Spatiotemporal Autoregressive Models of Neighborhood Effects

Using 70,822 observations on housing prices from 1969 to 1991 from Fairfax County Virginia, this article demonstrates the substantial benefits obtained by modeling the spatial as well as the temporal dependence of the data. Specifically, the spatiotemporal autoregression with twelve variables reduced median absolute error by 37.35% relative to an indicator-based model with twenty-six variables. One-step ahead forecasts also document the improved performance of the proposed spatiotemporal model. In addition, the article illustrates techniques for rapidly computing the estimates and shows how to compute indices for any location.     

Contemporaneous Spillover Effects between the U.S. and the U.K. Equity Markets

We use high frequency data and the “identification through heteroskedasticity” approach of Rigobon (2003) to capture the contemporaneous volatility spillover effects between the U.S. and U.K. equity markets. We demonstrate the relevance of taking into account the information present during simultaneous trading hours by comparing the results generated by our structural vector autoregression with those of a traditional reduced-form vector autoregression. Our findings clearly demonstrate that contemporaneous relations matter and that ignoring them leads to inappropriate conclusions regarding the magnitude and direction of volatility spillover.     

Spatial Autoregression Techniques for Real Estate Data

This paper describes how spatial techniques can be used to improve the accuracy of market value estimates obtained using multiple regression analysis. Rather than eliminating the problem of spatial residual dependencies through the inclusion of many independent variables, spatial statistical methods typically keep fewer independent variables and augment these with a simple model of the spatial error dependence. We discuss alternative spatial autoregression model specifications, estimation methods, and prediction procedures. An empirical example is provided in the appendix.     

The Predictive Power of Anisotropic Spatial Correlation Modeling in Housing Prices

This paper develops a method to capture anisotropic spatial autocorrelation in the context of the simultaneous autoregressive model. Standard isotropic models assume that spatial correlation is a homogeneous function of distance. This assumption, however, is oversimplified if spatial dependence changes with direction. We thus propose a local anisotropic approach based on non-linear scale-space image processing. We illustrate the methodology by using data on single-family house transactions in Lucas County, Ohio. The empirical results suggest that the anisotropic modeling technique can reduce both in-sample and out-of-sample forecast errors. Moreover, it can easily be applied to other spatial econometric functional and kernel forms.     

A Bayesian analysis of a variance decomposition for stock returns

We apply Bayesian methods to study a common vector autoregression (VAR)-based approach for decomposing the variance of excess stock returns into components reflecting news about future excess stock returns, future real interest rates, and future dividends. We develop a new prior elicitation strategy, which involves expressing beliefs about the components of the variance decomposition. Previous Bayesian work elicited priors from the difficult-to-interpret parameters of the VAR. With a commonly used data set, we find that the posterior standard deviations for the variance decomposition based on these previously used priors, including “non-informative” limiting cases, are much larger than classical standard errors based on asymptotic approximations. Therefore, the non-informative researcher remains relatively uninformed about the variance decomposition after observing the data. We show the large posterior standard deviations arise because the “non-informative” prior is implicitly very informative in a highly undesirable way. However, reasonably informative priors using our elicitation method allow for much more precise inference about components of the variance decomposition.     

Combining Census and Survey Data to Trace the Spatial Dimensions of Poverty: A Case Study of Ecuador

Poverty maps provide information on the spatial distributionof living standards. They are an important tool for policymakers,who rely on them to allocate transfers and inform policy design.Poverty maps are also an important tool for researchers, whouse them to investigate the relationship between distributionwithin a country and growth or other economic, environmental,or social outcomes. A major impediment to the development ofpoverty maps has been that needed data on income or consumptiontypically are available only from relatively small surveys.Census data have the required sample size but generally do nothave the required information. This article uses the case ofEcuador to demonstrate how sample survey data can be combinedwith census data to yield predicted poverty rates for the populationcovered by the census. These poverty rates are found to be preciselymeasured, even at fairly disaggregated levels. However, beyonda certain level of spatial disaggregation, standard errors riserapidly.     

