Proof 2. From the Proposition 1 of Nielsen ( 2005 ) we have
11. Appendix: Simulation results
As specified in the simulation results section, there is weak evidence of consistency when the pa- rameters lie in the non-stationary regions. Indeed, the RMSE decreases very slowly as the sample size increases. Mostly in the strong cointegration case, the bias increases as the sample size increases. Inter- estingly, results are less impacted by non-stationarity in the weak cointegration case. This suggests that practitioners should devote a particular attention to the stationarity or non-stationarity of the data. In the latter case, they should use the estimator ofHualde (2014).
Table 7: Simulation results for 10000 replications of the strong cointegration model when ξ=0.1 and ρ=0
m= bn0.5c 256 512 1024
δ2 δ1 ˆθ Bias SD RMSE Bias SD RMSE Bias SD RMSE
0.6 0 δ2 0.024 0.027 0.166 0.032 0.016 0.131 0.032 0.009 0.101 δ1 0.169 0.045 0.271 0.197 0.027 0.257 0.234 0.017 0.267 ξ 0.002 0.003 0.055 0.001 0.001 0.036 0.001 0.001 0.027 β 0.039 0.013 0.122 0.043 0.010 0.110 0.050 0.008 0.103 0.8 0.2 δ2 0.007 0.029 0.170 0.004 0.018 0.133 0.001 0.011 0.105 δ1 0.185 0.042 0.277 0.217 0.028 0.273 0.259 0.017 0.290 ξ -0.007 0.005 0.071 -0.007 0.002 0.048 -0.006 0.001 0.034 β -0.002 0.037 0.193 0.005 0.029 0.169 0.010 0.022 0.148 0.9 0.4 δ2 -0.007 0.031 0.176 -0.007 0.021 0.144 -0.006 0.014 0.117 δ1 0.129 0.041 0.240 0.151 0.028 0.224 0.178 0.018 0.223 ξ -0.026 0.008 0.095 -0.024 0.004 0.069 -0.023 0.002 0.052 β -0.062 0.063 0.259 -0.070 0.051 0.236 -0.067 0.042 0.215 m= bn0.8c 0.6 0 δ2 -0.048 0.004 0.080 -0.052 0.002 0.071 -0.063 0.001 0.072 δ1 0.085 0.005 0.113 0.118 0.004 0.133 0.154 0.003 0.163 ξ 0.034 0.001 0.050 0.041 0.001 0.049 0.047 0.000 0.051 β 0.104 0.006 0.129 0.149 0.005 0.166 0.205 0.005 0.217 0.8 0.2 δ2 -0.051 0.005 0.087 -0.063 0.003 0.085 -0.083 0.002 0.096 δ1 0.092 0.006 0.122 0.133 0.005 0.151 0.180 0.005 0.192 ξ 0.041 0.002 0.060 0.056 0.001 0.065 0.070 0.001 0.074 β 0.104 0.015 0.160 0.183 0.013 0.216 0.287 0.012 0.308 0.9 0.4 δ2 -0.057 0.006 0.095 -0.066 0.005 0.095 -0.086 0.004 0.105 δ1 0.067 0.006 0.104 0.089 0.005 0.115 0.120 0.005 0.139 ξ 0.013 0.004 0.062 0.031 0.002 0.058 0.046 0.002 0.060 β 0.027 0.036 0.191 0.092 0.036 0.212 0.182 0.037 0.265 References
Abadir, K., Talmain, G., 2002. Aggregation, Persistence and Volatility in a Macro Model. Review of Economic Studies 69, 749-779.
Abadir, K.M., Caggiano, G., Talmain, G., 2013. Nelson-Plosser revisited: The ACF approach. Journal of Econometrics 175, 22-34.
Baillie, R.T., Bollerslev, T., 2000. The forward premium anomaly is not as bad as you think. Journal of International Money and Finance 19, 471-488.
Brenner, R., Kroner, K., 1995. Arbitrage, cointegration, and testing the unbiasedness hypothesis in financial markets. Journal of Financial and Quantitative Analysis 30, 23-42.
Caporale, G., Ciferri, D., Girardi, A., 2014. Time-varying spot and futures oil price dynamics. Scottish Journal of Political Economy 61, 78-97.
Chakraborty, A., Evans, G.W., 2008. Can perpetual learning explain the forward-premium puzzle? Journal of Mone- tary Economics 55, 477-490.
Cheung, Y., Lai, K., 1993. A fractional cointegration analysis of purchasing power parity. Journal of Business & Economic Statistics 11, 103-112.
