Labor Unions and Coalitions in Buffalo

Labor unions have evolved tremendously since their inception in 1866 in the United States. Today, some unions in the Buffalo region are responding to free market fundamentalism with the development of multiple coalition partners. Coalitions are composed of unions and like-minded activist organizations. This creative response to a long-term economic crisis has created a high road social infrastructure. Unions have moved beyond their traditional roles of collective bargaining and representation to a more community-oriented mission of improving the quality of local jobs

Does Beta React to Market Conditions? Estimates of Bull and Bear Betas using a Nonlinear Market Model with an Endogenous Threshold Parameter

We apply a logistic smooth transition market model (LSTM) to a sample of returns on Australian industry portfolios to investigate whether bull and bear market betas differ. Unlike other studies, our LSTM model allows for smooth transition between bull and bear states and allows the data to determine the threshold value. The estimated value of the smoothness parameter was very large for all industries implying that transition is abrupt. Therefore we estimated the threshold as a parameter along with the two betas in a dual beta market (DBM) framework using a sequential conditional least squares (SCLS) method. Using Lagrange Multiplier type tests of linearity, and the SCLS method our results indicate that for all but two industries the bull and bear betas are significantly different.Logistic Smooth Transition Market Model (LSTM); Sequential Conditional Least Squares (SCLS); Linearity Tests; Bull/Bear Betas

Choosing Lag Lengths in Nonlinear Dynamic Models

Given that it is quite impractical to use standard model selection criteria in a nonlinear modeling context, the builders of nonlinear models often choose lag length by setting it equal to the lag length chosen for a linear autoregression of the data. This paper studies the performance of this procedure in a variety of circumstances, and then proposes some new and simple model selection procedures, based on linear approximations of the nonlinear forms. The idea here is to apply standard selection criteria to these linear approximations, rather than to autoregressions that make no provision for nonlinear behavior. A simulation study compares the properties of these proposed procedures with the properties of linear selection procedures.Nonlinear time series models, Neural networks, Model selection criteria, Polynomial approximations, Volterra expansions.

Forecasting Under Strucural Break Uncertainty

This paper proposes two new weighting schemes that average forecasts using different estimation windows to account for structural change. We let the weights reflect the probability of each time point being the most-recent break point, and we use the reversed ordered Cusum test statistics to capture this intuition. The second weighting method simply imposes heavier weights on those forecasts that use more recent information. The proposed combination forecasts are evaluated using Monte Carlo techniques, and we compare them with forecasts based on other methods that try to account for structural change, including average forecasts weighted by past forecasting performance and techniques that first estimate a break point and then forecast using the post break data. Simulation results show that our proposed weighting methods often outperform the others in the presence of structural breaks. An empirical application based on a NAIRU Phillips curve model for the United States indicates that it is possible to outperform the random walk forecasting model when we employ forecasting methods that account for break uncertainty.Forecasting with Structural breaks, Parameter Shifts, break Uncertainty, Structural break Tests, Choice of Estimation Sample, Forecast Combinations, NAIRU Phillips Curve.

Nonlinear Correlograms and Partial Autocorrelograms

This paper proposes neural network based measures of predictability in conditional mean, and then uses them to construct nonlinear analogues to autocorrelograms and partial autocorrelograms. In contrast to other measures of nonlinear dependence that rely on nonparametric estimation of densities or multivariate integration, our autocorrelograms are simple to calculate and appear to work well in relatively small samples.Nonlinear autocorrelograms, Nonlinear time series models, Neural networks, Model selection criteria, Nonlinear partial autocorrelograms

Forecasting the Volatility of Australian Stock Returns: Do Common Factors Help?

This paper develops univariate and multivariate forecasting models for realized volatility in Australian stocks. We consider multivariate models with common features or common factors, and we suggest estimation procedures for approximate factor models that are robust to jumps when the cross-sectional dimension is not very large. Our forecast analysis shows that multivariate models outperform univariate models, but that there is little difference between simple and sophisticated factor models.

Nonlinear Autoregresssive Leading Indicator Models of Output in G-7 Countries

This paper studies linear and nonlinear autoregressive leading indicator models of business cycles in G7 countries. The models use the spread between short-term and long-term interest rates as leading indicators for GDP, and their success in capturing business cycles is gauged by non-parametric shape tests, and their ability to predict the probability of recession. We find that bivariate nonlinear models of output and the interest rate spread can successfully capture the shape of the business cycle in cases where linear models fail. Also, our nonlinear leading indicator models for USA, Canada and the UK outperform other models of GDP with respect to predicting the probability of recession.Business Cycles, Leading Indicators, Model Evaluation, Nonlinear Models, Yield Spreads.

Beveridge-Nelson Decomposition with Markov Switching

This paper considers Beveridge-Nelson decomposition in a context where the permanent and transitory components both follow a Markov switching process. Our approach incorporates Markov switching into a single source of error state-space framework, allowing business cycle asymmetries and regime switches in the long run multiplier.Beveridge-Nelson decomposition, Markov switching, Single source of error state space models

Testing for co-jumps in high-frequency financial data: an approach based on first-high-low-last prices

This paper proposes a new test for simultaneous intraday jumps in a panel of high frequency financial data. We utilize intraday first-high-low-last values of asset prices to construct estimates for the cross-variation of returns in a large panel of high frequency financial data, and then employ these estimates to provide a first-high-low-last price based test statistic to detect common large discrete movements (co-jumps). We study the finite sample behavior of our first-high-low-last price based test using Monte Carlo simulation, and find that it is more powerful than the Bollerslev et al (2008) return-based co-jump test. When applied to a panel of high frequency data from the Chinese mainland stock market, our first-high-low-last price based test identifies more common jumps than the return-based test in this emerging market.Covariance, Co-jumps, High-frequency data, First-High-Low-Last price, Microstructure bias, Nonsynchronous trades, Realized covariance, Realized co-range.