2014 Summer Course School of Business, Nanjing University. State-Space Modelling and Its Applications in Economics and Finance

Transcription

1 2014 Summer Course School of Business, Nanjing University State-Space Modelling and Its Applications in Economics and Finance Instructor: Jun Ma This version: May 14, 2014 Primary Textbooks: Kim, Chang-Jin and Charles R. Nelson, State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications, MIT Press, Hamilton, James D. Time Series Analysis, Princeton University Press, Course Description: This course is an advanced applied time series class. The aim is to introduce the cuttingedge time series modelling techniques that have been frequently applied in economics and finance. Knowledge of econometrics and macroeconomics in the first year graduate courses are assumed. We will first review basic univariate and multivariate time series models and their estimation methods. We will then introduce the state-space modelling technique and Kalman filter through various examples. The applications include various topics in the areas of business cycle analysis, asset prices fluctuations, and exchange rate determinations among many others. We will study the estimation algorithms and illustrate the applications using GAUSS or/and MATLAB codes. Although our focus in this course is primarily the econometric tools, through applications we will also discuss some important models in macroeconomics, asset pricing, and international finance. Course Requirement: There will be a mid-term exam and a take-home project that involves replicating some empirical results in the designated papers. List of Topics (Subject to Changes): 1. Time Series: Motivation and Introduction (1) Implications of the time series properties of the data: An example for illustrations: a stylized current account model (2) Basic time series models AR, MA, ARMA, impulse response and variance decomposition stationary versus nonstationary 2. More on Basic Time Series Models (1) The MLE estimation and numerical optimization (2) VAR, SVAR, and VECM An example for illustrations: test of the present value model of the current account 1

2 3. State space models: motivations and notations (1) Motivations and Notations through examples Trend and cycle decomposition Clark and Harvey Its connection with the ARMA model Morley, Nelson, and Zivot Dynamic factor model (DFM) global, regional and local business cycles Time varying parameter model (TVP) CAPM, Taylor rule, TVP-VAR (2) Any model that involves rational expectations: Stock returns decomposition Campbell and Shiller Exchange rate decomposition Engel and West Term structure of interest rates Dynamic Nelson-Siegel model No arbitrage Affine Term Structure Model A stylized Gaussian model 4. State Space Model Estimations (1) Kalman filter and the estimation algorithm (2) An example: UC output decomposition Clark model, Morley, Nelson, and Zivot 5. More Applications (1) Multivariate UC output decomposition - Sinclair, Ma and Wohar Phillips curve estimation - Lee and Nelson TVP model - Kim and Nelson (2) Stock price decomposition - Su, Ma and Wohar Inflation and stock returns - Levant, Ma and Wohar Exchange rate decomposition - Balke, Ma and Wohar House price decomposition - Fairchild, Ma and Wu Dynamic factor model - Stock and Watson coincidental index 6. Markov Regime-Switching Model (1) A brief introduction of the nonlinear models: Markov switching - Hamilton model (2) Estimation algorithm and illustrations Hamilton model, Volatility switching 7. State Space Model with Markov Regime-Switching (1) Kim s filter and estimation algorithm (2) Applications Kim and Nelson plucking model Levant and Ma DNS with MS 8. More Advanced Topics (1) Introduction to Bayesian MCMC estimation Why MCMC motivations Some popular algorithms: Gibbs-Sampling, Metropolis-Hasting Applications: TVP-VAR (2) The second moment and its importance: GARCH, Markov regime-switching Stochastic volatility model 2

3 TVP-VAR-SV: Primeciri or Cogley and Sargent Reading List (by topics) 1. Trend and Cycle Decomposition and Business Cycles Analysis Basistha, A. and C.R. Nelson (2007): New Measures of the Output Gap Based on the Forward-looking New Keynesian Phillips Curve, Journal of Monetary Economics, 54, Beveridge, S. and C.R. Nelson (1981): A New Approach to Decomposition of Economic Time Series into Permanent and Transitory Components with Particular Attention to Measurement of the Business Cycle, Journal of Monetary Economics, 7, Clark, P.K. (1987): The Cyclical Component of U.S. Economic Activity, The Quarterly Journal of Economics, 102, Cogley, T. and T.J. Sargent (2005): Drift and Volatilities: Monetary Policies and Outcomes in the post WWII US, Review of Economic Dynamics, 8, Hamilton, J. (1989): A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle, Econometrica, 57, Kim, C.-J. and C.R. Nelson (1999): Friedman s Plucking Model of Business Fluctuations: Tests and Estimates of Permanent and Transitory Components, Journal of Money, Credit and Banking, 31, Kim, C.-J. and C.R. Nelson (2006): Estimation of a Forward-Looking Monetary Policy Rule: A Time-Varying Parameter Model using Ex Post Data, Journal of Monetary Economics, 53, Lee, J., and C. R. Nelson (2007): Expectation Horizon and the Phillips Curve: The Solution to an Empirical Puzzle, Journal of Applied Econometrics, 22, Ma, J. and M. E. Wohar (2013): An Unobserved Components Model That Yields Business and Medium-Run Cycles, Journal of Money, Credit, and Banking, Vol.45, No.7, October, Morley, J., C.R. Nelson and E. Zivot (2003): Why Are Beveridge-Nelson and Unobserved Component Decomposition of GDP So Different? Review of Economics and Statistics, 85, Primiceri, G.E. (2005): Time Varying Structural Vector Autoregressions and Monetary Policy, The Review of Economic Studies, 72,

