autoregressive roots under non-stationary volatility. The formulation of the volatility process is quite general including many existing deterministic and stochastic non-stationary volatility specifications. The aim of the paper is two-fold. First, it develops a limit theory for least squares estimators and shows how non-stationary volatility affects the consistency, convergence rates and asymptotic distributions of the slope and trend coefficients estimators in different ways. This complements the results recently obtained by Chung and Park (2007, Journal of Econometrics 137, 230,59. Second, it studies the recursive wild bootstrap procedure of Gonçalves and Kilian (2004, Journal of Econometrics 123, 89,120) in the presence of non-stationary volatility, and shows its validity when the estimates are asymptotically mixed Gaussian. Simulations are performed to compare favourably the recursive wild bootstrap with other inference procedures under non-stationary volatility. [source]