Forecasting China's inflation in a data-rich environment

Inflation is one of the most recent critical issues facing China. To improve inflation forecasts within China, this study investigates the predictive ability of three dimension reduction techniques used in a data-rich environment: Principal Components Analysis (PCA), Sliced Inverse Regression (SIR), and Partial Least Squares (PLS) applied in the Factor-Augmented Autoregression (FAAR) model proposed by Stock and Watson (2005). Varied macroeconomic data from China between January 1998 and December 2009 are obtained to construct factors for use by three different techniques. The performance of different dimension reduction methods depends on forecasting horizons, the number of factors chosen, and the number of slices for SIR. The empirical study finds that the FAAR model with an optimal number of PCA factors outperforms the other model in out-of-sample inflation forecasting in China.     

Forecasting UK house prices: A time varying coefficient approach

Previous studies of UK house prices, developed from the demand and supply ofhousing or from the asset market approach have been poor in terms of robustness and ex-post forecasting ability. The UK housing market has suffered a number of structural changes, particularly since the early 1980s with substantial house price increases, financial market deregulation and the removal of mortgage market constraints through competition. Consequently, models which assume that the underlying data-generating process is stable and apply constant parameter techniques tend to suffer in terms of parameter instability. This article uses the Time Varying Coefficient (TVC) methodology where the underlying data-generating process in the UK housing market is treated as unstable. The estimation results of the TVC regression of UK house prices is compared with those obtained from three alternative constant parameter regressions. Comparisons of forecasting performance suggest the TVC regression out-performs forecasts from an Error Correction Mechanism (ECM), Vector Autoregressive (VAR) and an Autoregressive Time Series regression.     

Forecasting the aggregate stock market volatility in a data-rich world

ABSTRACT

In this article, we utilize the basic lasso and elastic net models to revisit the predictive performance of aggregate stock market volatility in a data-rich world. Motivated by the existing literature, we determine several candidate predictors that have 22 technical indicators and 14 macroeconomic and financial variables. Our out-of-sample results reveal several noteworthy findings. First, few macroeconomic and financial variables and most of technical indicators have superior performance relative to the benchmark model. Second, combination forecasts are able to significantly beat the benchmark and some signal predictors Third, the lasso and elastic models with all predictors can generate more accurate forecasts than the benchmark and some other predictors in both the statistical and economic sense. Fourth, the lasso and elastic models exhibit higher forecast accuracy during periods of expansions and recessions. Finally, our findings are robust to several tests, such as different forecasting windows, forecasting models, and forecasting evaluations.     

Housing prices,stock prices and the US economy

This paper studies the relationship between housing prices, stock prices, interest rates and aggregate output in the US using monthly data from 1993 to 2014. Evidence from causality tests and a variance decomposition procedure suggest that stock prices have a much larger effect on aggregate output in the US economy than do either housing prices or interest rates. Instead, the wealth effect created by changes in stock prices has a relatively large impact on US aggregate output. Separate estimations and variance decompositions for the sample periods 1993–2001, 2002–2008 and 2009–2014 show that the impact of housing prices relative to stock prices has been waning over time.     

Forecasting the Australian economy with DSGE and BVAR models

Reflecting the importance of commodities for the Australian economy, we construct a dynamic stochastic general equilibrium (DSGE) model of the Australian economy with a commodity sector. We assess whether its forecasts can be improved by using it as a prior for an empirical Bayesian vector autoregression (BVAR). We find that the forecasts from the BVAR tend to be more accurate than those from the DSGE model. Nevertheless, for output growth these forecasts do not outperform benchmark models, such as a small open economy BVAR estimated using the standard priors for forecasting. A Bayesian factor augmented vector autoregression produces the most accurate near-term inflation forecasts.     

Convergence and spillover of house prices in Chinese cities

The issue of house price convergence in 34 Chinese cities is investigated. We augmented the convergence model with contemporaneous spatial dependence in house prices and found that price convergence and positive spatial spillover are both present. We explicitly addressed the endogeneity problem by introducing a Bayesian instrumental variable setup, which was estimated with particle filtering techniques. From a growth poles perspective, the empirical evidence indicates that the spread effect in regional house prices outweighs the backwash effect. The identified positive spatial spillover has two effects on the growth of house prices in Chinese cities. First, the spillover elevates the trajectories of the steady-state growth paths of house prices. Second, the spillover narrows the gaps between the growth paths of house prices in neighbouring cities. Shocks to the socio-economic variables of a city generate their own effects on domestic house prices that dominate the effects arising from cross-city price feedbacks, thus mitigating the prospect of level convergence. Our findings also suggest a collaborating role between time and spatial dependence parameters. The identification of inter-city spillover, which is a conditioning factor for regional house price convergence, offers implications to policies that are most likely to be effective in reducing regional disparity.     