Table 8: Simulation results for 10000 replications of the strong cointegration model when ξ=0.1 and ρ=0.4
m= bn0.5c 256 512 1024
δ2 δ1 ˆθ Bias SD RMSE Bias SD RMSE Bias SD RMSE
0.6 0 δ2 0.068 0.027 0.178 0.067 0.015 0.141 0.057 0.009 0.111 δ1 0.157 0.039 0.252 0.176 0.022 0.229 0.209 0.013 0.239 ξ 0.028 0.003 0.065 0.026 0.001 0.045 0.024 0.001 0.034 β 0.175 0.019 0.222 0.183 0.014 0.217 0.191 0.010 0.217 0.8 0.2 δ2 0.041 0.029 0.175 0.035 0.018 0.140 0.025 0.012 0.111 δ1 0.157 0.043 0.260 0.185 0.027 0.247 0.226 0.017 0.261 ξ 0.006 0.006 0.075 0.007 0.002 0.050 0.008 0.001 0.035 β 0.075 0.046 0.227 0.089 0.036 0.210 0.099 0.027 0.192 0.9 0.4 δ2 0.010 0.033 0.181 0.009 0.021 0.145 0.004 0.015 0.121 δ1 0.111 0.046 0.241 0.125 0.030 0.213 0.152 0.019 0.204 ξ -0.014 0.008 0.093 -0.012 0.004 0.068 -0.009 0.002 0.050 β 0.032 0.080 0.284 0.031 0.067 0.260 0.039 0.055 0.238 m= bn0.8c 0.6 0 δ2 -0.067 0.004 0.091 -0.066 0.002 0.079 -0.068 0.001 0.074 δ1 0.108 0.005 0.130 0.134 0.004 0.148 0.159 0.003 0.167 ξ 0.086 0.001 0.094 0.090 0.001 0.094 0.089 0.001 0.092 β 0.295 0.007 0.306 0.361 0.006 0.369 0.433 0.005 0.439 0.8 0.2 δ2 -0.068 0.005 0.098 -0.077 0.003 0.094 -0.093 0.002 0.102 δ1 0.116 0.007 0.143 0.156 0.005 0.172 0.200 0.005 0.212 ξ 0.082 0.002 0.094 0.096 0.001 0.101 0.108 0.001 0.111 β 0.255 0.018 0.289 0.367 0.016 0.388 0.506 0.015 0.520 0.9 0.4 δ2 -0.077 0.006 0.108 -0.084 0.004 0.106 -0.102 0.003 0.116 δ1 0.082 0.007 0.118 0.104 0.006 0.128 0.135 0.005 0.152 ξ 0.051 0.004 0.082 0.066 0.003 0.083 0.080 0.002 0.090 β 0.191 0.050 0.294 0.279 0.050 0.358 0.399 0.051 0.459
Chevillon, G., Massmann, M., Mavroeidis, S., 2010. Inference in models with adaptive learning. Journal of Monetary Economics 57, 341-351.
Chow, Y., McAleer, M., Sequeira, J., 2000. Pricing of forward and futures contracts. Journal of Economic Surveys 14, 215-253.
Christensen, B.J., Nielsen, M.Ø., 2006. Asymptotic normality of narrow-band least squares in the stationary fractional cointegration model and volatility forecasting. Journal of Econometrics 133, 343-371.
Engle, R., Granger, C.W.J, 1987. Co-integration and error correction: representation, estimation, and testing. Econo- metrica 55, 251-276.
de Truchis, G., 2013. Approximate Whittle Analysis of Fractional Cointegration and the Stock Market Synchronization Issue. Economic Modelling 24, 98-105.
Davidson, J., Hashimzade, N., 2009. Type I and type II fractional Brownian motions: A reconsideration. Computa- tional Statistics & Data Analysis 53, 2089-2106.
Davidson, J., Sibbertsen, P., 2005. Generating schemes for long memory processes: regimes, aggregation and linearity. Journal of Econometrics 128, 253-282.
Dueker, M., Startz, R., 1998. Maximum-likelihood estimation of fractional cointegration with an application to US and Canadian bond rates. Review of Economics and Statistics 80, 420-426.
