nep-ets New Economics Papers
on Econometric Time Series
Issue of 2005‒01‒16
fifteen papers chosen by
Yong Yin
SUNY at Buffalo

  2. A Copula-Based Autoregressive Conditional Dependence Model of International Stock Markets By Rob van den Goorbergh
  3. User-Friendly Parallel Econometric Computations: Monte Carlo, Maximum Likelihood, and GMM By Michael Creel
  4. Higher Power Tests for Bilateral Failure of PPP after 1973 By Graham Elliott; Elena Pesavento
  5. Residuals Bases Tests for the Null of No Cointegration: an Analytical Comparison By Elena Pesavento
  6. Meese-Rogoff Redux: Micro-Based Exchange Rate Forecasting By Martin D. D. Evans(Georgetown University and NBER) and Richard K. Lyons(U.C. Berkeley and NBER, Haas School of Business)
  7. Where Are We Now? Real-time Estimates of the Macro Economy By Martin D. D. Evans(Georgetown University and NBER)
  8. Testing Structural Hypotheses on Cointegration Vectors: A Monte Carlo Study By Eriksson , Åsa
  9. Forecasting Austrian Inflation By Gabriel Moser; Fabio Rumler; Johann Scharler
  10. Forecasting Realized Volatility Using a Long Memory Stochastic Volatility Model: Estimation, Prediction and Seasonal Adjustment By Rohit Deo; Clifford Hurvich; Yi Lu
  11. The Variance Ratio Statistic at large Horizons By Willa Chen; Rohit Deo
  12. Estimation of mis-specified long memory models By Willa Chen; Rohit Deo
  13. Tracing the Source of Long Memory in Volatility By Rohit Deo; Mengchen Hsieh; Clifford Hurvich
  14. Five Years of Continuous-time Random Walks in Econophysics By Enrico Scalas
  15. Sector-Specific Volatility Patterns in Investment By Matthias Kredler

  1. By: Guglielmo Maria Caporale; Luis A. Gil-Alana
    Abstract: This paper examines aggregate money demand relationships in five industrial countries by employing a two-step strategy for testing the null hypothesis of no cointegration against alternatives which are fractionally cointegrated. Fractional cointegration would imply that, although there exists a long-run relationship, the equilibrium errors exhibit slow reversion to zero, i.e. that the error correction term possesses long memory, and hence deviations from equilibrium are highly persistent. It is found that the null hypothesis of no cointegration cannot be rejected for Japan. By contrast, there is some evidence of fractional cointegration for the remaining countries, i.e., Germany, Canada, the US, and the UK (where, however, the negative income elasticity which is found is not theory-consistent). Consequently, it appears that money targeting might be the appropriate policy framework for monetary authorities in the first three countries, but not in Japan or in the UK.
    Date: 2005–01
  2. By: Rob van den Goorbergh
    Abstract: This paper investigates the level and development of cross-country stock market dependence using daily returns on stock indices. The use of copulas allows us to build exible models of the joint distribution of stock index returns. In particular, we apply univariate AR(p)-GARCH(1,1) models to the margins with possibly skewed and fat tailed return innovations, while modelling the dependence between markets using parametric families of copulas which offer various alternatives to the commonly assumed normal dependence structure. Moreover, the dependence across stock markets is allowed to vary over time through a GARCH-like autoregressive conditional copula model. Using synchronous daily returns on U.S., U.K., and French stock indices, we find strong evidence that the conditional dependence between pairs of each of these markets varies over time. All market pairs show high levels of dependence persistence. The performance of the copula-based approach is compared with Engle's (2002) dynamic conditional correlation model and found to be superior.</td></tr> <tr> <td valign="top" class="txt">download: <span class="txtblue"><a href="bin/doc/Working%20Paper%20No.%2022-2004_tcm12-49221.pdf" title="Download Engelse versie">Engelse versie</a></span> | <span class="txtblue"><a href="#" onClick="popup2('/webForms.nsf/AanvraagPublicatie?openForm&org=DNB&lang=nl&titel=nr 022 - A Copula-Based Autoregressive Conditional Dependence Model of International Stock Markets','AanvraagPublicatie',600,550);" title="Bestel">bestel</a></span>
    Date: 2004–12
  3. By: Michael Creel
    Abstract: This paper shows how MPITB for GNU Octave may be used to perform Monte Carlo simulation and estimation by maximum likelihood and GMM in parallel on symmetric multiprocessor computers or clusters of workstations. The implementation of parallelization is done in a way such that an investigator may use the programs without any knowledge of parallel programming. Three example problems show that parallelization can lead to important reductions in computational time. Detailed discussion of how the Monte Carlo problem was parallelized is included as an example for learning to write parallel programs for Octave.
