Comovements among U.S. state housing prices: Evidence from fractional cointegration

This study investigates the relationship between U.S. state housing prices and overall U.S. housing prices as well as the relationship among state housing prices using fractional integration and cointegration techniques. The results based on parametric and semiparametric estimators reveal that some states contain unit roots though we fail to find cointegrating relations between U.S. states housing prices and the overall U.S. housing prices as well as among state housing prices. The results raise doubts regarding the long-run convergence in U.S. state housing prices and the presence of the ripple effect.     

What drives housing price dynamics in Greece: New evidence from asymmetric ARDL cointegration

In this paper, we study the dynamics between house prices and selected macroeconomic fundamentals in Greece. The empirical analysis applies the asymmetric ARDL cointegration methodology proposed by Shin, Yu and Greenwood-Nimmo (2011) over the period from January 1999 to May 2011. The evidence suggests that ignoring the intrinsic nonlinearities may lead to misleading inference. In particular, the results reveal significant differences in the response of house prices to positive or negative changes of the explanatory variables in both the long- and short-run time horizons. The obtained evidence of asymmetry could be of major importance for more efficient policymaking and forecasting in the Greek house market.     

The impact of information signals on market prices when agents have non-linear trading rules

Several methods have been developed for filtering seasonal influences and extreme returns in financial and economic time series. The theoretical support for these approaches is rather questionable since it focuses on the effects of shocks on prices and not on their sources. Removing such effects modifies the true generating system of market dynamics because of the non-proportional character of non-linearity. Thus, taking into account that the underlying process of economic time series is highly non-linear we cannot be certain a priori what the impact of new information will be on the dynamic structure of a system. The main contribution of this paper is to demonstrate using the methodology of simulations the eventual distortions in time series data arising from the arrival of news when agents follow non-linear trading strategies. We argue that if news can really modify the dynamical behaviour of a system, then the methodology of filtering exogenous distortions needs to be re-examined.     

An in-sample and out-of-sample empirical investigation of the nonlinearity in house prices of South Africa

This paper tests whether housing prices in the five segments of the South African housing market, namely large-middle, medium-middle, small-middle, luxury and affordable, exhibit non-linearity based on smooth transition autoregressive (STAR) models estimated using quarterly data from 1970:Q2 to 2009:Q3. Findings point to an overwhelming evidence of non-linearity in these five segments based on in-sample evaluation of the linear and non-linear models. We next provide further support for non-linearity by comparing one- to four-quarters-ahead out-of-sample forecasts of the non-linear time series model with those of the classical and Bayesian versions of the linear autoregressive (AR) models for each of these segments, for the out-of-sample horizon 2001:Q1 to 2009:Q3, using the in-sample period 1970:Q2 to 2000:Q4. Our results indicate that barring the one-, two and four-step(s)-ahead forecasts of the small segment, the non-linear model always outperforms the linear models. In addition, given the existence of strong causal relationship amongst the house prices of the five segments, the multivariate versions of the linear (classical and Bayesian) and STAR (MSTAR) models were also estimated. The MSTAR always outperformed the best performing univariate and multivariate linear models. Thus, our results highlight the importance of accounting for non-linearity, as well as the possible interrelationship amongst the variables under consideration, especially for forecasting.     

Anatomy of Russia’s market segmentation1

Based on a relationship between price difference and demand difference among locations, the role of various market frictions in causing segmentation of the Russian goods market is analysed. The spatial sample covers most of Russian regions (70 of the 89); the data are annual, spanning 1992 to 2000. Spatial disconnectedness of regions is found to be responsible for about 70 percent of the average price differential, while the rest is caused by ‘artificial’ impediments to market integration such as regional protectionism, organized crime and intra‐region shipping conditions.     

Global influences on UK manufacturing prices: 1970-2000

We introduce the first consistent series of domestic-product and related import price indices at the industry level for the UK, using the data to analyse both domestic and international determinants of UK manufactured product prices. Foreign influences on UK prices in domestic markets are always present, but domestic cost movements dominate. We show that the pass-through of world-price, tariff and exchange rate changes into product prices is partial in general and varies markedly between product categories. Standard tariff and exchange rate theories overstate price responses to global pricing determinants and fail to allow for variation between industrial sectors. Such theories can mislead when used for policy analysis and prediction.     

The dependence structure between the Canadian stock market and the USD/CAD exchange rate: a copula approach

Abstract This paper investigates the dependence structure between the real Canadian stock returns and the real USD/CAD exchange rate returns, using the Symmetrized Joe‐Clayton (SJC) copula function. We estimate the SJC copula with monthly data over the period 1995:1 to 2006:12. Our results show significant asymmetric static and dynamic tail dependence between the real stock returns and the real exchange rate returns, such that the two returns are more dependent in the left than in the right tail of their joint distribution. We explain this asymmetric dependence in terms of an asymmetric interest rate policy by Canadian monetary authorities in response to changes in the real exchange rate during sub‐periods of falling and rising commodity prices.     