Table 9: Simulation results for 10000 replications of the weak cointegration model when ξ=0.1 and ρ=0
m= bn0.5c 256 512 1024
δ2 δ1 ˆθ Bias SD RMSE Bias SD RMSE Bias SD RMSE
0.6 0.4 δ2 -0.017 0.034 0.185 -0.002 0.022 0.147 0.002 0.013 0.116 δ1 0.003 0.031 0.176 0.003 0.018 0.135 0.003 0.010 0.102 ξ 0.013 0.015 0.123 0.006 0.007 0.086 0.004 0.004 0.062 β 0.094 0.074 0.288 0.117 0.053 0.258 0.138 0.037 0.236 0.8 0.6 δ2 -0.022 0.033 0.184 -0.010 0.021 0.144 -0.008 0.013 0.113 δ1 0.010 0.031 0.176 0.008 0.020 0.141 0.006 0.012 0.109 ξ 0.020 0.021 0.146 0.013 0.011 0.107 0.012 0.006 0.079 β 0.139 0.184 0.451 0.157 0.137 0.402 0.166 0.098 0.354 0.9 0.8 δ2 -0.032 0.038 0.197 -0.025 0.027 0.165 -0.015 0.016 0.128 δ1 -0.000 0.029 0.171 -0.001 0.019 0.139 0.005 0.012 0.111 ξ 0.009 0.033 0.182 0.004 0.022 0.148 -0.003 0.012 0.109 β 0.107 0.388 0.632 0.083 0.271 0.527 0.064 0.196 0.447 m= bn0.8c 0.6 0.4 δ2 -0.052 0.004 0.085 -0.048 0.002 0.067 -0.047 0.001 0.059 δ1 -0.013 0.004 0.061 -0.007 0.002 0.044 -0.002 0.001 0.032 ξ 0.035 0.003 0.065 0.037 0.001 0.054 0.038 0.001 0.048 β 0.179 0.016 0.218 0.203 0.012 0.230 0.224 0.010 0.245 0.8 0.6 δ2 -0.061 0.004 0.090 -0.059 0.002 0.077 -0.062 0.001 0.073 δ1 -0.003 0.004 0.061 0.007 0.002 0.047 0.014 0.001 0.040 ξ 0.029 0.003 0.062 0.036 0.002 0.054 0.043 0.001 0.053 β 0.239 0.042 0.314 0.281 0.033 0.334 0.332 0.028 0.372 0.9 0.8 δ2 -0.046 0.006 0.090 -0.036 0.003 0.066 -0.031 0.002 0.053 δ1 -0.014 0.004 0.064 -0.005 0.002 0.049 0.002 0.001 0.038 ξ -0.014 0.005 0.069 -0.010 0.002 0.050 -0.005 0.001 0.038 β 0.058 0.093 0.311 0.072 0.078 0.288 0.091 0.073 0.286
cesses. Journal of Econometrics 167, 426-447.
Gil-Alana, L.A., Hualde, J., 2009. Fractional integration and cointegration. An overview and an empirical application. Palgrave handbook of Econometrics, Vol. 2. Applied Econometrics, K. Patterson and T.C. Mills eds, Palgrave, MacMillan, Vol. 2, 434-469.
Granger, C., 1980. Long memory relationships and the aggregation of dynamic models. Journal of Econometrics 14, 227-238.
Granger, C.W.J., 1981. Some properties of time series data and their use in econometric model specification. Journal of econometrics 16, 121-130.
Granger, C.W.J., 1986. Developments in the study of cointegrated economic variables. Oxford Bulletin of Economics and Statistics 4, 221-238.
Granger, C.W.J., 2010. Some thoughts on the development of cointegration. Journal of Econometrics 158, 3-6. Grossman, S., Stiglitz, J., 1980. On the impossibility of informationally efficient markets. The American Economic
Review 70, 393-408.
Hassler, U., Marmol, F., Velasco, C., 2006. Residual log-periodogram inference for long-run relationships. Journal of Econometrics 130, 165-207.
Helgason, H., Pipiras, V., Abry, P., 2011. Fast and exact synthesis of stationary multivariate Gaussian time series using circulant embedding. Signal Processing 91, 1123-1133.