Detecting long‐run relationships in regional house prices in the UK

Recent developments in the analysis of cointegration in the presence of asymmetric adjustment are extended and applied to data on regional house prices in the UK. This extension is found to have a dramatic impact upon the results derived. In contrast to recent studies employing standard methods, allowance for the possibility of asymmetric behaviour results in the detection of a large number of long‐run relationships between house prices in different regions. A consistent pattern of asymmetric adjustment is observed, with reversion to equilibrium occurring more rapidly (slowly) when house prices in the South of England decrease (increase) relative to other regions. While the results derived support the existence of a ripple effect underlying the observed movements in regional house prices, the extent of cointegration uncovered casts doubt upon the recently proposed notion of weak segmentation in the UK housing market.     

A bottom-up approach for forecasting GDP in a data-rich environment

In an increasingly data-rich environment, the use of factor models for forecasting purposes has gained prominence in the literature and among practitioners. Herein, we assess the forecasting behaviour of factor models to predict several GDP components and investigate the performance of a bottom-up approach to forecast GDP growth in Portugal, which was one of the hardest hit economies during the latest economic and financial crisis. We find supporting evidence of the usefulness of factor models and noteworthy forecasting gains when conducting a bottom-approach drawing on the main aggregates of GDP.     

Commodity prices and the AUD-Yen exchange rate: a real-time forecasting analysis

I study the impact of the GSCI commodity price indices on the Australian dollar-Japanese yen nominal exchange rate using a modified version of the classic monetary approach of exchange rate determination. I use a broad range of model-selection and model-averaging criteria. I find some evidence for a short-lived relationship as far as inclusions in the optimal forecasting models are concerned. In general, though, results of the Diebold-Mariano and Clark-West test show that results are not stable over the whole sample.     

The out-of-sample forecasting performance of nonlinear models of regional housing prices in the US

This article provides out-of-sample forecasts of linear and nonlinear models of US and four Census subregions’ housing prices. The forecasts include the traditional point forecasts, but also include interval and density forecasts, of the housing price distributions. The nonlinear smooth-transition autoregressive model outperforms the linear autoregressive model in point forecasts at longer horizons, but the linear autoregressive and nonlinear smooth-transition autoregressive models perform equally at short horizons. In addition, we generally do not find major differences in performance for the interval and density forecasts between the linear and nonlinear models. Finally, in a dynamic 25-step ex-ante and interval forecasting design, we, once again, do not find major differences between the linear and nonlinear models. In sum, we conclude that when forecasting regional housing prices in the United States, generally the additional costs associated with nonlinear forecasts outweigh the benefits for forecasts only a few months into the future.     

A fractionally integrated model with a mean shift for the US and the UK real oil prices

In this article, we have modeled the log of the US and the UK real oil prices in terms of fractionally integrated processes with a mean shift. We used different versions of the tests of Robinson (1994), which have standard null and local limit distributions. The results indicated that if we model the series without a mean shift, then they are both non-stationary I(1). However, by including a mean shift component during the oil crises, they become fractionally integrated with an order of integration smaller than one and, thus, showing mean reverting behavior.     

A change in the time-varying correlation between oil prices and the stock market

We investigate the time-varying correlation between oil prices and stock prices. Estimation results from a multivariate DCC-GARCH model reveal that the correlation has changed since the financial crisis. Historically, the correlation has been close to zero or slightly negative. However, the correlation changed to positive during the Great Recession and continued to be positive through the first half of 2017. We investigate the role quantitative easing played in this change in correlation using a threshold model.     

Economic theory and econometric dynamics in modelling wages and prices in the United Kingdom

We show that a “competing claims” model of imperfect competition can explain the movements of wages and prices in the United Kingdom, using quarterly data covering 1976–93. We argue that careful attention both to economic theory and to the interaction between dynamics and identification is crucial in the building of the model and to dynamic econometric models in general. We use a small numerical example with simulated cointegrated data to illustrate the potential pitfalls. First version received: January 1998/final version received: November 1998     

Interactions between real economic and financial sides of the US economy in a regime-switching environment

This objective of this study is to examine the linkages between real (economic) and financial variables in the United States in a regime-switching environment that accounts explicitly for high volatility in the stock market and high stress in financial markets. Since the linearity test shows that the linear model should be rejected, we employ the Markov-switching VECM to examine the same objective using the Bayesian Markov-chain Monte Carlo method. The regime-dependent impulse response function (RDIRF) highlights the increasing importance of the financial sector of the economy during stress periods. The responses and their fluctuations are significantly greater in the high-volatility regime than in the low-volatility regime.     