    Keywords: parallel computing; Monte Carlo; maximum likelihood; GMM
    JEL: C13 C15 C63 C87
    Date: 2005–01–10
  4. By: Graham Elliott; Elena Pesavento
    Abstract: Whilst point estimates for mean reversion in real exchange rates suggest reasonable (but long) half lives to shocks, it still remains uncomfortable that models without any mean reversion at all are often compatible with individual country pair data from the floating period. Studies with data over longer periods find mean reversion, but at the cost of mixing in data from earlier exchange rate arrangements. Pooling the floating period data for a number of countries also finds evidence of mean reversion, but at the expense of potentially mixing in country pairs with and without mean reversion. We examine tests for mean reversion for individual country pairs where greater power against close alternatives is gained through modeling other economic variables with the real exchange rate. Our results are broadly consistent with other methods to improve the power of tests for unit roots in real exchange rates, finding support for the mean reversion hypothesis.
    Date: 2005–01
  5. By: Elena Pesavento
    Abstract: This paper computes the asymptotic distribution of five residuals-based tests for the null of no cointegration under a local alternative when the tests are computed using both OLS and GLS detrended variables. The local asymptotic power of the tests is shown to be a function of Brownian Motion and Ornstein-Uhlenbeck processes, depending on a single nuisance parameter, which is determined by the correlation at frequency zero of the errors of the cointegration regression with the shocks to the right-hand variables. The tests are compared in terms of power in large and small samples. It is shown that, while no significant improvement can be achieved by using different unit root tests than the OLS detrended t-test originally proposed by Engle and Granger (1987), the power of GLS residuals tests can be higher than the power of system tests for some values of the nuisance parameter.
    Date: 2005–01
  6. By: Martin D. D. Evans(Georgetown University and NBER) and Richard K. Lyons(U.C. Berkeley and NBER, Haas School of Business) (Department of Economics, Georgetown University)
    Abstract: This paper compares the true, ex-ante forecasting performance of a micro-based model against both a standard macro model and a random walk. In contrast to existing literature, which is focused on longer horizon forecasting, we examine forecasting over horizons from one day to one month (the one-month horizon being where micro and macro analysis begin to overlap). Over our 3-year forecasting sample, we find that the micro-based model consistently out-performs both the random walk and the macro model. Micro-based forecasts account for almost 16 per cent of the sample variance in monthly spot rate changes. These results provide a level of empirical validation as yet unattained by other models. Though our micro-based model out-performs the macro model, this does not imply that past macro analysis has overlooked key fundamentals: our structural interpretation using a fundamentals-based model shows that our findings are consistent with exchange rates being driven by standard fundamentals. Classification-JEL Codes:F3, F4, G1
    Keywords: Exchange rates, forecasting, Meese and Rogoff, microstructure, order flow
  7. By: Martin D. D. Evans(Georgetown University and NBER) (Department of Economics, Georgetown University)
    Abstract: This paper describes a method for calculating daily real-time estimates of the current state of the U.S. economy. The estimates are computed from data on scheduled U.S. macroeconomic announcements using an econometric model that allows for variable reporting lags, temporal aggregation, and other complications in the data. The model can be applied to find real-time estimates of GDP, inflation, unemployment or any other macroeconomic variable of interest. In this paper I focus on the problem of estimating the current level of and growth rate in GDP. I construct daily real-time estimates of GDP that incorporate public information known on the day in question. The real-time estimates produced by the model are uniquely-suited to studying how perceived developments the macro economy are linked to asset prices over a wide range of frequencies. The estimates also provide, for the first time, daily time series that can be used in practical policy decisions. Classification-JEL Codes:E37 C32
    Keywords: Keywords: Real-time data, Kalman Filtering, Forecasting GDP
  8. By: Eriksson , Åsa (Department of Economics, Lund University)
    Abstract: In this paper, two tests for structural hypotheses on cointegration vectors are evaluated in a Monte Carlo study. The tests are the likelihood ratio test proposed by Johansen (1991) and the test for stationarity proposed by Kwiatkowski et al (1992). The analysis of the likelihood ratio test is extended with the inclusion of a Bartlett correction factor. Under circumstances common in empirical applications, all tests suffer from large size distortions and have low power to detect a false cointegration vector, but the Johansen (1991) test fares slightly better than the Kwiatkowski et al (1992) test. Applying a Bartlett correction factor in small samples improves to a large extent the likelihood ratio test.
    Keywords: Cointegration; Structural hypothesis; Monte Carlo simulation
    JEL: C12 C15 C22
    Date: 2004–12–17
  9. By: Gabriel Moser (Oesterreichische Nationalbank, Foreign Research Department, Otto-Wagner Platz 3, POB 61, A-1011 Vienna); Fabio Rumler (Oesterreichische Nationalbank, Economic Analysis Division); Johann Scharler (Oesterreichische Nationalbank, Economic Analysis Division)
    Abstract: In this paper we apply factor models proposed by Stock and Watson [18] and VAR and ARIMA models to generate 12-month out of sample forecasts of Austrian HICP inflation and its subindices processed food, unprocessed food, energy, industrial goods and services price inflation. A sequential forecast model selection procedure tailored to this specific task is applied. It turns out that factor models possess the highest predictive accuracy for several subindices and that predictive accuracy can be further improved by combining the information contained in factor and VAR models for some indices. With respect to forecasting HICP inflation, our analysis suggests to favor the aggregation of subindices forecasts. Furthermore, the subindices forecasts are used as a tool to give a more detailed picture of the determinants of HICP inflation from both an ex-ante and ex-post perspective.