Table 10: Simulation results for 10000 replications of the weak cointegration model when ξ=0.1 and ρ=0.4
m= bn0.5c 256 512 1024
δ2 δ1 ˆθ Bias SD RMSE Bias SD RMSE Bias SD RMSE
0.6 0.4 δ2 -0.007 0.034 0.185 0.009 0.022 0.147 0.014 0.013 0.114 δ1 -0.003 0.032 0.179 -0.011 0.020 0.141 -0.011 0.011 0.107 ξ 0.030 0.011 0.111 0.022 0.005 0.076 0.019 0.002 0.053 β 0.413 0.091 0.511 0.434 0.064 0.502 0.455 0.043 0.500 0.8 0.6 δ2 -0.019 0.034 0.184 -0.008 0.022 0.149 -0.001 0.013 0.113 δ1 0.002 0.033 0.182 -0.000 0.021 0.145 -0.005 0.012 0.109 ξ 0.029 0.016 0.128 0.024 0.008 0.094 0.021 0.004 0.068 β 0.407 0.221 0.622 0.421 0.158 0.579 0.435 0.115 0.552 0.9 0.8 δ2 -0.037 0.039 0.200 -0.025 0.024 0.158 -0.015 0.017 0.131 δ1 -0.001 0.032 0.180 -0.007 0.021 0.143 -0.005 0.013 0.116 ξ 0.012 0.027 0.166 0.005 0.015 0.123 0.000 0.009 0.094 β 0.374 0.454 0.771 0.350 0.325 0.669 0.336 0.225 0.581 m= bn0.8c 0.6 0.4 δ2 -0.064 0.004 0.088 -0.057 0.002 0.071 -0.053 0.001 0.061 δ1 -0.007 0.003 0.056 -0.005 0.002 0.041 -0.004 0.001 0.031 ξ 0.065 0.002 0.081 0.064 0.001 0.073 0.061 0.001 0.067 β 0.502 0.017 0.518 0.525 0.012 0.536 0.544 0.010 0.552 0.8 0.6 δ2 -0.071 0.004 0.094 -0.065 0.002 0.079 -0.064 0.001 0.073 δ1 -0.004 0.004 0.059 0.003 0.002 0.045 0.007 0.001 0.038 ξ 0.059 0.003 0.079 0.064 0.001 0.074 0.068 0.001 0.074 β 0.523 0.047 0.567 0.569 0.036 0.599 0.623 0.030 0.646 0.9 0.8 δ2 -0.053 0.005 0.089 -0.042 0.003 0.068 -0.035 0.002 0.053 δ1 -0.015 0.004 0.066 -0.007 0.003 0.051 -0.003 0.002 0.039 ξ -0.001 0.004 0.061 0.002 0.002 0.046 0.006 0.001 0.036 β 0.339 0.104 0.468 0.342 0.088 0.452 0.365 0.081 0.462
Hualde, J., 2006. Unbalanced Cointegration. Econometric Theory 22, 765-814.
Hualde, J., 2012. Weak convergence to a modified fractional Brownian motion. Journal of Time Series Analysis 33, 519-529.
Hualde, J., 2013. A simple test for the equality of integration orders. Economics Letters 119, 233-237.
Hualde, J., 2014. Estimation of long-run parameters in unbalanced cointegration. Journal of Econometrics 178, 761-778. Hualde, J., Robinson, P. M., 2007. Root-n-consistent estimation of weak fractional cointegration. Journal of Economet-
rics, 140, 450-484.
Hualde, J., Robinson, P.M., 2010. Semiparametric inference in multivariate fractionally cointegrated systems. Journal of Econometrics 157, 492-511.
Hurvich, C.M., Ray, B.K., 1995. Estimation of the memory parameter for nonstationary or noninvertible fractionally integrated processes. Journal of Time Series Analysis, 16, 17-42.
Johansen, S., Nielsen, M.Ø., 2012. Likelihood Inference for a Fractionally Cointegrated Vector Autoregressive Model. Econometrica 80, 2667-2732.
Liu, P., Tang, K., 2010. No-arbitrage conditions for storable commodities and the modeling of futures term structures. Journal of Banking & Finance 34, 1675-1687.
Liu, P., Tang, K., 2011. The stochastic behavior of commodity prices with heteroskedasticity in the convenience yield. Journal of Empirical Finance 18, 211-224.
Lobato, I.N., 1999. A semiparametric two-step estimator in a multivariate long memory model. Journal of Economet- rics 90, 129-153.
L ¨utkepohl, H., 1996. Handbook of Matrices, New York: Wiley.
Marinucci, D., Robinson, P.M., 1999. Alternative forms of fractional Brownian motion. Journal of Statistical Planning and Inference 80, 111-122.
Maynard, A., Phillips, P., 2001. Rethinking an old empirical puzzle: Econometric evidence on the forward discount anomaly. Journal of Applied Econometrics 16, 671-708.
