Revisiting Useful Approaches to Data-Rich Macroeconomic Forecasting

This paper revisits a number of data-rich prediction methods, like factor models, Bayesian ridge regression and forecast combinations, which are widely used in macroeconomic forecasting, and compares these with a lesser known alternative method: partial least squares regression. Under the latter, linear, orthogonal combinations of a large number of predictor variables are constructed such that these linear combinations maximize the covariance between the target variable and each of the common components constructed from the predictor variables. We provide a theorem that shows that when the data comply with a factor structure, principal components and partial least squares regressions provide asymptotically similar results. We also argue that forecast combinations can be interpreted as a restricted form of partial least squares regression. Monte Carlo experiments confirm our theoretical result that principal components and partial least squares regressions are asymptotically similar when the data has a factor structure. These experiments also indicate that when there is no factor structure in the data, partial least squares regression outperforms both principal components and Bayesian ridge regressions. Finally, we apply partial least squares, principal components and Bayesian ridge regressions on a large panel of monthly U.S. macroeconomic and financial data to forecast, for the United States, CPI inflation, core CPI inflation, industrial production, unemployment and the federal funds rate across different sub-periods. The results indicate that partial least squares regression usually has the best out-of-sample performance relative to the two other data-rich prediction methods.Macroeconomic forecasting, Factor models, Forecast combination, Principal components, Partial least squares, (Bayesian) ridge regression

Commodity prices, commodity currencies, and global economic developments

In this paper we seek to produce forecasts of commodity price movements that can systematically improve on naive statistical benchmarks, and revisit the forecasting performance of changes in commodity currencies as efficient predictors of commodity prices, a view emphasized in the recent literature. In addition, we consider different types of factor-augmented models that use information from a large data set containing a variety of indicators of supply and demand conditions across major developed and developing countries. These factor-augmented models use either standard principal components or partial least squares (PLS) regression to extract dynamic factors from the data set. Our forecasting analysis considers ten alternative indices and sub-indices of spot prices for three different commodity classes across different periods. We .find that the exchange rate-based model and especially the PLS factor-augmented model are more prone to outperform the naive statistical benchmarks. However, across our range of commodity price indices we are not able to generate out-of-sample forecasts that, on average, are systematically more accurate than predictions based on a random walk or autoregressive specifications.

New multi-country evidence on purchasing power parity: multivariate unit root test results

In this paper a likelihood-based multivariate unit root testing framework is utilized to test whether the real exchange rates of G10 countries are non-stationary. The framework uses a likelihood ratio statistic which combines the information across all involved countries while retaining heterogeneous rates of mean reversion. This likelihood ratio statistic has an asymptotic distribution which can be typified as a summation of squared, univariate Dickey and Fuller (1979) distributions. Our multivariate unit root tests indicate that bilateral G10 real exchange rates are stationary, irrespective of the numeraire country. We also analyze per panel the time necessary to have an adjustment to a shock in the individual real exchange rates. From this analysis it becomes apparent that there are significant cross-country differences in the adjustment of individual real exchange rates within each panel

Evaluating future urbanisation patterns in the Netherlands

Although the Netherlands is one of the most densely populated countries, two thirds of the land area are still under agricultural use. Major socio-economic changes are however expected for the agricultural sector. The increasing globalisation of economic relations in agriculture and the possible reduction of European price support to farmers are examples of such developments that may affect agricultural land use. At the same time other land use functions put increasing pressure on rural land in order to accommodate housing, employment, recreation and water storage. The present study takes a closer look at the expected spatial developments and simulates possible future land use patterns by using an economics based land use model. Two opposing scenarios of anticipated land use change are used to illustrate the possible extremes of future land use configurations. These scenarios vary both in their quantitative and qualitative description of the projected changes. The simulation of low-density residential areas in green areas will illustrate this approach. The development of these new rural living areas is currently a sensitive topic in the public debate on urbanisation. The simulated urbanisation patterns are evaluated in terms of their impact on spatial policy related issues through the application of newly developed indicators. For decades the Dutch government has strived for compact forms of urbanisation in order to preserve the remaining stretches of open space. The applied metrics of land use change will therefor focus on the concentration of urbanisation and the fragmentation of open space. The findings of this study may be especially interesting now Dutch spatial policy seems to be on the brink of loosening its traditional grip on spatial planning.

