Contributions to Economic Analysis

By: C. Milas (Editor), P. A. Rothman (Editor), Dick van Dijk (Editor), David E. Wildasin (Editor)

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Hardcover | 8 February 2006

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The business cycle has long been the focus of empirical economic research. Until recently statistical analysis of macroeconomic fluctuations was dominated by linear time series methods. Over the past 15 years, however, economists have increasingly applied tractable parametric nonlinear time series models to business cycle data; most prominent in this set of models are the classes of Threshold AutoRegressive (TAR) models, Markov-Switching AutoRegressive (MSAR) models, and Smooth Transition AutoRegressive (STAR) models. In doing so, several important questions have been addressed in the literature, including:

1. Do out-of-sample (point, interval, density, and turning point) forecasts obtained with nonlinear time series models dominate those generated with linear models?
2. How should business cycles be dated and measured?
3. What is the response of output and employment to oil-price and monetary shocks?
4. How does monetary policy respond to asymmetries over the business cycle?
5. Are business cycles due more to permanent or to transitory negative shocks?
6. Is the business cycle asymmetric, and does it matter?

Accordingly, we have compiled and edited a book for the Elsevier economics program comprising 15 original papers on these and related themes.
*Contributions to Economic Analysis was established in 1952
*The series purpose is to stimulate the international exchange of scientific information
*The series includes books from all areas of macroeconomics and microeconomics

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Non-Fiction Mathematics Calculus & Mathematical Analysis Probability & Statistics Business & Management Business Strategy Business Mathematics & Systems Economics Microeconomics Management & Management Techniques

