nep-ets New Economics Papers
on Econometric Time Series
Issue of 2006‒08‒05
27 papers chosen by
Yong Yin
SUNY at Buffalo

  1. Linear and Threshold Forecasts of Output and Inflation with Stock and Housing Prices By Greg Tkacz; Carolyn Wilkins
  2. Using Monthly Indicators to Predict Quarterly GDP By Isabel Yi Zheng; James Rossiter
  3. Can Affine Term Structure Models Help Us Predict Exchange Rates? By Antonio Diez de los Rios
  4. Time series filtering techniques in Stata By Kit Baum
  5. Panels with Nonstationary Multifactor Error Structures By G. Kapetanios; M. Hashem Pesaran; T. Yamagata
  6. Modelling Structural Breaks in the US, UK and Japanese Unemployment Rates By Guglielmo Maria Caporale; Luis A. Gil-Alana
  7. Forecasting and Combining Competing Models of Exchange Rate Determination By Carlo Altavilla; Paul De Grauwe
  8. The relationship between expected inflation, disagreement, and uncertainty: evidence from matched point and density forecasts By Robert Rich; Joseph Tracy
  9. Stock returns and volatility: pricing the short-run and long-run components of market risk By Tobias Adrian; Joshua Rosenberg
  10. Setting the Operational Framework for Producing Inflation Forecasts By Eric Parrado; Turgut Kisinbay; Rodolfo Maino; Jorge Iván Canales Kriljenko
  11. U.S. Inflation Dynamics: What Drives Them Over Different Frequencies? By Ravi Balakrishnan; Sam Ouliaris
  12. An Evaluation of the World Economic Outlook Forecasts By Allan Timmermann
  14. Testing for Parameter Stability in Dynamic Models Across Frequencies By Bertrand Candelon; Gianluca Cubadda
  15. Measuring Core Inflation by Multivariate Structural Time Series Models By Tommaso Proietti
  16. On the Model Based Interpretation of Filters and the Reliability of Trend-Cycle Estimates By Tommaso Proietti
  17. "A Two-Stage Plug-In Bandwidth Selection and Its Implementation for Covariance Estimation" By Masayuki Hirukawa
  19. Forecasting German GDP using alternative factor models based on large datasets By Schumacher, Christian
  20. Ultra high frequency volatility estimation with dependent microstructure noise By Ait-Sahalia, Yacine; Mykland, Per A.; Zhang, Lan
  21. Dynamic factor models By Breitung, Jörg; Eickmeier, Sandra
  22. Unit roots and cointegration in panels By Breitung, Jörg; Pesaran, M. Hashem
  23. Inflation and relative price variability in the euro area: evidence from a panel threshold model By Nautz, Dieter; Scharff, Juliane
  24. Forecasting stock market volatility with macroeconomic variables in real time By Döpke, Jörg; Hartmann, Daniel; Pierdzioch, Christian
  25. Time Series of Count Data : Modelling and Estimation By Jung, Robert; Kukuk, Martin; Liesenfeld, Roman
  26. Alternative distributions for observation driven count series models By Drescher, Daniel
  27. Reviewing the sustainability/stationarity of current account imbalances with tests for bounded integration By Herwartz, Helmut; Xu, Fang

  1. By: Greg Tkacz; Carolyn Wilkins
    Abstract: The authors examine whether simple measures of Canadian equity and housing price misalignments contain leading information about output growth and inflation. Previous authors have found that the information content of asset prices in general, and equity and housing prices in particular, are unreliable in that they do not systematically predict future economic activity or inflation. However, earlier studies relied on simple linear relationships that would fail to pick up the potential non-linear effects of asset-price misalignments. The authors' results suggest that housing prices are useful for predicting GDP growth, even within a linear context. Moreover, both stock and housing prices can improve inflation forecasts, especially when using a threshold specification. These improvements in forecast performance are relative to the information contained in Phillips-curve type indicators for inflation and IS-curve type indicators for GDP growth.
