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on Econometric Time Series |
| By: | Gregor Bäurle |
| Abstract: | We propose a method to incorporate information from Dynamic Stochastic General Equilibrium (DSGE) models into Dynamic Factor Analysis. The method combines a procedure previously applied for Bayesian Vector Autoregressions and a Gibbs Sampling approach for Dynamic Factor Models. The factors in the model are rotated such that they can be interpreted as variables from a DSGE model. In contrast to standard Dynamic Factor Analysis, a direct economic interpretation of the factors is given. We evaluate the forecast performance of the model with respect to the amount of information from the DSGE model included in the estimation. We conclude that using prior information from a standard New Keynesian DSGE model improves the forecast performance. We also analyze the impact of identified monetary shocks on both the factors and selected series. The interpretation of the factors as variables from the DSGE model allows us to use an identification scheme which is directly linked to the DSGE model. The responses of the factors in our application resemble responses found using VARs. However, there are deviations from standard results when looking at the responses of specific series to common shocks. |
| Keywords: | Dynamic Factor Model; DSGE Model; Bayesian Analysis; Forecasting; Transmission of Shocks |
| JEL: | C11 C32 E0 |
| Date: | 2008–08 |
| URL: | http://d.repec.org/n?u=RePEc:ube:dpvwib:dp0803&r=ets |
| By: | Ali Choudhary (University of Surrey and State Bank of Pakistan); Adnan Haider (State Bank of Pakistan) |
| Abstract: | We assess the power of artificial neural network models as forecasting tools for monthly inflation rates for 28 OECD countries. For short out-of-sample forecasting horizons, we find that, on average, for 45% of the countries the ANN models were a superior predictor while the AR1 model performed better for 21%. Furthermore, arithmetic combinations of several ANN models can also serve as a credible tool for forecasting inflation. |
| Keywords: | Artificial Neural Networks; Forecasting; Inflation |
| JEL: | C51 C52 C53 E31 E37 |
| Date: | 2008–11 |
| URL: | http://d.repec.org/n?u=RePEc:sur:surrec:0808&r=ets |
| By: | Nils Herger (Study Center Gerzensee); |
| Abstract: | Using count data on the number of bank failures in US states during the 1960 to 2006 period, this paper endeavors to establish how far sources of economic risk (recessions, high interest rates, in ation) or differences in solvency and branching regulation can explain some of the fragility in banking. Assuming that variables are predetermined, lagged values provide instruments to absorb potential endogeneity between the number of bank failures and economic and regulatory conditions. Results suggest that bank failures are not merely self-fulfilling prophecies but relate systematically to inflation as well as to policy changes in banking regulation. Furthermore, in terms of statistical and economic significance, the distribution and development of bankruptcies across US states depends crucially on past bank failures suggesting that contagion provides an important channel through which banking crises emerge. |
| Date: | 2008–11 |
| URL: | http://d.repec.org/n?u=RePEc:szg:worpap:0804&r=ets |
| By: | Les Oxley, (University of Canterbury); Marco Reale; Granville Tunnicliffe Wilson |
| Abstract: | In this paper graphical modelling is used to select a sparse structure for a multivariate time series model of New Zealand interest rates. In particular, we consider a recursive structural vector autoregressions that can subsequently be described parsimoniously by a directed acyclic graph, which could be given a causal interpretation. A comparison between competing models is then made by considering likelihood and economic theory. |
| Keywords: | Graphical models; directed acyclic graphs; term structure; causality. |
| JEL: | E43 E44 C01 C32 |
| Date: | 2008–11–28 |
| URL: | http://d.repec.org/n?u=RePEc:cbt:econwp:08/19&r=ets |
| By: | Albrecht Ritschl; Samad Sarferaz; Martin Uebele |
| Abstract: | This paper presents insights on U.S. business cycle volatility since 1867 de- rived from diffusion indices. We employ a Bayesian dynamic factor model to obtain aggregate and sectoral economic activity indices. We find a remarkable increase in volatility across World War I, which is reversed after World War II. While we can generate evidence of postwar moderation relative to pre-1914, this evidence is not robust to structural change, implemented by time-varying factor loadings. We do find evidence of moderation in the nominal series, however, and reproduce the standard result of moderation since the 1980s. Our estimates broadly confirm the NBER historical business cycle chronology as well the National Income and Product Accounts, except for World War II where they support alternative estimates of Kuznets (1952). |
| Keywords: | U.S. business cycle, volatility, dynamic factor analysis |
| JEL: | N11 N12 C43 E32 |
| Date: | 2008–11 |
| URL: | http://d.repec.org/n?u=RePEc:hum:wpaper:sfb649dp2008-066&r=ets |
| By: | Thomas Flury; Neil Shephard |
| Abstract: | Suppose we wish to carry out likelihood based inference but we solely have an unbiased simulation based estimator of the likelihood. We note that unbiasedness is enough when the estimated likelihood is used inside a Metropolis-Hastings algorithm. This result has recently been intro- duced in statistics literature by Andrieu, Doucet, and Holenstein (2007) and is perhaps surprising given the celebrated results on maximum simulated likelihood estimation. Bayesian inference based on simulated likelihood can be widely applied in microeconomics, macroeconomics and financial econometrics. One way of generating unbiased estimates of the likelihood is by the use of a particle filter. We illustrate these methods on four problems in econometrics, producing rather generic methods. Taken together, these methods imply that if we can simulate from an economic model we can carry out likelihood based inference using its simulations. |
| Keywords: | Dynamic stochastic general equilibrium models, inference, likelihood, MCMC, Metropolis-Hastings, particle filter, state space models, stochastic volatility |
| JEL: | C11 C13 C15 C32 E32 |
| Date: | 2008 |
| URL: | http://d.repec.org/n?u=RePEc:sbs:wpsefe:2008fe32&r=ets |