    Keywords: Inflation Forecasting, Forecast Model selection, Aggregation
    JEL: C52 C53 E31
    Date: 2004–10–04
  10. By: Rohit Deo (New York University); Clifford Hurvich (New York University); Yi Lu (New York University)
    Abstract: We study the modeling of large data sets of high frequency returns using a long memory stochastic volatility (LMSV) model. Issues pertaining to estimation and forecasting of large datasets using the LMSV model are studied in detail. Furthermore, a new method of de-seasonalizing the volatility in high frequency data is proposed, that allows for slowly varying seasonality. Using both simulated as well as real data, we compare the forecasting performance of the LMSV model for forecasting realized volatility to that of a linear long memory model fit to the log realized volatility. The performance of the new seasonal adjustment is also compared to a recently proposed procedure using real data.
    Keywords: Realized Volatility, Long Memory Stochastic Volatility Model, High Frequency Data, Seasonal Adjustment
    JEL: C1 C2 C3 C4 C5 C8
    Date: 2005–01–07
  11. By: Willa Chen (Texas A&M University); Rohit Deo (New york University)
    Abstract: We make three contributions to using the variance ratio statistic at large horizons. Allowing for general heteroscedasticity in the data, we obtain the asymptotic distribution of the statistic when the horizon k is increasing with the sample size n but at a slower rate so that k/n¨0. The test is shown to be consistent against a variety of relevant mean reverting alternatives when k/n¨0. This is in contrast to the case when k/n¨ƒÂ>0, where the statistic has been recently shown to be inconsistent against such alternatives. Secondly, we provide and justify a simple power transformation of the statistic which yields almost perfectly normally distributed statistics in finite samples, solving the well known right skewness problem. Thirdly, we provide a more powerful way of pooling information from different horizons to test for mean reverting alternatives. Monte Carlo simulations illustrate the theoretical improvements provided.
    Keywords: Mean reversion, frequency domain, power transformations
    JEL: C12 C22
    Date: 2005–01–11
  12. By: Willa Chen (Texas A&M University); Rohit Deo (New York University)
    Abstract: We study the asymptotic behaviour of frequency domain maximum likelihood estimators of mis-specified models of long memory Gaussian series. We show that even if the long memory structure of the time series is correctly specified, mis-specification of the short memory dynamics may result in estimators of both long- and short-memory parameters that are slower than ãn consistent for the pseudo-true parameter values, which in general differ from the true values. The conditions under which this happens are provided and the asymptotic distribution of the estimators is shown to be non-Gaussian. Conditions under which estimators of the parameters of the mis-specified model have the standard ãn consistency for the pseudo-true values and are asymptotically normal are also provided.
    Keywords: long memory, model mis-specification
    JEL: C13 C22
    Date: 2005–01–11
  13. By: Rohit Deo (New York University); Mengchen Hsieh (New York University); Clifford Hurvich (New York University)
    Abstract: We study the effects of trade duration properties on dependence in counts (number of transactions) and thus on dependence in volatility of returns. A return model is established to link counts and volatility. We present theorems as well as a conjecture relating properties of durations to long memory in counts and thus in volatility. We then apply several parametric duration models to empirical trade durations and discuss our findings in the light of the theorems and conjecture.
    JEL: C1 C2 C3 C4 C5 C8
    Date: 2005–01–13
  14. By: Enrico Scalas (Universita' del Piemonte Orientale, Alessandria, Italy)
    Abstract: This paper is a short review on the application of continuos-time random walks to Econophysics in the last five years.
    Keywords: Duration; Continuous-time random walk; Fractional calculus; Statistical finance
    JEL: G
    Date: 2005–01–11
  15. By: Matthias Kredler (New York University)
    Abstract: This paper addresses the question if there are differences between time patterns in the volatility of investment across different industrial sectors. A competitive partial-equilibrium model with quadratic adjustment costs in investment and a GARCH demand shock is developed to predict aggregate investment in a sector. It is shown that under the assumptions made in the model, the GARCH property is inherited by the aggregate investment process in the rational-expectations equilibrium. The equation for investment from the model is estimated on quarterly time series from six industrial sectors in the UK. As conjectured, GARCH effects play an important role in some sectors but are not significant in others. Astonishingly, the volatility patterns are in general very different across sectors. This suggests that sector-specific factors are more important in determining investment volatility than the macroeconomic environment.
    Keywords: investment, volatility, variance, GARCH, ARCH, sector
    JEL: E22 C22
    Date: 2005–01–12

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