The Relationship Between The Inflation Rate And Inequality Across U.S. States: A Semiparametric Approach

This paper uses a cross-state panel for the United States over the 1976–2007 period to assess the relationship between income inequality and the inflation rate. Employing a semiparametric instrument variable (IV) estimator, we find that the relationship depends on the level of the inflation rate. A positive relationship occurs only if the states exceed a threshold level of inflation rate. Below this value, inflation rate lowers income inequality. The results suggest that a nonlinear relationship exists between income inequality and the inflation rate. © 2018 Springer Science+Business Media B.V., part of Springer Natur

Partisan Conflict and Income Inequality in the United States: A Nonparametric Causality-in-Quantiles Approach

This paper examines the predictive power of a partisan conflict on income inequality. Our study contributes to the existing literature by using the newly introduced nonparametric causality-in-quantile testing approach to examine how political polarization in the United States affects several measures of income inequality and distribution overtime. The study uses annual time-series data between the periods 1917–2013. We find evidence in support of a dynamic causal relationship between partisan conflict and income inequality, except at the upper end of the quantiles. Our empirical findings suggest that a reduction in partisan conflict will lead to a reduction in our measures of income inequality, but this requires that inequality is not exceptionally high

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.http://www.tandfonline.com/loi/raec202016-11-30hb2016Economic

Housing and the Great Depression

This paper considers the structural stability of the relationship between the real housing price and real GDP per capita for an annual sample that includes the Great Depression. We test for structural change in parameter values, using a sample of annual US data from 1890 to 1952. The paper examines the long-run and short-run dynamic relationships between the real housing price and real GDP per capita to determine if these relationships experienced structural change over the sample period. We find that temporal Granger causality exists between these two variables only for sub-samples that include the Great Depression. For the other sub-sample periods as well as for the entire sample period no relationship exists between these variables.http://www.tandfonline.com/loi/raec202016-02-28hb201

Structural Breaks, Long Memory, or Unit Roots in Stock Prices: Evidence from Emerging Markets

This paper investigates whether daily stock price indices from fourteen emerging markets are random walk (unit root) or mean reverting long memory processes. We use an efficient statistical framework that tests for random walks in the presence of multiple structural breaks at unknown dates. This approach allows us to investigate a broader range of persistence than that allowed by the I(0)/I(1) paradigm about the order of integration, which is usually implemented for testing the random walk hypothesis in stock market indices. Our approach extends Robinson’s (1994) efficient test of unit root against fractional integration to allow for multiple endogenously determined structural breaks. For almost all countries, we find support for the random walk hypothesis, with the exception of four stock markets, where weak evidence of mean reverting long memory exist. Structural breaks have impact on the unit root behavior only for Mexico; for all other 11 markets unit roots exist even when structural breaks are not taken into account. In order to check the robustness of our results, we use the two-step feasible exact local Whittle (FELW2ST) estimator of Shimotsu (2010), which allows for polynomial trends, non-normal distributions, and non-stationarity. The results from the semi-parametric FELW2ST approach shows that, except for Mexico, stock price indices of 13 emerging markets are not mean reverting

Regime switching model of US crude oil and stock market prices : 1859 to 2013

This paper examines the relationship between US crude oil and stock market prices, using a Markov-Switching vector error-correction model and a monthly data set from 1859 to 2013. The sample covers the entire modern era of the petroleum industry, which typically begins with the first drilled oil well in Titusville, Pennsylvania in 1858. We estimate a two-regime model that divides the sample into high- and low-volatility regimes based on the variance–covariance matrix of the oil and stock prices. We find that the high-volatility regime more frequently exists prior to the Great Depression and after the 1973 oil price shock caused by the Organization of Petroleum Exporting Countries. The low-volatility regime occurs more frequently when the oil markets fell largely under the control of the major international oil companies from the end of the Great Depression to the first oil price shock in 1973. Using the National Bureau of Economic research business cycle dates, we also find that the high-volatility regime more likely occurs when the economy experiences a recession.http://www.elsevier.com/locate/eneco2016-05-30hb201

The relationship between the inflation rate and inequality across U.S. states : a semiparametric approach

This paper uses a cross-state panel for the United States over the 1976–2007 period to assess the relationship between income inequality and the inflation rate. Employing a semiparametric instrument variable (IV) estimator, we find that the relationship depends on the level of the inflation rate. A positive relationship occurs only if the states exceed a threshold level of inflation rate. Below this value, inflation rate lowers income inequality. The results suggest that a nonlinear relationship exists between income inequality and the inflation rate.http://link.springer.com/journal/111352019-01-09hj2018Economic

Forecasting US real private residential fixed investment using a large number of predictors

This paper employs classical bivariate, slab-and-spike variable selection, Bayesian semi-parametric shrinkage, and factor augmented predictive regression models to forecast US real private residential fixed investment over an out-of-sample period from 1983Q1 to 2005Q4, based on in-sample estimates for 1963Q1 to 1982Q4. Both large-scale (188 macroeconomic series) and small-scale (20 macroeconomic series) slab-and-spike variable selection, and Bayesian semi-parametric shrinkage, and factor augmented predictive regressions, as well as 20 bivariate regression models, capture the influence of fundamentals in forecasting residential investment. We evaluate the ex-post out-of-sample forecast performance of the 26 models using the relative average Mean Square Error for one-, two-, four-, and eight-quarters-ahead forecasts and test their significance based on the McCracken (2004, 2007) mean-square-error F statistic. We find that, on average, the slab-and-spike variable selection and Bayesian semi-parametric shrinkage models with 188 variables provides the best forecasts amongst all the models. Finally, we use these two models to predict the relevant turning points of the residential investment, via an ex-ante forecast exercise from 2006Q1 to 2012Q4. The 188 variable slab-and-spike variable selection and Bayesian semi-parametric shrinkage models perform quite similarly in their accuracy of forecasting the turning points. Our results suggest that economy-wide factors, in addition to specific housing market variables, prove important when forecasting in the real estate market.http://link.springer.com/journal/1812017-12-31hb201

Forecasting Nevada gross gaming revenue and taxable sales using coincident and leading employment indexes

This article provides out-of-sample forecasts of Nevada gross gaming revenue (GGR) and taxable sales using a battery of linear and non-linear forecasting models and univariate and multivariate techniques. The linear models include vector autoregressive and vector error-correction models with and without Bayesian priors. The non-linear models include non-parametric and semi-parametric models, smooth transition autoregressive models, and artificial neural network autoregressive models. In addition to GGR and taxable sales, we employ recently constructed coincident and leading employment indexes for Nevada’s economy. We conclude that the non-linear models generally outperformhttp://link.springer.com/journal/181hb201

Was the recent downturn in US real GDP predictable?

This article uses a small set of variables – real GDP, the inflation rate and the short-term interest rate – and a rich set of models – atheoretical (time series) and theoretical (structural), linear and nonlinear, as well as classical and Bayesian models – to consider whether we could have predicted the recent downturn of the US real GDP. Comparing the performance of the models to the benchmark random-walk model by root mean-square errors, the two structural (theoretical) models, especially the nonlinear model, perform well on average across all forecast horizons in our ex post, out-ofsample forecasts, although at specific forecast horizons certain nonlinear atheoretical models perform the best. The nonlinear theoretical model also dominates in our ex ante, out-of-sample forecast of the Great Recession, suggesting that developing forward-looking, microfounded, nonlinear, dynamic stochastic general equilibrium models of the economy may prove crucial in forecasting turning points.http://www.tandfonline.com/loi/raec202017-04-30hb2016Economic