A simple test for PPP among traded goods

The so-called Balassa-Samuelson model implies that relative prices ofnon-traded goods may be nonstationary and, hence, that PPP should preferably betested on real exchange rates based on prices of traded goods only. We proposea simple test for PPP among traded goods which can be applied to real exchangerates based on prices of all (that is, both traded and non-traded) goods. Weshow through simulations that the test is reliable for a sample size commonlyconsidered in practice. Upon applying the test to bilateral real exchange ratesbased on the general CPI among a group of industrialized countries during therecent float, we find little evidence in favor of PPP among traded goods. Thisdoes not change when we use real exchange rates based on various componentsof the CPI.Purchasing power parity;Unidentified nuisance parameters;Unit roots

Measuring volatility with the realized range

Realized variance, being the summation of squared intra-day returns,has quickly gained popularity as a measure of daily volatility.Following Parkinson (1980) we replace each squared intra-day returnby the high-low range for that period to create a novel and moreefficient estimator called the realized range. In addition wesuggest a bias-correction procedure to account for the effects ofmicrostructure frictions based upon scaling the realized range withthe average level of the daily range. Simulation experimentsdemonstrate that for plausible levels of non-trading and bid-askbounce the realized range has a lower mean squared error than therealized variance, including variants thereof that are robust tomicrostructure noise. Empirical analysis of the S&P500index-futures and the S&P100 constituents confirm the potential ofthe realized range.realized volatility;bias-correction;high-frequency data;high-low range;market microstructure noise

Selecting a Nonlinear Time Series Model using Weighted Tests of Equal Forecast Accuracy

Nonlinear time series models have become fashionable tools to describe and forecast a variety of economic time series. A closer look at reported empirical studies, however, reveals that these models apparently fit well in-sample, but rarely show a substantial improvement in out-of-sample forecasts, at least over linear models. One of the many possible reasons for this finding is that inappropriate model selection criteria and forecast evaluation criteria are used. In this paper we therefore propose a novel criterion, which we believe does more justice to the very nature of nonlinear models. Simulations show that our criterion outperforms currently used criteria, in the sense that the true nonlinear model is more often found to perform better in out-of-sample forecasting than a benchmark linear model. An empirical illustration for US GDP emphasizes its relevance.model selection;forecast evaluation;forecasting;nonlinearity

Forecasting industrial production with linear, nonlinear, and structural change models

We compare the forecasting performance of linear autoregressive models, autoregressive models with structural breaks, self-exciting threshold autoregressive models, and Markov switching autoregressive models in terms of point, interval, and density forecasts for h-month growth rates of industrial production of the G7 countries, for the period January 1960-December 2000. The results of point forecast evaluation tests support the established notion in the forecasting literature on the favorable performance of the linear AR model. By contrast, the Markov switching models render more accurate interval and density forecasts than the other models, including the linear AR model. This encouraging finding supports the idea that non-linear models may outperform linear competitors in terms of describing the uncertainty around future realizations of a time series.nonlinearity;structural change;density forecasts;forecast evaluation tests;interval forecasts

The forecasting performance of various models for seasonality and nonlinearity for quarterly industrial production

Seasonality often accounts for the major part of quarterly or monthly movements in detrended macro-economic time series. In addition, business cycle nonlinearity is a prominent feature of many such series too. A forecaster can nowadays consider a wide variety of time series models which describe seasonal variation and regime-switching behaviour. In this paper we examine the forecasting performance of various models for seasonality and nonlinearity using quarterly industrial production series for 17 OECD countries. We find that forecasting performance varies widely across series, across forecast horizons and across seasons. However, in general, linear models with fairly simple descriptions of seasonality outperform at short forecast horizons, whereas nonlinear models with more elaborate seasonal components dominate at longer horizons.seasonality;industrial production;forecasting;nonlinearity

Are statistical reporting agencies getting it right? Data rationality and business cycle asymmetry

This paper provides new evidence on the rationality of industrial production (IP) and the producer price index (PPI). However, rather than examining preliminary and fully revised data, as is usually the practice, we examine the entire revision history for each data series. Thus, we are able to assess whether earlier releases of data are in any sense "less" rational than laterreleases, for example, and when early releases of data become rational. Our findings suggest that seasonally unadjusted IP and PPI become rational after approximately 3-4 months, while seasonally adjusted versions of these series remain irrational for at least 12 months after initial release. Additionally, we find that there is a clear increase in the volatility of early datareleases during recessions, suggesting that early data are less reliable in tougher economic times. One feature of the approach that we take is that we are able to include revision histories in the information sets used to examine the rationality of a particular release of data. This in turn allows us to assess whether the revision process itself is predictable from its own past, hence possibly leading to rules for the construction of "better" preliminary releases of data. For most of the variables examined, we find evidence of this form of predictability. Another feature of the approach taken in the paper is that we are able to provide evidence suggesting that nonlinearities in economic behavior manifest themselves in the form of nonlinearities in the rationality of early releases of economic data. This is done by separately analyzing expansionary and recessionary economic phases and by allowing for structural breaks. These types of nonlinearities are shown to be prevalent, and in some cases incorrect inferences concerning unbiasedness and efficiency arise when they are not taken account of. For example, seasonally unadjusted IP data become unbiased much more quickly after 1980 than before 1980. Additionally,seasonally adjusted IP data take less time to become efficient during expansions than during recessions.efficiency;real-time data set;unbiasedness

Short-term volatility versus long-term growth: evidence in US macroeconomic time series

We test for a change in the volatility of 215 US macroeconomic time series over the period 1960-1996. We find that about 90\\% of these series have experienced a break in volatility during this period. This result is robust to controlling for instability in the mean and business cycle nonlinearities. Real variables have seen a reduction in volatility since the early 1980s, which is accompanied by lower but steadier output growth. Furthermore, nominal variables have seentemporary increases in their volatility around the early 1980s. This suggests the existence of a trade-off between short-term volatility and the long-term pattern of growth.growth;Volatility;Business cycle nonlinearity;Structural change tests

Cointegration in a historical perspective

We analyse the impact of the Engle and Granger (1987) article by its citations over time, and find evidence of a second life starting in the new millennium. Next, we propose a possible explanation of the success of this citation classic. We argue that the conditions for its success were just right at the time of its appearance, because of the growing emphasis on time-series properties in econometric modelling, the empirical importance of stochastic trends, the availability of sufficiently long macro-economic time series, and the availability of personal computers and econometric software to carry out the new techniques.cointegration;citations

Modeling regional house prices

We develop a parsimonious panel model for quarterly regional house prices, for which both the cross-section and the time series dimension is large. The model allows for stochastic trends, cointegration, cross-equation correlations and, most importantly, latent-class clustering of regions. Class membership is fully data-driven and based on (i) average growth rates of house prices, (ii) the propagation of shocks to house prices across regions, also known as the ripple effect, and (iii) the relationship of house prices with economic growth and other variables. Applying the model to quarterly data for the Netherlands, we find convincing evidence for the existence of two distinct clusters of regions, with pronounced differences in house price dynamics.cointegration;ripple effect;cross-section dependence

Forecast comparison of principal component regression and principal covariate regression

Forecasting with many predictors is of interest, for instance, inmacroeconomics and finance. This paper compares two methods for dealing withmany predictors, that is, principal component regression (PCR) and principalcovariate regression (PCovR). Theforecast performance of these methods is compared by simulating data fromfactor models and from regression models. The simulations show that, in general, PCR performs better for the first type of data and PCovR performs better for the second type of data. The simulations also clarify the effect of the choice of the PCovR weight on the orecast quality.economic forecasting;principal components;factor model;principal covariates;regression model