Volatility spill-overs in commodity spot prices: New empirical results

The classical rational expectations model of commodity markets implies that expected spot price risk is an explanatory variable in spot price regressions; and also that inventory carryover, which is reduced by a larger price variance, creates autoregressive conditional heteroscedastic processes in spot prices. In order to falsify/verify this theory, it has typically been assumed that the square root of the conditional variance of spot prices, a proxy for spot price risk, enters the conditional mean function of spot prices. Based on this simple representation, a typical but counter intuitive outcome has been that spot price risk has an insignificant impact on spot prices, see, e.g., Beck (Beck, S., 1993. A Rational Expectations Model of Time Varying Risk Premia in Commodities Futures Markets: Theory and Evidence. International Economic Review 34, 149–168, Beck, S., 2001. Autoregressive Conditional Heteroskedasticity in Commodity Spot Prices. Journal of Applied Econometrics 16, 115–132). In this paper, we propose an alternative functional relationship (from GARCH(1,1) to GARCH(1,1)-AR(m)) between spot price risk and spot prices that is fully supported by the classical rational expectations model, and based on this new representation we are able to provide stronger empirical support for Muth's rational expectation theory.     

A reexamination of trends in primary commodity prices

This paper conducts a reexamination of the Prebisch-Singer hypothesis by employing the unit root test proposed by Lee and Strazicich (2003) and Lee and Strazicich (2004) that allow for up to two structural breaks. Given the higher power of these tests compared to the Zivot and Andrews (1992) and Lumsdaine and Papell (1997) tests, rejection of the null can be considered as genuine evidence of stationarity. The main findings of this paper are that eleven out of twenty-four commodity prices are found to be difference stationary implying that shocks to these commodities tend to be permanent in nature. The remaining thirteen prices are found to exhibit trend stationary behavior with either one or two structural breaks. Most of the commodities that do not exhibit difference stationary behavior seem to contain no significant trends. There are fewer cases, in relation to past studies, of commodities that display negative trends thereby weakening the case for the Prebisch-Singer hypothesis.     

Spatial random utility model with an application to recreation demand

The paper examines the relevance of unobserved spatial dependence between individual decision-makers in the analysis of discrete choices. To incorporate spatial interdependencies in the behavioral analysis, we propose a spatial random utility model of recreation demand. The model combines the set-theoretic concept of spatial neighborhood, endogeneity of spatial interdependencies, and unobservable individual preferences. Our estimation procedure uses an efficient pseudo maximum likelihood estimator. We apply the model to the study of recreational travel demand in Iowa and evaluate the significance of unobserved spatial interdependencies between individual households in recreational travel choices.     

The effect of monetary policy on real house price growth in South Africa: A factor-augmented vector autoregression (FAVAR) approach

This paper assesses the impact of monetary policy on real house price growth in South Africa using a factor-augmented vector autoregression (FAVAR), estimated using a large data set comprising of 246 quarterly series over the period 1980:01 to 2006:04. The results based on the impulse response functions indicate that, in general, house price inflation responds negatively to monetary policy shock, but the responses are heterogeneous across the middle-, luxury- and affordable-segments of the housing market. The luxury-, large-middle- and medium-middle-segments are found to respond much more than the small-middle- and the affordable-segments of the housing market. More importantly, we find no evidence of the home price puzzle, observed previously by other studies that analyzed house prices using small-scale models. We put this down to the benefit gained from using a large information set.     

Evaluating growth cycle synchronisation in the EU

This paper provides an insight into the level of economic and monetary integration in Europe by analysing the degree of growth cycle synchronisation between seven European countries over the past thirty years. Two univariate trend-cycle decomposition methodologies, the Beveridge–Nelson (BN) decomposition and Harvey and Trimbur [Harvey, A.C. & Trimbur, T.M. (2003) “General Model-Based Filters for Extracting Cycles and Trends in Economic Time Series”, The Review of Economics and Statistics, 85(2), 244–255.]'s unobserved component model, together with a multivariate extension of the BN decomposition incorporating trend and cycle restrictions, are used to identify the trend and cyclical components from real GDP for each of the seven countries. The cycles extracted from the two univariate approaches vary significantly in both cycle period and amplitude. The average correlation calculated from the BN cycles are also smaller than the corresponding correlation estimated using cycles extracted from the unobserved component model. This confirms the argument in Canova [Canova, F. (1998) “Detrending and Business Cycle Facts”, Journal of Monetary Economics, 41(3), 475–512.] that the use of different trend-cycle decomposition methodologies may influence the results obtained. The results produced from the multivariate model indicate the presence of common features in the data. This may reflect the coordinated and common monetary and fiscal policies that these countries have shared over the sample period. However, the finding of codependent and heterogeneous growth cycles raises concerns about the operation of the European Monetary Union (EMU), as it implies that members may face significant stabilisation costs.     