Using private forecasts to estimate the effects of monetary policy

I develop a methodology that uses the forecasts of market participants and of policy makers to estimate the effects of monetary policy on output and inflation. My approach has advantages over the standard practice of fitting a vector autoregression to the data. I apply my methodology to data on output, interest rates and prices. I find that, even using the Federal Reserve Board's Greenbook forecasts to control for the policy maker's information set, prices rise initially in response to a monetary contraction. This finding undermines the standard justification for including an index of commodity prices in VARs.     

银行信贷与股票价格关系研究--基于后金融危机时代数据的再检验

本文以08年次贷危机以后的变量月度数据为基础,综合运用向量自回归模型以及误差修正模型来研究后金融危机时代我国银行信贷与股票价格之间的关系,实证结果表明,在金融危机后的中国,银行信贷与股票价格的关系在短期内存在一定的正向关系,但在长期内存在负向关系。通过格兰杰因果检验发现,股票市场的变化是信贷市场波动的格兰杰原因,而反之不成立。     

The Effectiveness of Unconventional Monetary Policy at the Zero Lower Bound: A Cross‐Country Analysis

This paper assesses the macroeconomic effects of unconventional monetary policies by estimating a panel vector autoregression (VAR) with monthly data from eight advanced economies over a sample spanning the period since the onset of the global financial crisis. It finds that an exogenous increase in central bank balance sheets at the zero lower bound leads to a temporary rise in economic activity and consumer prices. The estimated output effects turn out to be qualitatively similar to the ones found in the literature on the effects of conventional monetary policy, while the impact on the price level is weaker and less persistent. Individual country results suggest that there are no major differences in the macroeconomic effects of unconventional monetary policies across countries, despite the heterogeneity of the measures that were taken.     

Fluctuations in the UK equity market: what drives stock returns?

Present value parameters from a state-space model are estimated for the UK FT All-Share Index. The estimated parameters are used to construct a time series of expected future returns and expected future values of dividend growth, both of which are found to be time-varying with persistent components. Variations in the price-dividend ratio appear to be driven primarily by the variance in expected returns. A comparison with the findings from a present value-constrained vector autoregression model indicates that the latter forecasts future realized returns and dividend growth better than the series constructed using a state-space approach. Furthermore, when the model is estimated for monthly and quarterly data, expected dividend growth is found to be more persistent.     

In search of the natural rate of unemployment

The natural rate of unemployment can be measured as the time-varying steady state of a structural vector autoregression. For post-War US data, the natural rate implied by this approach is more volatile than most previous estimates, with its movements accounting for the bulk of the variation in the unemployment rate, as well as substantial portions of the variation in aggregate output and inflation. These movements, in turn, can be related to variables associated with labor-market search theory, including unemployment benefits, labor productivity, real wages, and sectoral shifts in the labor market. There is also a strong negative relationship between inflation and the corresponding measure of cyclical unemployment, supporting the existence of a short-run Phillips curve.     

投资者关注对金银期货市场收益影响的时频分位研究

根据Google投资者关注度指数和金银期货市场交易数据,构建基于小波分解序列的时频门限自回归分布滞后模型,通过分位数模型参数估计,基于时域与频域联合分析视角,考量投资者关注度对金银期货市场收益的影响。结果表明：投资者关注度对金银期货市场的影响具有异质性;在低频域内,投资者关注度对金银期货市场影响相对较小;极端分位数水平下,投资者关注度对金银期货市场收益影响的时效性较短,投资者关注度对白银期货市场收益的影响较弱。     

A multivariate model of strategic asset allocation

We develop an approximate solution method for the optimal consumption and portfolio choice problem of an infinitely long-lived investor with Epstein–Zin utility who faces a set of asset returns described by a vector autoregression in returns and state variables. Empirical estimates in long-run annual and post-war quarterly U.S. data suggest that the predictability of stock returns greatly increases the optimal demand for stocks. The role of nominal bonds in long-term portfolios depends on the importance of real interest rate risk relative to other sources of risk. Long-term inflation-indexed bonds greatly increase the utility of conservative investors.     