Introduction	p. ix
List of Contributors	p. xxiii
Dating Business Cycle Turning Points	p. 1
Introduction	p. 2
What can we infer from U.S. GDP growth rates?	p. 3
Parametric representation	p. 10
Using multiple indicators to identify turning points	p. 22
Empirical performance of the monthly recession probability index	p. 32
Alternative approaches to monthly inference	p. 48
Acknowledgements	p. 50
References	p. 51
Appendix	p. 53
Combining Predictors & Combining Information in Modelling: Forecasting Us Recession Probabilities and Output Growth	p. 55
Introduction	p. 55
Models and data	p. 58
Logit models	p. 58
Models of output growth	p. 61
Non-linear models of output growth	p. 62
Out-of-sample forecasting exercise	p. 63
Forecast combination schemes	p. 63
Forecast evaluation	p. 63
Empirical results	p. 64
Conclusions	p. 69
Acknowledgements	p. 70
References	p. 70
The Importance of Nonlinearity in Reproducing Business Cycle Features	p. 75
Introduction	p. 75
An algorithm for establishing business cycle turning points	p. 79
Business cycle features in U.S. real GDP data	p. 81
Business cycle features in simulated data from time-series models	p. 83
Model description and estimation	p. 83
Business cycle features from linear models	p. 87
Business cycle features from regime-switching models	p. 88
Business cycle features and heteroskedasticity	p. 90
Conclusions	p. 92
Acknowledgements	p. 93
References	p. 93
The Vector Floor and Ceiling Model	p. 97
Introduction	p. 97
A nonlinear VAR with floor and ceiling effects	p. 99
Empirical results	p. 105
Model comparison results	p. 106
A comparison of Bayesian and classical results	p. 109
Impulse response analysis	p. 113
Conclusions	p. 118
Acknowledgements	p. 118
References	p. 119
Sample information	p. 121
Bayesian analysis of the VFC model	p. 122
Classical analysis of the VFC model	p. 128
Further details on impulse response analysis	p. 131
A New Framework to Analyze Business Cycle Synchronization	p. 133
Introduction	p. 133
A framework to analyze business cycle synchronization	p. 135
Univariate Markov-switching approach	p. 135
Multivariate Markov-switching approach	p. 136
Empirical results	p. 139
Preliminary analysis of data	p. 139
Comparative analysis of business cycle synchronization	p. 143
Business cycle synchronization across G7 countries	p. 145
Conclusions	p. 148
Acknowledgements	p. 148
References	p. 148
Non-Linearity and Instability in the Euro Area	p. 151
Introduction	p. 151
The data	p. 153
Forecasting methods	p. 154
Linear methods	p. 155
Time-varying methods	p. 156
Non-linear methods	p. 156
Forecast evaluation	p. 158
Measuring the extent of instability	p. 163
Instability tests	p. 163
Forecast evaluation for unstable series	p. 165
Forecasting industrial production, unemployment and inflation	p. 168
Conclusions	p. 171
Acknowledgements	p. 172
References	p. 172
Nonlinear Modelling of Autoregressive Structural Breaks in Some Us Macroeconomic Series	p. 175
Introduction	p. 175
Modelling structural breaks in autoregressive coefficients	p. 177
A Monte Carlo study	p. 183
Empirical application	p. 184
Conclusions	p. 195
Acknowledgements	p. 195
References	p. 195
Data Appendix	p. 197
Trend-Cycle Decomposition Models With Smooth-Transition Parameters: Evidence From U.S. Economic Time Series	p. 199
Introduction	p. 199
Trend-cycle decomposition model	p. 202
Fixed parameter specification	p. 202
Time-varying parameter specification	p. 203
State space representation	p. 205
Empirical evidence from U.S. economic time series	p. 206
Data	p. 206
Basic decompositions	p. 206
Smooth transitions over time	p. 209
Discussion and conclusion	p. 216
References	p. 217
Appendix	p. 218
Modeling Inflation and Money Demand Using a Fourier-Series Approximation	p. 221
Introduction	p. 222
Modeling with a Fourier approximation	p. 224
Dependent error structures	p. 228
Power	p. 228
A structural break in the inflation rate	p. 230
Selecting the optimal number of terms in the Fourier expansion	p. 233
Structural breaks in the demand for money	p. 234
The bootstrap	p. 238
The error-correction model	p. 240
The restricted model	p. 240
Integer frequencies	p. 241
Missing variables	p. 242
Conclusions	p. 243
Acknowledgements	p. 244
References	p. 244
Random Walk Smooth Transition Autoregressive Models	p. 247
Introduction	p. 247
The RW-STAR Model	p. 249
The model	p. 249
Modelling procedure	p. 251
Performance of the nonlinearity tests	p. 253
Modelling industrial production of selected OECD countries	p. 255
The data	p. 255
Linearity tests	p. 257
Development of baseline models	p. 258
Estimation of RW-STAR models	p. 261
Forecast performance	p. 270
Conclusions	p. 277
Acknowledgements	p. 278
References	p. 278
DGPs for the power simulations	p. 280
Nonlinearity and Structural Change in Interest Rate Reaction Functions for The Us, UK and Germany	p. 283
Introduction	p. 283
Interest rate models	p. 285
The models	p. 285
Selection of explanatory and transition variables	p. 288
Sample periods and data	p. 289
Results	p. 292
Linear models	p. 292
Nonlinear models	p. 295
Concluding remarks	p. 302
Acknowledgements	p. 303
References	p. 303
Modelling methodology and additional results	p. 305
State Asymmetries in the Effects of Monetary Policy Shocks on Output: Some New Evidence for the Euro-Area	p. 311
Introduction	p. 311
Related literature	p. 313
Estimation of a monetary policy reaction function	p. 315
Markov Switching Models for real output growth	p. 320
Extended Markov Switching model including interest-rate shocks	p. 321
Effects of monetary policy on state switches	p. 326
Conclusions	p. 328
Acknowledgements	p. 329
References	p. 329
Non-Linear Dynamics In Output, Real Exchange Rates and Real Money Balances: Norway, 1830-2003	p. 333
Introduction	p. 333
STR models	p. 336
Testing for non-linearity and its form	p. 337
Evaluation of STR models	p. 338
Data and its properties	p. 339
Multivariate linear models	p. 347
Linear dynamic models	p. 350
Non-linear conditional models	p. 354
STR models of output, the real exchange rate and real money	p. 355
The STR models	p. 358
LSTR model of output	p. 359
STR model of the real exchange rate	p. 362
LSTR model of real money	p. 366
Dynamics of the linear versus the non-linear systems of equations	p. 369
Concluding remarks	p. 371
Acknowledgements	p. 372
References	p. 372
Data	p. 376
A Predictive Comparison of Some Simple Long- and Short Memory Models of Daily U.S. Stock Returns, With Emphasis on Business Cycle Effects	p. 379
Introduction	p. 379
Empirical methods	p. 381
Long memory model estimation	p. 382
Short memory models	p. 386
Nonlinear STAR models	p. 387
Predictive accuracy testing	p. 387
Predictive model selection	p. 390
Empirical results	p. 391
S&P500 returns: business cycle effects	p. 395
Concluding remarks	p. 400
Acknowledgements	p. 400
References	p. 400
Nonlinear Modeling of the Changing Lag Structure in U.S. Housing Construction	p. 407
Introduction	p. 407
The data	p. 409
Unrestricted finite distributed lag model	p. 412
The autoregressive distributed lag model	p. 416
Nonlinear autoregressive distributed lag models	p. 419
Conclusions	p. 427
Acknowledgements	p. 428
References	p. 428
Subject Index	p. 431
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Contributions to Economic Analysis

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