Stock Market, Interest Rate and Output: A Model and Estimation for US Time Series Data

Carl Chiarella 1 , 1 , Willi Semmler 2 , 2 , Stefan Mittnik 3 , 3  and Peiyuan Zhu 4 , 4
  • 1 School of Finance and Economics, University of Technology, Sydney, Australia,
  • 2 Center for Empirical Macroeconomics, Bielefeld and New School University
  • 3 Dept. of Economics, University of Kiel, Germany
  • 4 School of Finance and Economics, University of Technology, Sydney, Australia,

In this paper we construct a model of stock market, interest rate and output interaction which is a generalization of the well known 1981 model of Blanchard. We allow for imperfect substitutability between stocks and bonds in the asset market and for lagged portfolio adjustment. The reaction of agents to changes in the stock market is dependent on the state of the economy. We analyze the dynamics of the model and its local stability properties. A discretization in terms of observable variables is derived. Some empirical results for U.S. output, stock price and interest rate data are presented using nonlinear least square estimates. We perform some stochastic simulations of the estimated non-linear model, obtaining distributions of the key economic quantities, their autocorrelation structure and financial statistics which are compared with historical data and RBC models. In addition, following Mittnik and Zadrozny (1993) a VAR with confidence bands for historical data is estimated and cumulative impulse-response functions compared to the model's impulse response functions. We find that the model captures a number of features of the data.Acknowledgements: Willi Semmler wants to acknowledge financial support from the CEPA of the New School University and the Ministry of Science and Technology of the State of Northrhine-Westfalia, Germany. Carl Chiarella acknowledges support from Australia Research Council grant number: A79802872.

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SNDE recognizes that advances in statistics and dynamical systems theory can increase our understanding of economic and financial markets. The journal seeks both theoretical and applied papers that characterize and motivate nonlinear phenomena. Researchers are required to assist replication of empirical results by providing copies of data and programs online. Algorithms and rapid communications are also published.