Estimation of hedonic price functions via additive nonparametric regression

We model a hedonic price function for housing as an additive nonparametric regression. Estimation is done via a backfitting procedure in combination with a local polynomial estimator. It avoids the pitfalls of an unrestricted nonparametric estimator, such as slow convergence rates and the curse of dimensionality. Bandwidths are chosen using a novel plug in method that minimizes the asymptotic mean average squared error (AMASE) of the regression. We compare our results to alternative parametric models and find evidence of the superiority of our nonparametric model. From an empirical perspective our study is interesting in that the effects on housing prices of a series of environmental characteristics are modeled in the regression. We find these characteristics to be important in the determination of housing prices.First version received: October 2002/Final version received: October 2003We thank B. Baltagi and two anonymous referees for their comments. The authors retain responsibility for any remaining errors.     

Forecasting UK consumer price inflation using inflation forecasts

The inflation rate is a key economic indicator for which forecasters are constantly seeking to improve the accuracy of predictions, so as to enable better macroeconomic decision making. Presented in this paper is a novel approach which seeks to exploit auxiliary information contained within inflation forecasts for developing a new and improved forecast for inflation by modeling with Multivariate Singular Spectrum Analysis (MSSA). Unlike other forecast combination techniques, the key feature of the proposed approach is its use of forecasts, i.e. data into the future, within the modeling process and extracting auxiliary information for generating a new and improved forecast. We consider real data on consumer price inflation in UK, obtained via the Office for National Statistics. A variety of parametric and nonparametric models are then used to generate univariate forecasts of inflation. Thereafter, the best univariate forecast is considered as auxiliary information within the MSSA model alongside historical data for UK consumer price inflation, and a new multivariate forecast is generated. We find compelling evidence which shows the benefits of the proposed approach at generating more accurate medium to long term inflation forecasts for UK in relation to the competing models. Finally, through the discussion, we also consider Google Trends forecasts for inflation within the proposed framework.     

Did Baltic stock markets offer diversification benefits during the recent financial turmoil? Novel evidence from a nonparametric causality-in-quantiles test

Motivated by financial liberalization investors seek for new investment opportunities through international portfolio diversification. To this end we explore any asymmetric causal relationship between developed European stock markets (Germany, France and UK) and emerging Baltic markets namely; Estonia, Latvia and Lithuania. Our analysis focuses on the period before and after countries’ EU accession and pre- and post the global financial crisis. For this purpose, both the standard parametric test for causality and a novel nonparametric test for causality-in-quantiles are employed. The results of both the parametric and nonparametric Granger causality test support a causal relationship in mean that runs from all of the major markets to the Baltic markets across both samples. The results imply the existence of significant nonlinear return and volatility spillover from European markets to Baltic markets. Policy implications for international investors are also discussed.     

Alcohol availability and crime: a robust approach

The relationship between alcohol availability and crime is investigated in this study. It first considers common parametric specifications that have been used in the literature. After applying a powerful consistent conditional moment test for correct specification, it is found that these common parametric specifications are rejected by the data. The study then proceeds with a robust nonparametric method that can have a rate of convergence close to that for a correctly specified parametric model when the underlying relationship is somewhat linear. The application of nonparametric methods reveals structure present in the data that would remain undetected when applying common parametric specifications, but more importantly reveals that the impact of alcohol availability is considerably higher than one might believe on the basis of the misspecified parametric model. It is also found that the marginal effect of alcohol availability on crime changes with the level of alcohol availability.     

Average of forecasts: an improvement

Stein-rules are used in forming combination forecasts. Such forecasts have smaller prediction risk, under certain conditions, than the preferred method of average of forecasts. The new composite forecasts will be theoretically biased, but are likely to have smaller mean squared error of forecasts as shown in the empirical exercises examined. When the forecasts appear to be unbiased and have a common mean, Lindley-type combination is most useful.     

Measuring and testing the long-term impact of terrorist attacks on the US futures market

This study compares the accuracy and information content of economic forecasts for G7 countries made in the 1990s by the OECD and IMF. The benchmarks for comparison are the average forecasts of private sector economists published by Consensus Economics. With few exceptions, the private sector forecasts are less biased and more accurate in terms of mean absolute error and root mean square error. Formal tests show these differences are statistically significant for forecasts of real growth and production, less so for forecasts of inflation and unemployment. Overall, there appears little information in the OECD and IMF forecasts that could be used to reduce significantly the error in the private sector forecasts.     

The Accuracy of OECD Forecasts for Japan

A variety of accuracy measures, error diagnostics and rationality tests are applied to the OECD's macroeconomic forecasts for Japan of aggregate demand and output, inflation and the balance of payments. It is found that the OECD forecasts are superior to naive no-change predictions and forecasts generated by simple autoregressive time-series models. Most forecasting error is nonsystematic. As predictors of direction the OECD's six-month ahead forecasts should be considered valuable; this cannot be said for forecasts which look ahead a year and 18 months. Many forecasts fail bias, efficiency and consistency tests so that the rational expectations hypothesis is not generally supported.     

