|
on Econometric Time Series |
| By: | John W. Dawson; Mark C. Strazicich |
| Abstract: | This paper uses newly available long-span data on real per capita incomes from 1900-2001 to test for stochastic convergence in a diverse group of 29 countries. To perform our tests, we utilize the two-break LM unit root test of Lee and Strazicich (2003) and endogenously determine two distinct structural breaks in level and trend for each country. Despite including both OECD and non-OECD countries, we find significant evidence that incomes are stochastically converging. World War II is the most often identified time period of breaks. |
| Date: | 2006 |
| URL: | http://d.repec.org/n?u=RePEc:apl:wpaper:06-01&r=ets |
| By: | Krishnakumar, Jaya; Neto, David |
| Abstract: | This paper proposes a simple procedure to test the hypothesis of no cointegration against both threshold cointegration and an intermediate possibility that we call partial cointegration. Asymptotic theory is developed, the power of the proposed test is analysed through simulations and a successful empirical example is provided. |
| Date: | 2005–11 |
| URL: | http://d.repec.org/n?u=RePEc:gen:geneem:2005.04&r=ets |
| By: | Gary Koop; Roberto Leon-Gonzalez; Rodney Strachan |
| Abstract: | This paper develops methods of Bayesian inference in a cointegrating panel data model. This model involves each cross-sectional unit having a vector error correction representation. It is flexible in the sense that different cross-sectional units can have different cointegration ranks and cointegration spaces. Furthermore, the parameters which characterize short-run dynamics and deterministic components are allowed to vary over cross-sectional units. In addition to a noninformative prior, we introduce an informative prior which allows for information about the likely location of the cointegration space and about the degree of similarity in coefficients in different cross-sectional units. A collapsed Gibbs sampling algorithm is developed which allows for efficient posterior inference. Our methods are illustrated using real and artificial data. |
| Keywords: | Bayesian; panel data cointegration; error correction model; reduced rank regression; Markov Chain Monte Carlo |
| JEL: | C11 C32 C33 |
| Date: | 2006–01 |
| URL: | http://d.repec.org/n?u=RePEc:lec:leecon:06/2&r=ets |
| By: | Joseph Engelberg; Charles F. Manski; Jared Williams |
| Abstract: | We use data from the Survey of Professional Forecasters to compare point forecasts of GDP growth and inflation with the subjective probability distributions held by forecasters. We find that SPF forecasters summarize their underlying distributions in different ways and that their summaries tend to be favorable relative to the central tendency of the underlying distributions. We also find that those forecasters who report favorable point estimates in the current survey tend to do so in subsequent surveys. These findings, plus the inescapable fact that point forecasts reveal nothing about the uncertainty that forecasters feel, suggest that the SPF and similar surveys should not ask for point forecasts. It seems more reasonable to elicit probabilistic expectations and derive measures of central tendency and uncertainty, as we do here. |
| JEL: | C42 E27 E47 |
| Date: | 2006–01 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:11978&r=ets |
| By: | P.N. Smith; S. Sorensen; M.R. Wickens |
| Abstract: | We examine the relation between US stock market returns and the US business cycle for the period 1960-2003 using a new methodology that allows us to estimate a time-varying equity premium. We identify two channels in the transmission mechanism. One is through the mean of stock returns via the equity risk premium, and the other is through the volatility of returns. We provide support for previous findings based on simple correlation analysis that the relation is asymmetric with downturns in the business cycle having a greater negative impact on stock returns than the positive effect of upturns. We also obtain a new result, that demand and supply shocks affect stock returns differently. Our model of the relation between returns and their volatility encompasses CAPM, consumption CAPM and Merton's (1973) inter-temporal CAPM. It is implemented using a multi-variate GARCH-in-mean model with an asymmetric time-varying conditional heteroskedasticity and correlation structure. |