    Keywords: Inflation and prices; Business fluctuations and cycles
    JEL: C53 E4
    Date: 2006
  2. By: Isabel Yi Zheng; James Rossiter
    Abstract: The authors build a model for predicting current-quarter real gross domestic product (GDP) growth using anywhere from zero to three months of indicators from that quarter. Their equation links quarterly Canadian GDP growth with monthly data on retail sales, housing starts, consumer confidence, total hours worked, and U.S. industrial production. The authors use time-series methods to forecast missing observations of the monthy indicators; this allows them to assess the performance of the method under various amounts of monthly information. The authors' model forecasts GDP growth as early as the first month of the reference quarter, and its accuracy generally improves with incremental monthly data releases. The final forecast from the model, available five to six weeks before the release of the National Income and Expenditure Accounts, delivers improved accuracy relative to those of several macroeconomic models used for short-term forecasting of Canadian output. The implications of real-time versus pseudo-real-time forecasting are investigated, and the authors find that the choice between real-time and latestavailable data affects the performance ranking among alternative models.
    Keywords: Economic models; Econometric and statistical methods
    JEL: C22 C53
    Date: 2006
  3. By: Antonio Diez de los Rios
    Abstract: The author proposes an arbitrage-free model of the joint behaviour of interest and exchange rates whose exchange rate forecasts outperform those produced by a random-walk model, a vector autoregression on the forward premiums and the rate of depreciation, and the standard forward premium regression. In addition, the model is able to reproduce the forward premium puzzle.
    Keywords: Exchange rates; Interest rates; Econometric and statistical methods
    JEL: E43 F31 G12 G15
    Date: 2006
  4. By: Kit Baum (Boston College)
    Abstract: I will describe a number of time series filtering techniques, including the Hodrick-Prescott, Baxter-King and bandpass filters and variants, and present new Mata-coded versions of these routines which are considerably more efficient than previous ado-code routines. Applications to an economic time series will be discussed.
    Date: 2006–07–23
  5. By: G. Kapetanios; M. Hashem Pesaran; T. Yamagata
    Abstract: The presence of cross-sectionally correlated error terms invalidates much inferential theory of panel data models. Recent work by Pesaran (2006) suggests a method which makes use of cross-sectional averages to provide valid inference for stationary panel regressions with multifactor error structure. This paper extends this work and examines the important case where the unobserved common factors follow unit root processes and could be cointegrated. It is found that the presence of unit roots does not affect most theoretical results which continue to hold irrespective of the integration and the cointegration properties of the unobserved factors. This finding is further supported for small samples via an extensive Monte Carlo study. In particular, the results of the Monte Carlo study suggest that the cross-sectional average based method is robust to a wide variety of data generation processes and has lower biases than all of the alternative estimation methods considered in the paper.
    Keywords: Cross Section Dependence, Large Panels, Unit Roots, Principal Components, Common Correlated Effects
    JEL: C12 C13 C33
    Date: 2006–08
  6. By: Guglielmo Maria Caporale; Luis A. Gil-Alana
    Abstract: In this paper we use a general procedure to detect structural breaks at unknown points in time which allows for different orders of integration and deterministic components in each subsample (see Gil-Alana, 2006). First, we extend it to the non-linear case, and show by means of Monte Carlo experiments that the procedure performs well in a non-linear environment. Second, we apply it to test for breaks in the unemployment rate in the US, the UK and Japan. Our results shed some light on the empirical relevance of alternative unemployment theories for these countries. Specifically, a structuralist interpretation appears more appropriate for the US and Japan, whilst a hysteresis model accounts better for the UK experience (and also for the Japanese one in the second subsample). We interpret these findings in terms of different labour market features.
    Keywords: unemployment, structural breaks, fractional integration
    JEL: C32 E32
    Date: 2006
  7. By: Carlo Altavilla; Paul De Grauwe
    Abstract: This paper investigates the out-of-sample forecast performance of a set of competing models of exchange rate determination. We compare standard linear models with models that characterize the relationship between exchange rate and its underlying fundamentals by nonlinear dynamics. Linear models tend to outperform at short forecast horizons especially when deviations from long-term equilibrium are small. In contrast, nonlinear models with more elaborate mean-reverting components dominate at longer horizons especially when deviations from long-term equilibrium are large. The results also suggest that combining different forecasting procedures generally produces more accurate forecasts than can be attained from a single model.
