Finite Sample Properties of Impulse Response Intervals in SVECMs with Long-Run Identifying Restrictions

This paper investigates the finite sample properties of confidence intervals for structural vector error correction models (SVECMs) with long-run identifying restrictions on the impulse response functions. The simulation study compares methods that are frequently used in applied SVECM studies including an interval based on the asymptotic distribution of impulse responses, a standard percentile (Efron) bootstrap interval, Hall’s percentile and Hall’s studentized bootstrap interval. Data generating processes are based on empirical SVECM studies and evaluation criteria include the empirical coverage, the average length and the sign implied by the interval. Our Monte Carlo evidence suggests that applied researchers have little to choose between the asymptotic and the Hall bootstrap intervals in SVECMs. In contrast, the Efron bootstrap interval may be less suitable for applied work as it is less informative about the sign of the underlying impulse response function and the computationally demanding studentized Hall interval is often outperformed by the other methods. Differences between methods are illustrated empirically by using a data set from King, Plosser, Stock & Watson (1991).Structural vector error correction model, impulse response intervals, cointegration, long-run restrictions, bootstrap

Nonlinear Interest Rate Reaction Functions for the UK

We empirically analyze Taylor-type equations for short-term interest rates in the United Kingdom using quarterly data from 1970Q1 to 2006Q2. Starting from strong evidence against a simple linear Taylor rule, we model nonlinearities using logistic smooth transition regression (LSTR) models. The LSTR models with time-varying parameters consistently track actual interest rate movements better than a linear model with constant parameters. Our preferred LSTR model uses lagged interest rates as a transition variable and suggests that in times of recessions the Bank of England puts more weight on the output gap and less so on inflation. A reverse pattern is observed in non-recession periods. Parameters of the model change less frequently after 1992, when an inflation target range was announced. We conclude that for the analysis of historical monetary policy, the LSTR approach is a viable alternative to linear reaction functions.interest rate reaction functions, smooth transition regression model, monetary policy

Are Eastern European Countries Catching Up? Time Series Evidence for Czech Republic, Hungary, and Poland

The catching up process in Czech Republic, Hungary, and Poland is analyzed by investigating the integration properties of log-differences in per-capita GDP versus the EU15 and a Mediterranean country group. We account for structural changes by using unit root tests that allow for two endogenous breaks in the level and the trend. We find that Czech Republic and Hungary are stochastically converging towards the Mediterranean group, while only Czech Republic is stochastically converging towards EU15. Remaining per capita GDP differences are only reduced by deterministic trends. Extrapolating these trends we find that catching up will take about 20 years.Stochastic convergence, Catching up, Unit root tests, EU accession

Uncovered Interest Rate Parity and the Expectations Hypothesis of the Term Structure: Empirical Results for the U.S. and Europe

A system of U.S. and euro area short- and long-term interest rates is analyzed. According to the expectations hypothesis of the term structure the interest rate spreads should be stationary and according to the uncovered interest rate parity the difference between the U.S. and euro area longterm interest rates should also be stationary. If all four interest rates are integrated of order one, one would expect to find three linearly independent cointegration relations in the system of four interest rate series. Combining German and European Monetary Union data to obtain the euro area interest rate series we find indeed the theoretically expected three cointegration relations, in contrast to previous studies based on different data sets.Expectations hypothesis of the term structure, uncovered interest rate parity, unit roots, cointegration analysis

Comparison of Model Reduction Methods for VAR Processes

The objective of this study is to compare alternative computerized model-selection strategies in the context of the vector autoregressive (VAR) modeling framework. The focus is on a comparison of subset modeling strategies with the general-to-specific reduction approach automated by PcGets. Different measures of the possible gains of model selection are considered: (i) the chances of finding the `correct' model, that is, a model which contains all necessary right-hand side variables and is as parsimonious as possible, (ii) the accuracy of the implied impulse-responses and (iii) the forecast performance of the models obtained with different specification algorithms. In the Monte Carlo experiments, the procedures recover the DGP specification from a large VAR with anticipated size and power close to commencing from the DGP itself when evaluated at the empirical size. We find that subset strategies and PcGets are close competitors in many respects, with the forecast comparison indicating a clear advantage of the PcGets algorithm.Model selection, Vector autoregression, Subset model, Lag order determination, Data mining

Forecasting Euro-area macroeconomic variables using a factor model approach for backdating

"We suggest to use a factor model based backdating procedure to construct historical Euro-area macroeconomic time series data for the pre-Euro period. We argue that this is a useful alternative to standard contemporaneous aggregation methods. The paper investigates for a number of Euro-area variables whether forecasts based on the factorbackdated data are more precise than those obtained with standard area-wide data. A recursive pseudo-out-of-sample forecasting experiment using quarterly data and a forecasting period 2000Q1-2007Q4 is conducted. Our results suggests that some key variables (e.g. real GDP and in ation) can indeed be forecasted more precisely with the factor-backdated data." (author's abstract

VAR Modeling for Dynamic Semiparametric Factors of Volatility Strings

The implied volatility of a European option as a function of strike price and time to maturity forms a volatility surface. Traders price according to the dynamics of this high dimensional surface. Recent developments that employ semiparametric models approximate the implied volatility surface (IVS) in a finite dimensional function space, allowing for a low dimensional factor representation of these dynamics. This paper presents an investigation into the stochastic properties of the factor loading times series using the vector autoregressive (VAR) framework and analyzes associated movements of these factors with movements in some macroeconomic variables of the Euro - economy.Implied volatility surface, dynamic semiparametric factor model, unit root tests, vector autoregression, impulse responses

Inference in VARs with Conditional Heteroskedasticity of Unknown Form

We derive a framework for asymptotically valid inference in stable vector autoregressive (VAR) models with conditional heteroskedasticity of unknown form. We prove a joint central limit theorem for the VAR slope parameter and innovation covariance parameter estimators and address bootstrap inference as well. Our results are important for correct inference on VAR statistics that depend both on the VAR slope and the variance parameters as e.g. in structural impulse response functions (IRFs). We also show that wild and pairwise bootstrap schemes fail in the presence of conditional heteroskedasticity if inference on (functions) of the unconditional variance parameters is of interest because they do not correctly replicate the relevant fourth moments' structure of the error terms. In contrast, the residual-based moving block bootstrap results in asymptotically valid inference. We illustrate the practical implications of our theoretical results by providing simulation evidence on the finite sample properties of different inference methods for IRFs. Our results point out that estimation uncertainty may increase dramatically in the presence of conditional heteroskedasticity. Moreover, most inference methods are likely to understate the true estimation uncertainty substantially in finite samples

LONG JUMP KINEMATICS OF WORD CLASS ATHLETES WITH AN INTELLECTUAL DISABILITY

The aim of this study was to describe performance related kinematics parameters in long jump of elite athletes with an intellectual disability (ID athletes) and to compare to elite athletes without intellectual disability (non-ID athletes). The 2010 INAS athletics world indoor championships were analysed. Three high speed (100Hz) video cameras were used to observe the run up in 2D. A laser device recorded the full run up velocity. Overall jumping performance was worse in ID athletes compared to literature values of non-ID athletes. This also reflects low maximal run up and take-off velocities, a high within subject variations in the landing distance and distance of the last 3 steps to the take-off board. The take-off angles were comparable to those of non-ID athletes. Future research should relate performance related parameters to the cognitive potential of the athletes