On the prevalence of trends in primary commodity prices

This paper applies new time series procedures to examine the Prebisch–Singer hypothesis of a secular deterioration in relative primary commodity prices. Specifically, we allow for (up to) two structural breaks in 24 price series, covering the 1900–98 period. For the majority of commodities, it is shown that the trend is not well represented by a single downward slope, but instead by a shifting trend that often changes sign over the sample period. Unlike some recent work that has also allowed for structural breaks, these results provide much less support for the Prebisch–Singer hypothesis.     

Exchange rate regime verification: An alternative method of testing for regime changes

This work proposes a change in persistence test for identifying de facto exchange rate regime changes. The results from 25 African countries show that this approach is able to identify some regime changes not captured by existing methods.     

Forecasting the US real house price index: Structural and non-structural models with and without fundamentals

We employ a 10-variable dynamic structural general equilibrium model to forecast the US real house price index as well as its downturn in 2006:Q2. We also examine various Bayesian and classical time-series models in our forecasting exercise to compare to the dynamic stochastic general equilibrium model, estimated using Bayesian methods. In addition to standard vector-autoregressive and Bayesian vector autoregressive models, we also include the information content of either 10 or 120 quarterly series in some models to capture the influence of fundamentals. We consider two approaches for including information from large data sets — extracting common factors (principle components) in factor-augmented vector autoregressive or Bayesian factor-augmented vector autoregressive models as well as Bayesian shrinkage in a large-scale Bayesian vector autoregressive model. We compare the out-of-sample forecast performance of the alternative models, using the average root mean squared error for the forecasts. We find that the small-scale Bayesian-shrinkage model (10 variables) outperforms the other models, including the large-scale Bayesian-shrinkage model (120 variables). In addition, when we use simple average forecast combinations, the combination forecast using the 10 best atheoretical models produces the minimum RMSEs compared to each of the individual models, followed closely by the combination forecast using the 10 atheoretical models and the DSGE model. Finally, we use each model to forecast the downturn point in 2006:Q2, using the estimated model through 2005:Q2. Only the dynamic stochastic general equilibrium model actually forecasts a downturn with any accuracy, suggesting that forward-looking microfounded dynamic stochastic general equilibrium models of the housing market may prove crucial in forecasting turning points.     

Illustrating extraordinary shocks causing trend breaks

Structural breaks in a trending variable have been specified as changes in the drift parameter in the trend component, but extraordinary shocks causing these breaks have not been explicitly formulated. In this paper, the Hodrick–Prescott filter is extended by assuming two kinds of variance for the system noise driving the trend component: the larger one adopted in a point of time causing a trend break, and the smaller one adopted for remaining sequences. The number and location of structural breaks are determined by information criteria. In the proposed method, extraordinary shocks themselves can be illustrated. A Monte Carlo study shows the efficacy of the proposed model. Empirical results suggest that except for the UK, extraordinary shocks in quarterly time series of industrial production are detected for remaining six developed countries. Finally, it is shown that the proposed method considerably outperforms the other competing methods in correctly detecting business cycles.     

Does seasonality change over the business cycle? An investigation using monthly industrial production series

This paper examines the proposition that the business cycle affects seasonality in industrial production, with output being switched to the traditionally low production summer months when recent (annual) growth has been strong. This is investigated through the use of a restricted threshold autoregressive model for the monthly growth rate in a total of 74 industries in 16 OECD countries. Approximately one-third of the series exhibit significant nonlinearity, with this nonlinearity predominantly associated with changes in the seasonal pattern. Estimates show that the summer slowdown in many European countries is substantially reduced in the regime of higher recent growth.     

Nowcasting causality in mixed frequency vector autoregressive models

This paper introduces nowcasting causality as the mixed-frequency version of instantaneous causality. We analyze the relationship between nowcasting and Granger causality in a mixed-frequency VAR and illustrate its impact on the significance of high-frequency variables in mixed-frequency conditional models.     

Econometric applications of positive rank-one modifications of the symmetric factorization of a positive semi-definite matrix

We present an algorithm for updating the symmetric factorization of a positive semi-definite matrix after a positive rank-one modification, which works even if the matrices involved do not have full rank. Recursive least squares and factor analysis provide two important econometric applications. An illustrative simulation shows that it can be potentially very useful in recursive situations.