    Keywords: non-linearity, exchange rate modelling, forecasting
    JEL: C53 F31
    Date: 2006
  8. By: Robert Rich; Joseph Tracy
    Abstract: This paper examines matched point and density forecasts of inflation from the Survey of Professional Forecasters to analyze the relationship between expected inflation, disagreement, and uncertainty. We extend previous studies through our data construction and estimation methodology. Specifically, we derive measures of disagreement and uncertainty by using a decomposition proposed in earlier research by Wallis and by applying the concept of entropy from information theory. We also undertake the empirical analysis within a seemingly unrelated regression framework. Our results offer mixed support for the propositions that disagreement is a useful proxy for uncertainty and that increases in expected inflation are accompanied by heightened inflation uncertainty. However, we document a robust, quantitatively and statistically significant positive association between disagreement and expected inflation.
    Keywords: Inflation (Finance) ; Economic forecasting ; Information theory ; Uncertainty
    Date: 2006
  9. By: Tobias Adrian; Joshua Rosenberg
    Abstract: We decompose the time series of equity market risk into short- and long-run volatility components. Both components have negative and highly significant prices of risk in the cross section of equity returns. A three-factor model with the market return and the two volatility components compares favorably to benchmark models. We show that the short-run component captures market skewness risk, while the long-run component captures business cycle risk. Furthermore, short-run volatility is the more important cross-sectional risk factor, even though its average risk premium is smaller than the premium of the long-run component.
    Keywords: Stocks - Rate of return ; Risk
    Date: 2006
  10. By: Eric Parrado; Turgut Kisinbay; Rodolfo Maino; Jorge Iván Canales Kriljenko
    Abstract: How should a central bank organize itself to produce the best possible inflation forecast? This paper discusses elements for building a comprehensive platform for an inflation forecasting framework. It describes the exercise of forecasting inflation as a production process, which induces a strict discipline concerning data management, information gathering, the use of a suitable statistical apparatus, and the exercise of sound communication strategies to reinforce reputation and credibility. It becomes critical how a central bank organizes itself to produce relevant macroeconomic forecasts, with special consideration to product design, the essential requirements needed in the forecasting process, and key related organizational issues. In addition, the paper proposes to factor into the process the authorities' policy responses to previous inflation forecasts in order to be consistent with the spirit of the inflation targeting framework.
    Keywords: Inflation , Central bank organization , Inflation targeting , Monetary policy , Central bank policy , Data collection , Data analysis , Forecasting models ,
    Date: 2006–05–19
  11. By: Ravi Balakrishnan; Sam Ouliaris
    Abstract: This paper aims to improve the understanding of U.S. inflation dynamics by separating out structural from cyclical effects using frequency domain techniques. Most empirical studies of inflation dynamics do not distinguish between secular and cyclical movements, and we show that such a distinction is critical. In particular, we study traditional Phillips curve (TPC) and new Keynesian Phillips curve (NKPC) models of inflation, and conclude that the long-run secular decline in inflation cannot be explained in terms of changes in external trade and global factor markets. These variables tend to impact inflation primarily over the business cycle. We infer that the secular decline in inflation may well reflect improved monetary policy credibility and, thus, maintaining low inflation in the long run is closely linked to anchored inflation expectations.
    Date: 2006–07–12
  12. By: Allan Timmermann
    Abstract: The World Economic Outlook (WEO) is a key source of forecasts of global economic conditions. It is therefore important to review the performance of these forecasts against both actual outcomes and alternative forecasts. This paper conducts a series of statistical tests to evaluate the quality of the WEO forecasts for a very large cross section of countries, with particular emphasis on the recent recession and recovery. It assesses whether forecasts were unbiased and informationally efficient, and characterizes the process whereby WEO forecasts get revised as the time to the point of the forecast draws closer. Finally, the paper assess whether forecasts can be improved by combining WEO forecasts with the Consensus forecasts. The results suggest that the performance of the WEO forecasts is similar to that of the Consensus forecasts. While WEO forecasts for many variables in many countries meet basic quality standards in some, if not all, dimensions, the paper raises a number of concerns with current forecasting performance.
