|
on Econometric Time Series |
| By: | Barbara Rossi; Tatevik Sekhposyan |
| Abstract: | This paper proposes forecast optimality tests that can be used in unstable environments. They include tests for forecast unbiasedness, efficiency, encompassing, serial uncorrelation, and, in general, regression-based tests of forecasting ability. The proposed tests are applied to evaluate the rationality of the Federal Reserve Greenbook forecasts as well as a variety of survey-based private forecasts. In addition, we consider whether Money Market Services forecasts are rational. Our robust tests suggest more empirical evidence against forecast rationality than previously found but con firm that the Federal Reserve has additional information about current and future states of the economy relative to market participants. |
| Keywords: | Forecasting, forecast optimality, regression-based tests of forecasting ability, Greenbook forecasts, survey forecasts, real-time data |
| JEL: | C22 C52 C53 |
| Date: | 2011 |
| URL: | http://d.repec.org/n?u=RePEc:duk:dukeec:11-18&r=ets |
| By: | Paresh Kumar Narayan; Stephan Popp |
| Abstract: | In this paper, we compare the small sample size and power properties of a newly developed endogenous structural break unit root test of Narayan and Popp (NP, 2010) with existing two break unit root tests, namely the Lumsdaine and Papell (LP, 1997) and the Lee and Strazicich (LS, 2003) tests. In contrast to the widely used LP and LS tests, the NP test chooses the break date by maximizing the significance of the break dummy coefficient. Using Monte Carlo simulations, we show that the NP test has better size and high power, and identifies the structural breaks accurately. Power and size comparisons of the NP test with the LP and LS tests reveal that the NP test is significantly superior. |
| Keywords: | size, power, structural breaks, unit root |
| Date: | 2011–08–29 |
| URL: | http://d.repec.org/n?u=RePEc:dkn:ecomet:fe_2011_07&r=ets |
| By: | Morten Ørregaard Nielsen (Queen's University and CREATES); Lealand Morin (Queen's University) |
| Abstract: | This manual describes the usage of the accompanying freely available software package for estimation and testing in the fractionally cointegrated vector autoregressive (VAR) model. |
| Keywords: | cofractional process, cointegration rank, fractional autoregressive model, fractional cointegration, fractional unit root, VAR model |
| JEL: | C22 C32 |
| Date: | 2011–08 |
| URL: | http://d.repec.org/n?u=RePEc:qed:wpaper:1273&r=ets |
| By: | Monica Billio (University of Venice, Gretta Assoc. and School for Advanced Studies In Venice); Roberto Casarin (University of Venice, Gretta Assoc. and School for Advanced Studies In Venice); Francesco Ravazzolo (Norges Bank); Herman K. van Dijk (Erasmus University Rotterdam, VU University Amsterdam) |
| Abstract: | We propose new forecast combination schemes for predicting turning points of business cycles. The combination schemes deal with the forecasting performance of a given set of models and possibly providing better turning point predictions. We consider turning point predictions generated by autoregressive (AR) and Markov-Switching AR models, which are commonly used for business cycle analysis. In order to account for parameter uncertainty we consider a Bayesian approach to both estimation and prediction and compare, in terms of statistical accuracy, the individual models and the combined turning point predictions for the United States and Euro area business cycles. |
| Keywords: | Turning Points; Markov-switching; Forecast Combination; Bayesian Model Averaging |
| JEL: | C11 C15 C53 E37 |
| Date: | 2011–08–22 |
| URL: | http://d.repec.org/n?u=RePEc:dgr:uvatin:20110123&r=ets |
| By: | Ramirez, Octavio A. |
| Abstract: | Simulation methods are used to measure the expected differentials between the Mean Square Errors of the forecasts from models based on temporally disaggregated versus aggregated data. This allows for novel comparisons including long-order ARMA models, such as those expected with weekly data, under realistic conditions where the parameter values have to be estimated. The ambivalence of past empirical evidence on the benefits of disaggregation is addressed by analyzing four different economic time series for which relatively large sample sizes are available. Because of this, a sufficient number of predictions can be considered to obtain conclusive results from out-of-sample forecasting contests. The validity of the conventional method for inferring the order of the aggregated models is revised. |
| Keywords: | Data Aggregation, Efficient Forecasting, Research Methods/ Statistical Methods, |
| Date: | 2011–08 |
| URL: | http://d.repec.org/n?u=RePEc:ags:ugeofs:113520&r=ets |
| By: | Ray-Bing Chen; Ying Chen; Wolfgang Härdle |