Characteristics and mechanisms of the U.S. stock market spillover effects on the Chinese A-share market: Evidence from 6 A-share broad-based and 31 sector indices

This study investigated the NYSE and NASDAQ composite indices' return and volatility spillover on 37 Chinese A-share indices. We used data from 2010 to 2022 with Multiplicative-Heteroskedasticity-GARCHX and rolling window analysis. We also applied a vector autoregression and impulse response analysis to study the relationship between the spillover effect and market return or volume condition. We found that: (1) the spillover effect of U.S. stocks can be quickly and mostly absorbed by the A-share market in the next trading day; (2) the dissimilarity of constituents among A-share indices contributes to the differences in the NYSE and NASDAQ spillover effects; (3) the spillover becomes more significant and prominent when the market performance is poor or when some special events occurred.     

Estimation of a rational expectations model of the term structure

This paper shows how to represent a vector autoregression (VAR) in terms of the eigenvalues and eigenvectors of its companion matrix. This representation is used to impose the exact restrictions implied by the expectations hypothesis on the VAR for short and long term interest rates and to calculate the restricted maximum likelihood estimates. The first difference representation for short and long rates used by Sargent (1979) is shown to be inconsistent with the expectations hypothesis, but a VAR with two unit roots is constructed that satisfies the exact restrictions and leads to similar restricted estimates.     

Money and Output: Friedman and Schwartz Revisited

More than 50 years ago, Friedman and Schwartz examined historical data for the United States and found evidence of procyclical movements in the money stock, which led corresponding movements in output. We find similar correlations in more recent data; these appear most clearly when Divisia monetary aggregates are used in place of the Federal Reserve's official, simple‐sum measures. When we use information in Divisia money to estimate a structural vector autoregression, identified monetary policy shocks appear to have large and persistent effects on output and prices, with a lag that has lengthened considerably since the early 1980s.     

TESTING THE PRESENT VALUE MODEL OF EQUITY PRICES FOR THE UK STOCK MARKET

This paper applies present value tests to the UK stock market. Using monthly data from 1965 to 1990 on real equity price and dividend indices, it is found that the restrictions imposed by the present value model on a vector autoregression comprised of the 'spread' between prices and dividends and the change in real dividends can be rejected both for the complete sample period and for a shorter sample which omits the early years of dividend control and the run up to and aftermath of the stock market 'Crash' of October 1987. These tests are supplemented by informal methods for evaluating the 'fit' of the present value model: the observed spread is found to move 'too much', so that deviations from the model are persistent and long-lasting.     

Reconsidering the role of money for output, prices and interest rates

New Keynesian models of monetary policy downplay the role of monetary aggregates, in the sense that the level of output, prices, and interest rates can be determined without knowledge of the quantity of money. This paper evaluates the empirical validity of this prediction by studying the effects of shocks to monetary aggregates using a vector autoregression (VAR). Shocks to monetary aggregates are identified by the restrictions suggested by New Keynesian monetary models. Contrary to the theoretical predictions, shocks to broad monetary aggregates have substantial and persistent effects on output, prices and interest rates.     

What Drives the Cross‐Section of Credit Spreads?: A Variance Decomposition Approach

I decompose the variation of credit spreads for corporate bonds into changing expected returns and changing expectation of credit losses. Using a log‐linearized pricing identity and a vector autoregression applied to microlevel data from 1973 to 2011, I find that expected returns contribute to the cross‐sectional variance of credit spreads nearly as much as expected credit loss does. However, most of the time‐series variation in credit spreads for the market portfolio corresponds to risk premiums.     

Identifying Discretionary Fiscal Policy Reactions with Real‐Time Data

We propose a method of identifying discretionary fiscal policy reactions using real‐time data. Automatic stabilizers should depend on true GDP, while discretionary fiscal policy is contingent on the information that policy makers have in real time. We can compute a real‐time measurement error by comparing the first release of GDP data with later revisions. Discretionary fiscal policy is influenced by this measurement error, whereas automatic fiscal policy is not. We use this identification approach to test the central identifying assumption of Blanchard and Perotti’s (2002) seminal structural vector autoregression (VAR). According to this assumption, fiscal policy makers do not react to GDP developments contemporaneously in a discretionary fashion. We find that government expenditure is adjusted upward if GDP growth in real time is lower than true GDP. This suggests that fiscal policy makers use short‐term funds to buy goods and services in response to their perception of GDP dynamics.