    Keywords: World Economic Outlook , Economic forecasting , Economic conditions ,
    Date: 2006–03–15
  13. By: Chin Nam Low; Heather Anderson; Ralph Snyder
    Abstract: This paper considers Beveridge-Nelson decomposition in a context where the permanent and transitory components both follow a Markov switching process. Our approach insorporates Markov switching into a single source of error state-space framework, allowing business cycle asymmetries and regime switches in the long-run multiplier.
    JEL: C22 C51 E32
    Date: 2006–07
  14. By: Bertrand Candelon (University of Maastricht - Department of Economics); Gianluca Cubadda (University of Rome II - Department of Financial and Quantitative Economics)
    Abstract: This paper contributes to the econometric literature on structural breaks by proposing a test for parameter stability in VAR models at a particular frequency w, where w [0, p]. When a dynamic model is affected by a structural break, the new tests allow for detecting which frequencies of the data are responsible for parameter instability. If the model is locally stable at the frequencies of interest, the whole sample size can be then exploited despite the presence of a break. Two empirical examples illustrate that local stability can concern only the lower frequencies (change in the U.S. monetary policy in the early 80'(s) or higher frequencies (decrease in the postwar U.S. productivity).
    Keywords: Structural breaks, spectral analysis, productivity slowdown, yield curve
    JEL: C32 E43
    Date: 2006–05–31
  15. By: Tommaso Proietti (Università degli Studi di Udine - Dipartimento di Scienze Statistiche)
    Abstract: The measurement of core inflation can be carried out by optimal signal extraction techniques based on the multivariate local level model, by imposing suitable restrictions on its parameters. The various restrictions correspond to several specialisations of the model:the core inflation measure becomes the optimal estimate of the common trend in a multivariate time series of inflation rates for a variety of goods and services, or it becomes a minimum variance linear combination of the inflation rates, or it represents the component generated by the common disturbances in a dynamic error component formulation of the multivariate local level model. Particular attention is given to the characterisation of the optimal weighting functions and to the design of signal extraction filters that can be viewed as two sided exponentially weighted moving averages applied to a cross-sectional average of individual inflation rates. An empirical application relative to U.S. monthly inflation rates for 8 expenditure categories is proposed.
    Keywords: common trends, dynamic factor analysis, homogeneity, exponential smoothing
    Date: 2006–05–31
  16. By: Tommaso Proietti (Università degli Studi di Udine - Dipartimento di Scienze Statistiche)
    Abstract: The paper explores and illustrates some of the typical trade-offs which arise in designing filters for the measurement of trends and cycles in economic time series, focusing, in particular, on the fundamental trade-off between the reliability of the estimates and the magnitude of the revisions as new observations become available. This assessment is available through a novel model based approach, according to which an important class of highpass and bandpass filters, encompassing the Hodrick-Prescott filter, are adapted to the particular time series under investigation. Via a suitable decomposition of the innovation process, it is shown that any linear time series with ARIMA representation can be broken down into orthogonal trend and cycle components, for which the class of filters is optimal. The main results then follow from Wiener-Kolmogorov signal extraction theory, whereas exact finite sample inferences are provided by the Kalman filter and smoother for the relevant state space representation of the decomposition.
    Keywords: Signal Extraction, Revisions, Kalman filter and Smoother, Bandpass
    Date: 2006–05–31
  17. By: Masayuki Hirukawa (Department of Economics, Concordia University and CIREQ)
    Abstract: To improve two existing bandwidth choice rules for kernel HAC estimation by Andrews (1991) and Newey and West (1994), this paper proposes to estimate an unknown quantity in the optimal bandwidth (called the normalized curvature) with a general class of kernels and derives the bandwidth that minimizes the asymptotic mean squared error of this estimator. The theory of the two-stage plug-in bandwidth selection and a reliable implementation method are developed. It is shown that the optimal bandwidth for the kernel-smoothed normalized curvature estimator should diverge at a slower rate than the one for the HAC estimator with the same kernel. Finite sample performances of the new HAC estimator are assessed through Monte Carlo simulations.
