Forecasting the UK economy with a medium-scale Bayesian VAR

We estimate a Bayesian VAR (BVAR) for the UK economy and assess its performance in forecasting GDP growth and CPI inflation in real time relative to forecasts from COMPASS, the Bank of England’s DSGE model, and other benchmarks. We find that the BVAR outperformed COMPASS when forecasting both GDP and its expenditure components. In contrast, their performances when forecasting CPI were similar. We also find that the BVAR density forecasts outperformed those of COMPASS, despite under-predicting inflation at most forecast horizons. Both models over-predicted GDP growth at all forecast horizons, but the issue was less pronounced in the BVAR. The BVAR’s point and density forecast performances are also comparable to those of a Bank of England in-house statistical suite for both GDP and CPI inflation, as well as to the official Inflation Report projections. Our results are broadly consistent with the findings of similar studies for other advanced economies.     

PITFALLS IN CROSS‐SECTION STUDIES WITH INTEGRATED REGRESSORS: A SURVEY AND NEW DEVELOPMENTS

In cross‐section studies, if the dependent variable is I(0) but the regressor is I(1), the true slope must be zero in the resulting “unbalanced regression.” A spuriously significant relationship may be found in large cross‐sections, however, if the integrated regressor is related to a stationary variable that enters the DGP but is omitted from the regression. The solution is to search for the related stationary variable, in some cases the first difference of the integrated regressor, in other cases, a categorical variable that can take on limited number of values which depend on the integrated variable. We present an extensive survey, new developments, and applications particularly in finance.     

Predictive regressions under asymmetric loss: Factor augmentation and model selection

This paper discusses the specifics of forecasting using factor-augmented predictive regressions under general loss functions. In line with the literature, we employ principal component analysis to extract factors from the set of predictors. In addition, we also extract information on the volatility of the series to be predicted, since the volatility is forecast-relevant under non-quadratic loss functions. We ensure asymptotic unbiasedness of the forecasts under the relevant loss by estimating the predictive regression through the minimization of the in-sample average loss. Finally, we select the most promising predictors for the series to be forecast by employing an information criterion that is tailored to the relevant loss. Using a large monthly data set for the US economy, we assess the proposed adjustments in a pseudo out-of-sample forecasting exercise for various variables. As expected, the use of estimation under the relevant loss is found to be effective. Using an additional volatility proxy as the predictor and conducting model selection that is tailored to the relevant loss function enhances the forecast performance significantly.     

A Note on adding-up restrictions when modelling trade flows

An ad hoc rule is often required when modelling trade in multi-country models to ensure that aggregate imports equal aggregate exports in volumes and values. This paper offers an approach that offers some advantages over current methods.     

Economic design of one-sided screening procedures based on a correlated variable with all parameters unknown

Summary Economic design of one-sided screening procedures for improving outgoing product quality based on a screening variable is considered for the case with all parameters unknown. It is assumed that the performance and screening variables are jointly normally distributed and that costs are incurred by screening inspection, acceptance of an imperfect item, and disposition of a rejected one. A method for finding optimal cutoff value based on the predictive distribution is presented.     

CREDIBILITY, MACROECONOMIC FUNDAMENTALS AND MARKOV REGIME SWITCHES IN THE EMS

The main objective of this paper is to use the Markov regime‐switching modelling framework to describe and analyse the credibility of a number of countries participating in the European Monetary System during 1980–1998. Our credibility indicator, based on Hughes Hallet et al.'s (1997) methodology, is subject to discrete regime shifts and is made dependent on macroeconomic fundamentals. We carry out extensive testing to assess the specification of the Markov regime‐switching model and the potential existence of permanent breaks. A contribution of our paper is the specification of a multivariate Markov switching model that allows us to examine whether macroeconomic variables have asymmetric effects on credibility. Another contribution is the specification of a regime‐switching model with time‐varying transition probabilities, which enables us to determine whether changes in macroeconomic variables can trigger switches between the low and high credibility regimes. We find strong evidence of regime switching behaviour in all countries. Both the level of credibility and the transition probabilities display an asymmetric response to changes in macroeconomic variables, with the stance of fiscal policy exerting the most systematic influence in all countries.     

Modelling the inflation process in transition economies: Empirical comparison of Poland, Hungary and Czech Republic

This paper starts from the observation that inflation in transition economies appears to be persistently high and volatile and attempts to provide some empirical characterisation of the inflation process in three such transition economies: Poland, Hungary and Czech Republic. We first consider the role of monetary growth as a major causal factor for inflation in these economies, and argue that the evidence provides rather weak support for the causal relationship. We then propose a transition economy cost-plus model and estimate this using the equilibrium-correction modelling (ECM) strategy augmented by introduction of a number of transitory factors and changes in the internal structure of the real economy which we believe may have had a significant impact on inflation in these economies. We show that this approach enables us to account for long-run inflation in these economies from the early 1980s to the present despite the turbulence of the latter part of the sample period. Our results support wage and exchange rate based inflation policies.     

Use of Statistics in the Monetary Policy of the Czech National Bank: The Case of a Country in Transition

In the presented text the authors judge the importance of statistics in the monetary policy of the Czech National Bank (CNB) over the course of the economic transformation process, with particular consideration of changing statistical needs and the possibilities and limits of statistical data exploitation in the monetary analyses. The importance of statistics lies on the level of collection and processing of statistical information and on the level of use of statistical methods to analyse data. Since the start of the 1990s the requirements for statistics were significantly influenced by monetary policy. In the period 1990–1997, monetary targeting was the primary influential factor. Since 1998, the monetary policy is influenced by inflation targeting. Statistical priorities switched from monetary data to economy and financial market data. Much progress has been made in the use of statistical methods for analysing data. Statistics available at present cover the CNB's standard monetary-policy requirements and are on par with those in developed countries. Its further development will reflect the standard changes taking place in the more advanced countries.     

韩国企业对中国直接投资的实情分析和决定因素

文章通过对韩国企业对中国直接投资的实情分析,考察不同规模企业的特征,并依 据投资国的国内因素分析了对中国直接投资的决定因素。     

Deregulation,news releases,and price discovery

We hypothesize that the firm’s regulatory environment influences the sensitivity of its equity value to information. Using intraday stock price data of utilities operating in differing regulatory environments we test for systematic differences between the responsiveness of stock prices of utility firms operating in deregulated and regulated environments to a common information set. Our findings reveal sharp differences in responses, with those of utilities operating in deregulated environments the strongest, and the responses of utilities in highly regulated environments the weakest. While the evidence supports our hypothesis, in a broader sense, the evidence suggests that deregulation aids in the process of price discovery. We also find evidence that suggests that deregulation, per se, does not lead to higher stock price volatility.