Nonparametric regression estimation with general parametric error covariance: a more efficient two-step estimator

Recently Martins-Filho and Yao (J Multivar Anal 100:309–333, 2009) have proposed a two-step estimator of nonparametric regression function with parametric error covariance and demonstrate that it is more efficient than the usual LLE. In the present paper we demonstrate that MY’s estimator can be further improved. First, we extend MY’s estimator to the multivariate case, and also establish the asymptotic theorem for the slope estimators; second, we propose a more efficient two-step estimator for nonparametric regression function with general parametric error covariance, and develop the corresponding asymptotic theorems. Monte Carlo study shows the relative efficiency loss of MY’s estimator in comparison with our estimator in nonparametric regression with either AR(2) errors or heteroskedastic errors. Finally, in an empirical study we apply the proposed estimator to estimate the public capital productivity to illustrate its performance in a real data setting.     

Efficiency in banking: a meta-regression analysis

One learns two main lessons from studying the great quantity of banking efficiency literature. These lessons regard the heterogeneity in results and the absence of a comprehensive review aimed at understanding the reasons for this variability. Surprisingly, although this issue is well-known, it has not been systematically analyzed before. In order to fill this gap, we perform a Meta-Regression-Analysis (MRA) by examining 1661 efficiency scores retrieved from 120 papers published over the period 2000–2014. The meta-regression is estimated by using the Random Effects Multilevel Model (REML) because it controls for within- and between-study heterogeneity. The analysis yields four main results. First, parametric methods yield lower levels of banking efficiency than nonparametric studies. This holds true even after controlling for the approach used in selecting the inputs and outputs of the frontier. Secondly, we show that banking efficiency is highest when using the value-added approach, followed by estimates from studies based on the intermediation method, whereas those based on the hybrid approach are the lowest. Thirdly, efficiency scores are also determined by the quality of studies and the number of observations and variables used in the primary papers. As far as the effects of sample size, dimension and quality of papers are concerned, there are significant differences in sign and magnitude between parametric and nonparametric studies. Finally, cost efficiency is found to be higher than profit efficiency. Interestingly, MRA results are robust to the potential outliers in efficiency and sample size distributions.     

Forecast combination when outcomes are difficult to predict

We show that when outcomes are difficult to forecast in the sense that forecast errors have a large common component that (a) optimal weights are not affected by this common component, and may well be far from equal to each other but (b) the relative mean square error loss from averaging over optimal combination can be small. Hence, researchers could well estimate combining weights that indicate that correlations could be exploited for better forecasts only to find that the difference in terms of loss is negligible. The results then provide an additional explanation for the commonly encountered practical situation of the averaging of forecasts being difficult to improve upon.     

NONPARAMETRIC LIKELIHOOD: EFFICIENCY AND ROBUSTNESS*

Nonparametric likelihood is a natural generalization of parametric likelihood and it offers effective methods for analysing economic models with nonparametric components. This is of great interest, since econometric theory rarely suggests a parametric form of the probability law of data. Being a nonparametric method, nonparametric likelihood is robust to misspecification. At the same time, it often achieves good properties that are analogous to those of parametric likelihood. This paper explores various applications of nonparametric likelihood, with some emphasis on the analysis of biased samples and data combination problems.     

Inequality in democracies: Testing the classic democratic theory of redistribution

The classic democratic theory of redistribution claims that an increase in the mean-to-median (MM) income ratio causes a majority coalition in the electorate to collectively demand more redistribution. The functional dependence of redistribution on the MM income ratio is tested in parametric and nonparametric regression frameworks using an OECD panel dataset. While the parametric regression model is found to be misspecified rendering subsequent inference invalid, the robust nonparametric regression model fails to uncover evidence that the MM income ratio is relevant for predicting redistribution.     

Fractional Integration of Nominal Exchange Rates: Evidence from CEECs in the Light of EMU Enlargement

This paper uses fractional integration models to describe the long‐run dependence of nominal exchange rates in Central and Eastern European countries (CEECs). The analysis is validated using nonparametric, semiparametric and parametric techniques. From comparing the results across the three approaches, it was clear that mean reversion takes places only for the euro exchange rates in Bulgaria, Estonia, and Slovenia. Other exchange rates based on the euro also display mean reversion with the parametric methods. For the US dollar rates, the unit‐root null hypothesis cannot be rejected in any single country, indicating that shocks affecting the exchange rates against the US dollar are of a permanent nature, while those directed against the euro are less persistent, and tend sometimes to disappear in the long run. Policy implications are derived.     