    Date: 2006–06
  18. By: Alicia Pérez Alonso (Universidad de Alicante)
    Abstract: This paper discusses how to test for conditional symmetry in time seriesregression models. To that end, we utilize the Bai and Ng test. We also examinethe performance of some popular (unconditional) symmetry tests for observationswhen applied to regression residuals. The tests considered include the coeficientof skewness, a joint test of the third and fifth moments, the Runs test, the Wilcoxonsigned-rank test and the Triples test. An easy-to-implement symmetric bootstrapprocedure is proposed to calculate critical values for these tests. Consistency of thebootstrap procedure will be shown. A simple Monte Carlo experiment isconducted to explore the finite-sample properties of all the tests.
    Keywords: Near Epoch Dependence; Nonparametric tests; Conditional symmetry; Boot- strap; Monte Carlo simulation
    JEL: C12 C15 C22
    Date: 2006–07
  19. By: Schumacher, Christian
    Abstract: This paper discusses the forecasting performance of alternative factor models based on a large panel of quarterly time series for the german economy. One model extracts factors by static principals components analysis, the other is based on dynamic principal components obtained using frequency domain methods. The third model is based on subspace algorithm for state space models. Out-of-sample forecasts show that the prediction errors of the factor models are generally smaller than the errors of simple autoregressive benchmark models. Among the factors models, either the dynamic principal component model or the subspace factor model rank highest in terms of forecast accuracy in most cases. However, neither of the dynamic factor models can provide better forecasts than the static model over all forecast horizons and different specifications of the simulation design. Therefore, the application of the dynamic factor models seems to provide only small forecasting improvements over the static factor model for forecasting German GDP.
    Keywords: Factor models, static and dynamic factors, principal components, forecasting accuracy
    JEL: C43 C51 E32
    Date: 2005
  20. By: Ait-Sahalia, Yacine; Mykland, Per A.; Zhang, Lan
    Abstract: We analyze the impact of time series dependence in market microstructure noise on the properties of estimators of the integrated volatility of an asset price based on data sampled at frequencies high enough for that noise to be a dominant consideration. We show that combining two time scales for that purpose will work even when the noise exhibits time series dependence, analyze in that context a refinement of this approach based on multiple time scales, and compare empirically our different estimators to the standard realized volatility.
    Keywords: Market microstructure, Serial dependence, High frequency data, Realized volatility, Subsampling, Two Scales Realized Volatility
    Date: 2005
  21. By: Breitung, Jörg; Eickmeier, Sandra
    Abstract: Factor models can cope with many variables without running into scarce degrees of freedom problems often faced in a regression-based analysis. In this article we review recent work on dynamic factor models that have become popular in macroeconomic policy analysis and forecasting. By means of an empirical application we demonstrate that these models turn out to be useful in investigating macroeconomic problems.
    Keywords: Principal components, dynamic factors, forecasting
    JEL: C13 C33 C51
    Date: 2005
  22. By: Breitung, Jörg; Pesaran, M. Hashem
    Abstract: This paper provides a review of the literature on unit roots and cointegration in panels where the time dimension (T), and the cross section dimension (N) are relatively large. It distinguishes between the first generation tests developed on the assumption of the cross section independence, and the second generation tests that allow, in a variety of forms and degrees, the dependence that might prevail across the different units in the panel. In the analysis of cointegration the hypothesis testing and estimation problems are further complicated by the possibility of cross section cointegration which could arise if the unit roots in the different cross section units are due to common random walk components.