4 Sinclair, T. (2009): The Relationship between Permanent and Transitory Movements in U.S. Output and the Unemployment Rate, Journal of Money, Credit and Banking, 41, Stock, J.H. and M.W. Watson (2007): Why Has U.S. Inflation Become Harder to Forecast? Journal of Money, Credit and Banking, 39, Current Account Determination and Exchange Rate Dynamics Balke, N., J. Ma, and M. Wohar (2013): The Contribution of Economic Fundamentals to Movements in Exchange Rates, Journal of International Economics, 90, Corsetti, G. and P. Konstantinou (2012): What Drives US Foreign Borrowing? Evidence on the External Adjustment to Transitory and Permanent Shocks, American Economic Review, 102, Engel, C. and J. Hamilton (1990): Long Swings in the Dollar: Are They in the Data and Do Markets Know It? American Economic Review, 1990, 80, Engel, C. and C.-J. Kim (1999): The Long-Run U.S./U.K. Real Exchange Rate, Journal of Money, Credit and Banking, 31, Engel, C. and K.D. West (2005): Exchange Rate and Fundamentals, The Journal of Political Economy, 113, Engel, C. and K.D. West (2006): Taylor Rules and the Deutschmark-Dollar Real Exchange Rate, Journal of Money, Credit, and Banking, 38, Engel, C., N. Mark, and K. West (2012), Factor Model Forecasts of Exchange Rates, Econometric Reviews, forthcoming. Gourinchas, P. and H. Rey (2007): International Financial Adjustment, Journal of Political Economy, 115, Nason, J.M. and J.H. Rogers (2006): The Present-Value Model of the Current Account Has Been Rejected: Round up the Usual Suspects, Journal of International Economics, 68, Nason, J.M. and J.H. Rogers (2008): Exchange Rates and Fundamentals: A Generalization, International Finance Discussion Papers Number 948, Board of Governors of the Federal Reserve System. Sheffrin, S.M. and W.T.Woo (1990): Present Value Tests of an Intertemporal Model of the Current Account, Journal of International Economics, 29, Empirical Asset Pricing 4

5 Ang, A. and M. Piazzesi (2003): A No-Arbitrage Vector Autoregression of Term Structure Dynamics with Macroeconomic and Latent Variables, Journal of Monetary Economics, 50, Bansal, R. and A. Yaron (2004): Risks for The Long Run: A Potential Resolution of Asset Pricing Puzzles, Journal of Finance, 59, Binsbergen, J. H. van, and R. S. J. Koijen (2010): Predictive regressions: A Presentvalue Approach, Journal of Finance, 65, Campbell, J., and R. Shiller (1988): The Dividend-price Ratio and Expectations of Future Dividends and Discount Factors, Review of Financial Studies, 1, Cochrane, J. (2011): Discount Rates: American Finance Association Presidential Address, Journal of Finance, 66, Diebold, F.X., G.D. Rudebusch, and B.S. Aruoba (2006): The Macroeconomy and the Yield Curve: A Dynamic Latent Factor Approach, Journal of Econometrics, 131, Duffee, G.R. (2002): Term Premia and Interest Rate Forecasts in Affine Models, Journal of Finance, 57, Fairchild, J., J. Ma, and S. Wu (2014): Understanding Housing Market Volatility, working paper, University of Alabama and University of Kansas. Joslin, S., K.J. Singleton, and H. Zhu (2011): A New Perspective on Gaussian Dynamic Term Structure Models, Review of Financial Studies, 24, Ma, J. (2013): Long-Run Risks and Its Implications for the Equity Premium Puzzle: New Evidence from a Multivariate Framework, Journal of Money, Credit, and Banking, 45, Ma, J. and M.E. Wohar (2014): Expected Returns and Expected Dividend Growth: Time to Rethink an Established Empirical Literature, Applied Economics, forthcoming. Nelson, C.R. and A. Siegel (1987): Parsimonious Modeling of Yield Curves, Journal of Business, 60, Su, Z., J. Ma, and M.E. Wohar (2012): Sources of the Stock Price Fluctuations in Chinese Equity Market, European Journal of Finance, forthcoming. 5