Conditional correlations in the returns on oil companies stock prices and their determinants

The identification of the forces that drive stock returns and the dynamics of their associated volatilities is a major concern in empirical economics and finance. This analysis is extremely important for determining optimal hedging strategies. This paper investigates the stock prices’ returns and their financial risk factors for several integrated oil companies, namely Bp (BP), Chevron-Texaco (CVX), Eni (ENI), Exxon-Mobil (XOM), Royal Dutch (RD) and Total-Fina Elf (TFE). We measure the actual co-risk in stock returns and their determinants “within” and “between” the different oil companies, using multivariate cointegration techniques in modelling the conditional mean, as well as multivariate GARCH models for the conditional variances. The distinguishing features of this paper are: (i) focus on the determinants of the market value of each company using the cointegrated VAR/VECM methodology; (ii) specification of the conditional variances of VECM residuals with the Constant Conditional Correlation (CCC) multivariate GARCH model of Bollerslev [(1990) Review of Economics and Statistics 72:498–505] and the Dynamic Conditional Correlation (DCC) multivariate GARCH model of Engle [(2002) Journal of Business and Economic Statistics 20:339–350]; (iii) discussion of the performance of optimal hedge ratios calculated with the DCC estimates. The “within” and “between” DCC indicate time-varying interdependence between stock return volatilities and their determinants. Moreover, DCC models are shown to produce more accurate hedging strategies.     

An alternative method for identifying booms and busts in the Euro area housing market

This article develops a model-based method to detect booms and busts in the Euro area housing market. A model is constructed and tested, whereby the user cost rate, a demographic variable, unemployment rate, disposable income, debt-to-income ratio and housing stock are fundamental variables significantly explaining house price (HP) developments. Booms/busts are identified as episodes when the HP index exceeds the levels implied by those economic fundamentals. Furthermore, a cross-check with boom/bust episodes based on other methods is carried out to substantiate the results, while the ability of the model in predicting booms/busts in real time is also tested.     

基于GARCH族模型对中国股市波动的分析与预测

运用GARCH类模型对沪深300指数序列的波动性、收益率进行了实证研究,并且对序列做了拟合与预测,获得了不错的效果。除此,还证实了中国股市存在着显著的非对称效应。     

The law of value and laws of statistics: sectoral values and prices in the US economy, 1977-97

This study replicates findings that sectoral prices and valuesare highly correlated cross-sectionally, and that deviationsbetween them are small. Yet after controlling for variationsin industry size that produce ‘spurious correlation’,I find no reliable evidence that relative values have any influenceupon relative prices. The smallness of price–value deviationsthus does not result from such an influence; it is shown insteadto result from a lack of dispersion in the data. Values turnout to be no better predictors of prices than any other randomvariable with the same probability distribution.     

Assessing the causal relationship between euro-area money and prices in a time-varying environment

The paper provides new evidence on the causal relationship between money and price for the euro area using quarterly data for the period 1980 to 2006, employing two alternative methods of estimation: the vector error correction (VEC) and time-varying coefficient (TVC) estimation techniques. The latter technique has the advantage over the former technique in that it can deal with possible specification biases and spurious relationships that may have arisen from structural changes. The empirical results from the VEC method reveal a bidirectional causal relationship between money and prices. The results from the TVC technique suggest that money is acting as an exogenous process determining the price level.     

Error correction modelling and dynamic specifications as a conduit to outperforming the random walk in exchange rate forecasting

The proposition that dynamic exchange rate models can outperform the random walk in out-of-sample forecasting, in the sense that they produce lower mean square errors, is examined and disputed. By using several dynamic versions of three macroeconomic exchange rate models, it is demonstrated that dynamic specifications outperform the corresponding static models but improvement in the forecasting power may not be sufficient for the dynamic models to perform better than the random walk. The results are explained by suggesting that any dynamic specification or transformation of the static model leads to the introduction of a lagged dependent variable, which in effect is a random walk component. The analysis leads to the conclusion that it is implausible to aim at beating the random walk by augmenting a static model with a random walk component.