| JEL: | G12 C32 C51 E44 |
| Date: | 2006–01 |
| URL: | http://d.repec.org/n?u=RePEc:pas:camaaa:2006-05&r=ets |
| By: | Marcelo Cunha Medeiros (Department of Economics PUC-Rio); Felix Chan (University of Western Australia); Michael McAller (University of Western Australia) |
| Abstract: | Nonlinear time series models, especially those with regime-switching and GARCH errors, have become increasingly popular in the economics and finance literature. However, much of the research has concentrated on the empirical applications of various models, with little theoretical or statistical analysis associated with the structure of the processes or the associated asymptotic theory. In this paper we derive necessary and sufficient conditions for strict stationarity and ergodicity of three different specifications of the first-order STAR-GARCH model, and sufficient conditions for the existence of moments. This is important, among others, to establish the conditions under which the traditional LM linearity tests based on Taylor expansions are valid. Finally, we provide sufficient conditions for consistency and asymptotic normality of the Quasi-Maximum Likelihood Estimator. |
| Keywords: | Nonlinear time series, regime-switching, STAR, GARCH, log-moment, moment conditions, asymptotic theory. |
| Date: | 2005–11 |
| URL: | http://d.repec.org/n?u=RePEc:rio:texdis:506&r=ets |
| By: | Roberto Basile; Sergio Destefanis; Mauro Costantini |
| Abstract: | There is a plethora of studies of regional production functions using stationary panel data. Only some recent works consider non-stationary panel data. All of them assume the hypothesis of cross-section independence. Here, we claim that the independence assumption is too strong when regional data are used. In this paper, the cross-section independence assumption is released and cross-sectional dependence is assumed. First, unit roots and cointegration properties of the panel dataset are properly investigated by using newly developed tests for cross-sectionally dependent panels. Second, dynamic OLS (DOLS) and recent regression models for cross-sectionally correlated panels are used to estimate the cointegrated relationship between value added, physical and human capital, for Italian regions over the period 1970-1998. |
| Date: | 2005–08 |
| URL: | http://d.repec.org/n?u=RePEc:wiw:wiwrsa:ersa05p171&r=ets |
| By: | Ivan Paya (Universidad de Alicante); Agustín Duarte (Universidad de Alicante); Ioannis A. Venetis (Centre of Planning and Economic Research (KEPE)) |
| Abstract: | This papers finds evidence of fractional integration for a number of monthly ex post real interest rate series using the GPH semiparametric estimator on data from fourteen European countries and the US. However, we pose empirical questions on certain time series requirements that emerge from fractional integration and we find that they do not hold pointing to ¿spurious¿ long memory and casting doubts with respect to the theoretical origins of long memory in our sample. Common stochastic trends expressed as the sum of stationary past errors do not seem appropriate as an explanation of real interest rate covariation. From an economic perspective, our results suggest that most European countries show higher speed of real interest rate equalization with Germany rather than the US. |
| Keywords: | Real interest rate; long memory, fractional integration |
| JEL: | C22 E40 F41 |
| Date: | 2005–01 |
| URL: | http://d.repec.org/n?u=RePEc:ivi:wpasad:2005-01&r=ets |
| By: | Ivan Paya (Universidad de Alicante); David A. Peel (University Management School) |
| Abstract: | Recent research has reported the lack of correct size in stationarity test for PPP deviations within a linear framework. However, theoretically well motivated nonlinear models, such as the ESTAR, appear to parsimoniously fit the PPP data and provide an explanation for the PPP ¿puzzle¿. Employing Monte Carlo experiments we analyze the size and power of the nonlinear tests against a variety of nonstationary hypotheses. We also fit the ESTAR model to data from high inflation economies. Our results provide further support for ESTAR specification. |
| Keywords: | ESTAR, Real Exchange Rate, Size, Linearity Test. |
| JEL: | C15 C22 F31 |
| Date: | 2005–06 |
| URL: | http://d.repec.org/n?u=RePEc:ivi:wpasad:2005-23&r=ets |