Prasad Bidarkota                                                                        July 31, 2011

 

Econometrics III (ECO 7429)

Ref. No. 88739

Department of Economics, Florida International University (University Park)

Fall Semester 2011

 

 

 

List of Readings

 

  1. Introduction to Difference Equations and Lag Operators.

Walter Enders (2004), Applied Econometric Time Series, John Wiley & Sons, Inc., Chapter 1: Difference Equations, p. 1-47.

 

  1. Stationary Time Series Models.

Walter Enders (2004), Applied Econometric Time Series, John Wiley & Sons, Inc., Chapter 2: Stationary Time Series Models, p. 48-107.

 

  1. Maximum Likelihood Estimation.

Andrew C. Harvey (1991), An Econometric Analysis of Time Series, 2nd Edition, Cambridge University Press, Chapter 3: The Method of Maximum Likelihood, p. 84-121.

 

  1. Numerical Optimization.

Andrew C. Harvey (1991), An Econometric Analysis of Time Series, 2nd Edition, Cambridge University Press, Chapter 4: Numerical Optimization, p. 122-145.

 

  1. Some advanced topics in Time Series.

See the accompanying list of readings.

 

I. AUTOREGRESSIVE CONDITIONAL HETEROSKEDASTICITY

ARCH AND GARCH CLASS OF MODELS

 

Introduction

 

Enders, W., Applied Econometric Time Series, John Wiley & Sons, Inc. (2004). (Chapter 3)

 

Hamilton, James D., Time Series Analysis, Princeton University Press (1994), Princeton, New Jersey. (Chapter 21)

 

Engle, R.F. (1982), ‘Autoregressive conditional heteroskedasticity with estimates of the variance of United Kingdom inflation,’ Econometrica, Vol.50, No.4, 987-1007.

 

 

Extensions of & Alternatives to the Basic Model

 

1. Generalized Autoregressive Conditional Heteroskedasticity (GARCH) Model

 

Bollerslev, T. (1986), ‘Generalized autoregressive conditional heteroskedasticity,’ Journal of Econometrics, 31, 307-327.

 

2. Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) Model

 

Nelson, D.B. (1990), ‘Conditional heteroskedasticity in asset returns: a new approach,’ Econometrica, Vol.59, No.2, 347-370.

 

3. Multivariate GARCH Models (see also DCC Models)

 

Bollerslev, T. and R.F. Engle (1993), ‘Common persistence in conditional variances,’ Econometrica, Vol.61, No.1, 167-186.

 

4. Stochastic Volatility Models

 

Danielsson, J. (1994), ‘Stochastic volatility in asset prices: estimation with simulated maximum likelihood,’ Journal of Econometrics, 64, 375-400.

 

Sandmann, G. and S.J. Koopman (1998), ‘Estimation of stochastic volatility models via Monte Carlo maximum likelihood,’ Journal of Econometrics, 87, 271-301.

 

 

II. TAR MODELS

 

Threshold Autoregressions: Introduction

 

Tong, Howell, Non-Linear Time Series: A Dynamical System Approach, Oxford University Press (1990), New York.

 

Tsay, R.S., “Detecting and modeling nonlinearity in univariate time series analysis,” Statistica Sinica, 1(1991), 431-451.

 

Granger, C.W.J. and T. Terasvirta, Modeling Nonlinear Economic Relationships, Oxford university Press (1993), New York.

 

 

Applications

 

Tsay, R.S., “Non-linear time series analysis of blowfly population,” Journal of Time Series Analysis, Vol.9, No.3 (1988), 247-63.

 

Potter, S.M., “A non-linear approach to U.S. GNP,” Journal of Applied Econometrics, Vol.10 (1995), 109-25.

 

Blanchard, O.J. and M.W. Watson, “Are business cycles all alike?” The American Business Cycle: Continuity and Change, R.J. Gordon (ed.), University of Chicago Press (1986), 123-79.

 

DeLong, J.B. and L.H. Summers, “Are business cycles symmetrical?” The American Business Cycle: Continuity and Change, R.J. Gordon (ed.), University of Chicago Press (1986), 166-79.

 

Scheinkman, J.A. and B.LeBaron, “Non-linear dynamics and GNP data,” Economic Complexity: Chaos, Sunspots, Bubbles, and Non-linearity, W.A. Barnett et al. (eds.), Cambridge University Press (1989), 213-27.

 

Brunner, A.D., “On the dynamic properties of asymmetric models of real GNP,” The Review of Economics and Statistics, 79 (1997), 321-326.

 

Neftci, S.N., “Are economic time series asymmetric over the business cycle?” Journal of Political Economy, Vol.92, No.2 (1984), 307-28.

 

 

III. MARKOV SWITCHING MODELS

 

Introduction

 

Hamilton, J.D., ‘A new approach to the economic analysis of nonstationary time series and the business cycle,’ Econometrica, Vol.57, No.2 (1989), 357-84.

 

 

Bivariate Models

 

Phillips, K.L., ‘A two-country model of stochastic output with changes in regime,’ Journal of International Economics, 31 (1991), 121-142.

 

 

Extensions of the Basic Model

 

Lam, P-s., ‘The Hamilton model with a general autoregressive component: Estimation and comparison with other models of economic time series,’ Journal of Monetary Economics, 26 (1990), 409-32.

 

Durland, J.M. and T.H. McCurdy, ‘Duration-dependent transitions in a Markov model of US GNP growth,’ Journal of Business and Economic Statistics, Vol.12, No.3 (1994), 279-288.

 

 

Applications

 

Cecchetti, S.G., P-s. Lam, and N.C. Mark, 1990, Mean reversion in equilibrium asset prices, The American Economic Review 80, 398-418.

 

Engel, C. and J.D. Hamilton (1990), ‘Long swings in the dollar: Are they in the data and do markets know it?’ The American Economic Review 80, No.4, 689-713.

 

Evans, M. and K. Lewis, ‘Do expected shifts in inflation affect estimates of the long-run Fisher relation?’ Journal of Finance, Vol.L, No.1 (1995), 225-253.

 

Garcia, R. and P. Perron, ‘An analysis of the real interest rate under regime shifts,’ The Review of Economics and Statistics (1996), 111-123.

 

Raymond, J.E. and R.W. Rich (1997), “Oil and the macroeconomy: a Markov state-switching approach,” Journal of Money, Credit, and Banking, Vol.29, No.2, 193-213.

 

 

 

IV. STATE SPACE MODELS

 

Introduction

 

Durbin, J. and S.J. Koopman, Time Series Analysis by State Space Methods, Oxford Statistical Series 24 (2001), Oxford University Press.

 

Harvey, Andrew C., Forecasting, Structural Time Series Models and the Kalman Filter, Cambridge University Press (1989), New York. (Chapter 3)

 

Hamilton, James D., Time Series Analysis, Princeton University Press (1994), Princeton, New Jersey. (Chapter 13)

 

 

Applications

 

Gregory, A.W., A.C. Head, and J. Raynauld (1997), “Measuring world business cycles,” International Economic Review, Vol.38, No.3, 677-701.

 

Harvey, A. C. (1985), “Trends and cycles in macroeconomic time series,” Journal of Business and Economic Statistics,” Vol.3, No.3, 216-27.

 

Wolff, C.C.P. (1987), “Forward foreign exchange rates, expected spot rates, and premia: a signal-extraction approach,” The Journal of Finance, Vol.XLII, No.2, 395-406.

 

 

State Space Models and ARCH

 

Harvey, A., E. Ruiz, and E. Sentana (1992), ‘Unobserved component time series models with ARCH disturbances,’ Journal of Econometrics, 52, 129-157.