    Keywords: Panel Unit Roots, Panel Cointegration, Cross Section Dependence, Common Effects
    JEL: C12 C15 C22 C23
    Date: 2005
  23. By: Nautz, Dieter; Scharff, Juliane
    Abstract: In recent macroeconomic theory, relative price variability (RPV) generates the central distortions of inflation. This paper provides first evidence on the empirical relation between inflation and RPV in the euro area focusing on threshold effects of inflation. We find that expected inflation significantly increases RPV if inflation is either very low (below -1.38% p.a.) or very high (above 5.94% p.a.). In the intermediate regime, however, expected inflation has no distorting effects which supports price stability as an outcome of optimal monetary policy.
    Keywords: Inflation, Relative Price Variability, Panel Threshold Models
    JEL: C23 E31
    Date: 2006
  24. By: Döpke, Jörg; Hartmann, Daniel; Pierdzioch, Christian
    Abstract: We compared forecasts of stock market volatility based on real-time and revised macroeconomic data. To this end, we used a new dataset on monthly real-time macroeconomic variables for Germany. The dataset covers the period 1994-2005. We used a statistical, a utility-based, and an options-based criterion to evaluate volatility forecasts. Our main result is that the statistical and economic value of volatility forecasts based on real-time data is comparable to the value of forecasts based on revised macroeconomic data.
    Keywords: Forecasting stock market volatility, Real-time macroeconomic data, Evaluation of forecasting accuracy
    JEL: C53 E44 G11
    Date: 2005
  25. By: Jung, Robert; Kukuk, Martin; Liesenfeld, Roman
    Abstract: This paper compares various models for time series of counts which can account for discreetness, overdispersion and serial correlation. Besides observation- and parameter-driven models based upon corresponding conditional Poisson distributions, we also consider a dynamic ordered probit model as a flexible specification to capture the salient features of time series of counts. For all models, we present appropriate efficient estimation procedures. For parameter-driven specifications this requires Monte Carlo procedures like simulated Maximum likelihood or Markov Chain Monte-Carlo. The methods including corresponding diagnostic tests are illustrated with data on daily admissions for asthma to a single hospital.
    Keywords: Efficient Importance Sampling, GLARMA, Markov Chain Monte-Carlo, Observation-driven model, Parameter-driven model, Ordered Probit
    Date: 2005
  26. By: Drescher, Daniel
    Abstract: Observation-driven models provide a flexible framework for modelling time series of counts. They are able to capture a wide range of dependence structures. Many applications in this field of research are concerned with count series whose conditional distribution given past observations and explanatory variables is assumed to follow a Poisson distribution. This assumption is very convenient since the Poisson distribution is simple and leads to models which are easy to implement. On the other hand this assumption is often too restrictive since it implies equidispersion, the fact that the conditional mean equals the conditional variance. This assumption is often violated in empirical applications. Therefore more flexible distributions which allow for overdispersion or underdispersion should be used. This paper is concerned with the use of alternative distributions in the framework of observationdriven count series models. In this paper different count distributions and their properties are reviewed and used for modelling. The models under consideration are applied to a time series of daily counts of asthma presentations at a Sydney hospital. This data set has already been analyzed by Davis et al. (1999, 2000). The Poisson-GLARMA model proposed by these authors is used as a benchmark. This paper extends the work of Davis et al. (1999) to distributions which are nested in either the generalized negative binomial or the generalized Poisson distribution. Additionally the maximum likelihood estimation for observation-driven models with generalized distributions is presented in this paper.
    Keywords: Count series, observation-driven models, GLARMA, dicrete distributions
    JEL: C13 C22 C25
    Date: 2005
  27. By: Herwartz, Helmut; Xu, Fang
    Abstract: We investigate for 26 OECD economies if their current account imbalances are driven by stochastic trends. Standard ADF results are contrasted with tests accounting for the bounded support of the current account. Neglecting the latter feature might give misleading results in the sense that ADF based conclusions are biased towards the rejection of unit root features. The current account imbalances are found to be bounded nonstationary for most OECD economies. Panel based test statistics confirm the bounded nonstationarity for these series.
    Keywords: Current account, bounded unit root tests
    JEL: C12 C22 C23 E21 E22 F32
    Date: 2006

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