Jurgen A. Doornik

Statistical algorithms for models in state space using SsfPack 2.2

This paper discusses and documents the algorithms of SsfPack 2.2. SsfPack is a suite of C routines for carrying out computations involving the statistical analysis of univariate and multivariate models in state space form. The emphasis is on documenting the link we have made to the Ox computing environment. SsfPack allows for a full range of different state space forms: from a simple time-invariant model to a complicated time-varying model. Functions can be used which put standard models such as ARIMA and cubic spline models in state space form. Basic functions are available for filtering, moment smoothing and simulation smoothing. Ready-to-use functions are provided for standard tasks such as likelihood evaluation, forecasting and signal extraction. We show that SsfPack can be easily used for implementing, fitting and analysing Gaussian models relevant to many areas of econometrics and statistics. Some Gaussian illustrations are given.

Stamp 5.0 : structural time series analyser, modeller and predictor

Part 1: installation procedure for STAMP. Part 2 Tutorials on structural time series modelling: getting started on simple univariate modelling tutorial on components tutorial on interventions and explanatory variables tutorial on multivariate models applications in macroeconomics and finance. Part 3 STAMP tutorials: the basic skills tutorial on graphics tutorial on data input and output tutorial on data transformation and description tutorial on model building and testing. Part 4 Statistical output: descriptive statistics statistical treatment of models model output output from STAMP model estimation selected model and estimation output summary statistics the sample period test hyperparameters variances and standard deviations cycle and AR(1) covariance matrices (multivariate models) factor loading matrices (multivariate models) transformed hyperparameters and standard errors final state analysis of state regression analysis seasonal tests cycle tests data in logs goodness of fit prediction error variance prediction error mean deviation coefficients of determination information criteria - AIC and BIC components series with components detrended seasonally adjusted individual seasonals data in logs joint components residuals correlogram periodogram and spectrum cumulative statistics and graphs distribution statistics heteroskedasticity. Part 5 STAMP manuals: general information STAMP files the information and binary data files (.IN7/.BN7) spreadsheet files (.XLS,.WKS,.WK1) human-readable files (.DAT) the information and ASCII data files (.IN7/.DAT) the print file (.PRN) results file (.OUT) PCX files (.PCX) algebra file (.ALG) forecast file (.STF) configuration file (STAMP.CFG) STAMP output the results window is full printscreen graphics the graphics window printing graphs graphics modes graphics display configuration limitations of STAMP memory management out of memory memory is low memory fragmentation saving memory command-line options session logging and playback lags the database size variable names missing values details of algebra STAMP menus. Part 6 Appendices: giveman file open configuration save configuration exit DataManager convert edit information file reconstruct information file compress information and binary file information video system setup colours install 800 x 600 x 16 HPP, PSP and NCDC GRAFPLUS user manual DOS extender manual STAMP error messages.

Approximations To The Asymptotic Distributions Of Cointegration Tests

The asymptotic distributions of cointegration tests are approximated using the Gamma distribution. The tests considered are for the I(1), the conditional I(1), as well as the I(2) model. Formulae for the parameters of the Gamma distributions are derived from response surfaces. The resulting approximation is flexible, easy to implement and more accurate than the standard tables previously published.

Inference in Cointegrating Models: UK M1 Revisited

The paper addresses the practical determination of cointegration rank. This is difficult for many reasons: deterministic terms play a crucial role in limiting distributions, and systems may not be formulated to ensure similarity to nuisance parameters; finite-sample critical values may differ from asymptotic equivalents; dummy variables alter critical values, often greatly; multiple cointegration vectors must be identified to allow inference; the data may be 1(2) rather than 1(1), altering distributions; and conditioning must be done with care. These issues are illustrated by an empirical application of multivariate cointegration analysis to a small model of narrow money, prices, output and interest rates in the UK. Copyright 1998 by Blackwell Publishers Ltd

Constructing Historical Euro-Zone Data

Existing methods of reconstructing historical Euro-zone data by aggregation of the individual countries aggregate data raises numerous difficulties, especially due to past exchange rate changes. The approach proposed here is designed to avoid such distortions, and aggregate exactly when exchange rates are fixed. We first compute growth rates within states, aggregate these, then cumulate this Euro-zone growth rate to obtain the aggregated levels variables. The aggregate of the implicit-deflator price index coincides with the implicit deflator of our aggregate nominal and real data. We apply the method to Eurozone M3, GDP and prices over the previous two decades.

Inference and Forecasting for ARFIMA Models With an Application to US and UK Inflation

Practical aspects of likelihood-based inference and forecasting of series with long memory are considered, based on the arfima(p; d; q) model with deterministic regressors. Sampling characteristics of approximate and exact first-order asymptotic methods are compared. The analysis is extended using modified profile likelihood analysis, which is a higher-order asymptotic method suggested by Cox and Reid (1987). The relevance of the differences between the methods is investigated for models and forecasts of monthly core consumer price inflation in the US and quarterly overall consumer price inflation in the UK.