Miller, J.I., Park, J.Y., 2010. Nonlinearity, nonstationarity, and thick tails: How they interact to generate persistence in memory. Journal of Econometrics 155, 83-89.
Nielsen, M.Ø., 2004. Optimal Residual-Based Tests for Fractional Cointegration and Exchange Rate Dynamics. Journal of Business & Economic Statistics 22, 331-345.
Nielsen, M.Ø., 2005. Semiparametric Estimation in Time-Series Regression with Long-Range Dependence. Journal of Time Series Analysis 26, 279-304.
Nielsen, M.Ø., 2007. Local Whittle Analysis of Stationary Fractional Cointegration and the Implied-Realized Volatility Relation. Journal of Business & Economic Statistics 25, 427-446.
Nielsen, M.Ø., Frederiksen, P., 2011. Fully modified narrow-band least squares estimation of weak fractional cointe- gration. The Econometrics Journal 14, 77-120.
Nielsen, M.Ø., Shimotsu, K., 2007. Determining the cointegrating rank in nonstationary fractional systems by the exact local Whittle approach. Journal of Econometrics 141, 574-596.
Olver, F., Lozier, D., Boisvert, R., 2010. NIST Handbook of Mathematical Functions. Cambridge University Press. Perron, P., Qu, Z., 2007. An analytical evaluation of the log-periodogram estimate in the presence of level shifts.
Boston University Working paper series.
Phillips, P., 1991. Optimal inference in cointegrated systems. Econometrica 59, 283-306.
Qu, Z., 2011. A test against spurious long memory. Journal of Business & Economic Statistics 29, 423-438.
Robinson, P., 1978. Statistical inference for a random coefficient autoregressive model. Scandinavian Journal of Statis- tics 5, 163-168.
Robinson, P.M., 1994. Semiparametric analysis of long-memory time series. The Annals of Statistics 22, 515-539. Robinson, P.M., 1995. Gaussian semiparametric estimation of long range dependence. The Annals of statistics 23,
1630-1661.
Robinson, P.M., 1995. Log-periodogram regression of time series with long range dependence. The Annals of Statistics 23, 1048-1072.
Robinson, P.M., 2005. The distance between rival nonstationary fractional processes. Journal of Econometrics 128, 283-300.
Robinson, P., 2008. Multiple local Whittle estimation in stationary systems. The Annals of Statistics 36, 2508-2530. Robinson, P., Henry, M., 1999. Long and short memory conditional heteroskedasticity in estimating the memory
parameter of levels. Econometric theory 15, 299-336.
Robinson, P.M., Hualde, J., 2003. Cointegration in fractional systems with unknown integration orders. Econometrica 71, 1727-1766.
Robinson, P.M., Marinucci, D., 2003. Semiparametric frequency domain analysis of fractional cointegration. In Time Series with Long Memory (ed. P. M. Robinson). Oxford University Press, pp. 334-373.
Robinson, P.M., Yajima, Y., 2002. Determination of cointegrating rank in fractional systems. Journal of Econometrics 106, 217-241.
Rossi, E., Santucci de Magistris, P., 2013. A no-arbitrage fractional cointegration model for futures and spot daily ranges. Journal of Futures Markets 33, 77-102.
Schennach, S., 2013. Long memory via networking. CEMMAP Working Paper No13.
Shimotsu, K., 2007. Gaussian semiparametric estimation of multivariate fractionally integrated processes. Journal of Econometrics 137, 277-310.
Shimotsu, K., 2010. Exact local Whittle estimation of fractional integration with unknown mean and time trend. Econometric Theory 1-43.
Shimotsu, K., 2012. Exact local Whittle estimation of fractionally cointegrated systems. Journal of Econometrics 169, 266-278.
Thornton, M.A., 2014. The aggregation of dynamic relationships caused by incomplete information. Journal of Econo- metrics 178, 342-351.
Tsay, W. J., Chung, C. F., 2000. The spurious regression of fractionally integrated processes. Journal of Econometrics, 96, 155-182.
Velasco, C., 1999a. Non-stationary log-periodogram regression. Journal of Econometrics 91, 325-371.
Velasco, C., 1999b. Gaussian semiparametric estimation for non-stationary time series. Journal of Time Series Analysis, 20, 87-127.
Velasco, C., 2003. Gaussian semi-parametric estimation of fractional cointegration. Journal of Time Series Analysis, 24, 345-378.
Zaffaroni, P., 2004. Contemporaneous aggregation of linear dynamic models in large economies. Journal of Econo- metrics 120, 75-102.