Commodity prices and related equity prices

This paper shows that commodity‐sensitive stock price indices have strong power in predicting nominal and real commodity prices at short horizons (one‐month‐ahead predictions) using both in‐ and out‐of‐sample tests. The forecasts based on commodity‐sensitive stock price indices are able to significantly outperform naïve no‐change forecasts. For example, the one‐month‐ahead forecasts for nominal commodity prices reduce the mean squared prediction error by between 1.5% (for natural gas prices) and 20% (for copper prices). Moreover, the one‐month‐ahead directional forecast is found to perform significantly better than a 50:50 coin toss. As stock prices are not subject to revision, the proposed variable, which reflects timely and readily available market information, can potentially be a valuable predictor and thereby help to improve the accuracy of commodity price forecasts.     

An empirical comparison of nonparametric and parametric Engel functions

Nonparametric techniques are used to estimate income densities, Engel functions and their derivatives, and income elasticities. Nonparametric kernel methods give less ambiguous estimates of the densities than discrete, maximum penalised likelihood estimation. The nonparametric estimates of the Engel functions, their derivatives, and the income elasticities are compared with some corresponding parametric estimates. The nonparametrically estimated Engel functions provide a better fit to the data. Also, the nonparametric elasticity estimates are qualitatively similar to the parametric estimates. Some statistically significant differences are observed between the parametric versus nonparametric estimates, but not to the extent of having any major policy implications.     

Forecasting the price of gold

This article seeks to evaluate the appropriateness of a variety of existing forecasting techniques (17 methods) at providing accurate and statistically significant forecasts for gold price. We report the results from the nine most competitive techniques. Special consideration is given to the ability of these techniques to provide forecasts which outperforms the random walk (RW) as we noticed that certain multivariate models (which included prices of silver, platinum, palladium and rhodium, besides gold) were also unable to outperform the RW in this case. Interestingly, the results show that none of the forecasting techniques are able to outperform the RW at horizons of 1 and 9 steps ahead, and on average, the exponential smoothing model is seen providing the best forecasts in terms of the lowest root mean squared error over the 24-month forecasting horizons. Moreover, we find that the univariate models used in this article are able to outperform the Bayesian autoregression and Bayesian vector autoregressive models, with exponential smoothing reporting statistically significant results in comparison with the former models, and classical autoregressive and the vector autoregressive models in most cases.     

Real-time data revisions and the PCE measure of inflation

This paper tracks data revisions in the Personal Consumption Expenditure using the exclusions-from-core inflation persistence model. Keeping the number of observations the same, the regression parameters of earlier vintages of real-time data, beginning with vintage 1996:Q1, are tested for coincidence against the regression parameters of the last vintage of real-time data, used in this paper, which is vintage 2008:Q2 in a parametric and two nonparametric frameworks. The effects of data revisions are not detectable in the vast majority of cases in the parametric model, but the flexibility of the two nonparametric models is able to utilize the data revisions.     

Recent approaches to estimating Engel curves

Classical approaches of estimating cross-section Engel curves are based on parametric models. However, misspecification of a parametric model implies that information of structural nature might be masked. An alternative avoiding problems related to predetermined functional relations is the nonparametric approach. This paper surveys recent advances of nonparametric statistics in their relevance to estimating cross-section Engel curves.     

Does inflation cause gold market price changes? evidence on the G7 countries from the tests of nonparametric quantile causality in mean and variance

This article utilizes the newly proposed nonparametric causality-in-quantiles test to examine the predictability of mean and variance of changes in gold prices based on inflation for G7 countries. The causality-in-quantiles approach permits us to test for not only causality in mean but also causality in variance. We start our investigation by utilizing tests for nonlinearity. These tests identify nonlinearity, showing that the linear Granger causality tests are subject to misspecification error. Unlike tests of misspecified linear models, our nonparametric causality-in-quantiles tests find causality in mean and variance from inflation to gold market price changes between the 0.20 quantile and the 0.70 quantile, implying that very low- and high-price changes in gold markets are not related to inflation. These changes should be related to other sources, such as financial shocks and exchange market shocks. We find support that gold serves as a hedge against inflation, but only in the mid-quantile ranges, i.e. quantiles from 0.20 to 0.70. Our results show that gold does not serve as a hedge against inflation during periods when gold market price changes are very low or very high, which are respectively quiet and highly volatile periods.     

Bootstrapping covariate stationarity tests for inflation rates

This paper investigates the stationarity properties of international inflation rates by bootstrapping two stationarity tests with covariates in Jansson (2004). When the asymptotic critical values are used, the two powerful tests are found to reject the null hypothesis less in the presence of a large negative moving-average (MA) error in inflation. To cope with this problem, a parametric bootstrap scheme is developed and then is investigated by a Monte Carlo study. The simulation results demonstrate that the bootstrap tests display a better control over the empirical rejection rates at finite samples. Furthermore, after applying these tests to the inflation in G-10 countries, we find that one of the two tests using bootstrap critical values yields inferences that differ from when using asymptotic ones, and as a whole, the bootstrap tests consistently provide strong evidence in support of mean reversion in inflation in most countries of the G-10.