| Abstract: | Source extraction and dimensionality reduction are important in analyzing high dimensional and complex financial time series that are neither Gaussian distributed nor stationary. Independent component analysis (ICA) method can be used to factorize the data into a linear combination of independent compo- nents, so that the high dimensional problem is converted to a set of univariate ones. However conventional ICA methods implicitly assume stationarity or stochastic homogeneity of the analyzed time series, which leads to a low accu- racy of estimation in case of a changing stochastic structure. A time varying ICA (TVICA) is proposed here. The key idea is to allow the ICA filter to change over time, and to estimate it in so-called local homogeneous intervals. The question of how to identify these intervals is solved by the LCP (local change point) method. Compared to a static ICA, the dynamic TVICA pro- vides good performance both in simulation and real data analysis. The data example is concerned with independent signal processing and deals with a portfolio of highly traded stocks. |
| Keywords: | Adaptive Sequential Testing, Independent Component Analysis, Local Homogeneity, Signal Processing, Realized Volatility. |
| JEL: | C14 |
| Date: | 2011–08 |
| URL: | http://d.repec.org/n?u=RePEc:hum:wpaper:sfb649dp2011-054&r=ets |
| By: | Peter C.B. Phillips (Yale University, University of Auckland, University of Southampton & Singapore Management University); Shu-Ping Shi (Research School of Economics, The Australian National University); Jun Yu (School of Economics, Singapore Management Unversity) |
| Abstract: | Identifying explosive bubbles that are characterized by periodically collapsing behavior over time has been a major concern in the literature and is of great importance for practitioners. The complexity of the nonlinear structure in multiple bubble phenomena diminishes the discriminatory power of existing tests, as evidenced in early simulations conducted by Evans (1991). Multiple collapsing bubble episodes within the same sample period make bubble diagnosis particularly di¢ cult and complicate attempts at econometric dating. The present paper systematically investigates these issues and develops new procedures for practical implementation and surveillance strategies by central banks. We show how the testing procedure and dating algorithm of Phillips, Wu and Yu (2011, PWY) is affected by multiple bubbles and may fail to be consistent. To assist performance in such contexts, the present paper proposes a generalized version of the sup ADF test of PWY that addresses the difficulty. The asymptotic distribution of the generalized test is provided and the test is shown to signfi cantly improve discriminatory power in simulations. The paper advances a new date-stamping strategy for the origination and termination of multiple bubbles that is based on this generalized test and consistency of the date-stamping algorithm is established. The new strategy leads to distinct power gains over the date-stamping strategy of PWY when multiple bubbles occur. Empirical applications are conducted with both tests along with their respective date-stamping technology to S&P 500 stock market data from January 1871 to December 2010. The new approach identi es many key historical episodes of exuberance and collapse over this period, whereas the strategy of PWY locates only two such episodes in the same sample range. |
| Keywords: | Date-stamping strategy, Generalized sup ADF test, Multiple bubbles, Rational bubble, Periodically collapsing bubbles, Sup ADF test |
| JEL: | C15 C22 |
| Date: | 2011–08 |
| URL: | http://d.repec.org/n?u=RePEc:siu:wpaper:09-2011&r=ets |
| By: | Shu-Ping Shi (Research School of Economics, The Australian National University); Peter C.B. Phillips (Yale University, University of Auckland, University of Southampton & Singapore Management University); Jun Yu (School of Economics, Singapore Management Unversity) |
| Abstract: | Right-tailed unit root tests have proved promising for detecting exuberance in economic and financial activities. Like left-tailed tests, the limit theory and test performance are sensitive to the null hypothesis and the model specifi cation used in parameter estimation. This paper aims to provide some empirical guidelines for the practical implementation of right-tailed unit root tests, focussing on the sup ADF test of Phillips, Wu and Yu (2011), which implements a right-tailed ADF test repeatedly on a sequence of forward sample recursions. We analyze and compare the limit theory of the sup ADF test under different hypotheses and model speci cations. The size and power properties of the test under various scenarios are examined in simulations and some recommendations for empirical practice are given. An empirical application to Nasdaq data reveals the practical importance of model speci cation on test outcomes. |
| Keywords: | Unit root test; Mildly explosive process; Recursive regression; Size and power |
| JEL: | C15 C22 |
| Date: | 2011–08 |
| URL: | http://d.repec.org/n?u=RePEc:siu:wpaper:08-2011&r=ets |