New Multi-Country Evidence on Purchasing Power Parity: Multi-Variate Unit Root Test Results

In this paper a likelihood-based multi-variate unit root testing framework is utilized to test whether the real exchange rates of G10 countries are non-stationary. The framework uses a likelihood ratio statistic which combines the information across all involved countries while retaining heterogeneous rates of mean reversion. This likelihood ratio statistic has an asymptotic distribution which can be typified as a summation of squared, univariate Dickey and Fuller (1979) distributions. Our multi-variate unit root tests indicate that bilateral G10 real exchange rates are stationary, irrespective of the numeraire country. On the other hand, the choice of the numeraire country seems to be of importance for the issue whether mean reversion rates across G10 real exchange rates are heterogeneous or homogeneous.

Sacral (S3) segmental nerve stimulation as a treatment for urge incontinence in patients with detrusor instability: Results of chronic electrical stimulation using an implantable neural prosthesis

Most patients with urge incontinence and idiopathic detrusor instability are initially treated conservatively with bladder retraining, pelvic floor exercises and biofeedback, while in the majority this regimen will be supplemented with anticholinergic drugs. The urinary incontinence guideline panel has summarized the literature on results achieved with various drugs for urge incontinence, and found that oxybutynin and terodiline appeared to be the most effective.[1] Subjective cure rates of up to 44 percent over placebo and decreased urinary incontinence rates in up to 56 percent over placebo were achieved with these drugs.[1] Interestingly, no changes in urodynamic parameters were found in well designed drug trials despite symptomatic improvement.2 and 3 Fortunately, many patients seem to be satisfied with a less than optimal result. Patients who do not achieve an acceptable condition remain a therapeutic problem and alternative procedures, with variable success rates, such as bladder transection, transvesical phenol injection of the pelvic plexus, augmentation ileocystoplasty and even urinary diversion, are being advocated.[4] Unilateral sacral segmental nerve stimulation by a permanent foramen S3 electrode (neuromodulation) offers a nondestructive alternative for those whose condition is refractory to conservative measures. The aim of this treatment modality is to achieve detrusor inhibition by chronic electrical stimulation of afferent somatic sacral nerve fibers via an implanted electrode coupled to a subcutaneously placed pulse generator. The ratio of this treatment modality is based on the existence of spinal inhibitory systems that are capable of interrupting a detrusor contraction. Inhibition can be achieved by electrical stimulation of afferent anorectal branches of the pelvic nerve, afferent sensory fibers in the pudendal nerve and muscle afferents from the limbs.5, 6 and 7 Most of these branches and fibers reach the spinal cord via the dorsal roots of the sacral nerves. Of the sacral nerve roots the S3 root is the most practical for use in chronic electrical stimulation.[8] We evaluate the effectiveness of this treatment modality in patients with urge incontinence due to bladder instability

Real-time inflation forecasting in a changing world

This paper revisits inflation forecasting using reduced form Phillips curve forecasts, i.e., inflation forecasts using activity and expectations variables. We propose a Phillips curve-type model that results from averaging across different regression specifications selected from a set of potential predictors. The set of predictors includes lagged values of inflation, a host of real activity data, term structure data, nominal data and surveys. In each of the individual specifications we allow for stochastic breaks in regression parameters, where the breaks are described as occasional shocks of random magnitude. As such, our framework simultaneously addresses structural change and model certainty that unavoidably affects Phillips curve forecasts. We use this framework to describe PCE deflator and GDP deflator inflation rates for the United States across the post-WWII period. Over the full 1960-2008 sample the framework indicates several structural breaks across different combinations of activity measures. These breaks often coincide with, amongst others, policy regime changes and oil price shocks. In contrast to many previous studies, we find less evidence for autonomous variance breaks and inflation gap persistence. Through a \\textit{real-time} out-of-sample forecasting exercise we show that our model specification generally provides superior one-quarter and one-year ahead forecasts for quarterly inflation relative to a whole range of forecasting models that are typically used in the literature

Parsimonious estimation with many instruments

We suggest a way to perform parsimonious instrumental variables estimation in the presence of many, and potentially weak, instruments. In contrast to standard methods, our approach yields consistent estimates when the set of instrumental variables complies with a factor structure. In this sense, our method is equivalent to instrumental variables estimation that is based on principal components. However, even if the factor structure is weak or nonexistent, our method, unlike the principal components approach, still yields consistent estimates. Indeed, simulations indicate that our approach always dominates standard instrumental variables estimation, regardless of whether the factor relationship underlying the set of instruments is strong, weak, or absent