How Important are Oil and Money Shocks in Explaining Housing Market Fluctuations in an Oil-exporting Country?: Evidence from Iran

This paper analyzes the effects of oil price and monetary shocks on the Iranian housing market in a Bayesian SVAR framework. The prior information for the contemporaneous identification of the SVAR model is derived from standard economic theory. To deal with uncertainty in the identification schemes, I calculate posterior model probabilities for the SVAR model identified by a different set of over-identification restrictions. In order to draw accurate inferences regarding the effectiveness of the shocks in an over-identified Bayesian SVAR, a Bayesian Monte Carlo integration method is applied. The findings indicate that oil price shocks explain a substantial portion of housing market fluctuations. Housing prices increase in response to a positive credit shock, but only with a noticeably smaller magnitude when compared with the response to a positive oil price shock.Housing market fluctuations, Oil price shocks, Credit shocks, Bayesian Structural VAR, Bayesian model averaging (BMA), Bayesian Monte Carlo integration method

Estimating Time-Varying Effective Connectivity in High-Dimensional fMRI Data Using Regime-Switching Factor Models

Recent studies on analyzing dynamic brain connectivity rely on sliding-window analysis or time-varying coefficient models which are unable to capture both smooth and abrupt changes simultaneously. Emerging evidence suggests state-related changes in brain connectivity where dependence structure alternates between a finite number of latent states or regimes. Another challenge is inference of full-brain networks with large number of nodes. We employ a Markov-switching dynamic factor model in which the state-driven time-varying connectivity regimes of high-dimensional fMRI data are characterized by lower-dimensional common latent factors, following a regime-switching process. It enables a reliable, data-adaptive estimation of change-points of connectivity regimes and the massive dependencies associated with each regime. We consider the switching VAR to quantity the dynamic effective connectivity. We propose a three-step estimation procedure: (1) extracting the factors using principal component analysis (PCA) and (2) identifying dynamic connectivity states using the factor-based switching vector autoregressive (VAR) models in a state-space formulation using Kalman filter and expectation-maximization (EM) algorithm, and (3) constructing the high-dimensional connectivity metrics for each state based on subspace estimates. Simulation results show that our proposed estimator outperforms the K-means clustering of time-windowed coefficients, providing more accurate estimation of regime dynamics and connectivity metrics in high-dimensional settings. Applications to analyzing resting-state fMRI data identify dynamic changes in brain states during rest, and reveal distinct directed connectivity patterns and modular organization in resting-state networks across different states.Comment: 21 page

How much structure in empirical models?

This chapter highlights the problems that structural methods and SVAR approaches have when estimating DSGE models and examining their ability to capture important features of the data. We show that structural methods are subject to severe identification problems due, in large part, to the nature of DSGE models. The problems can be patched up in a number of ways but solved only if DSGEs are completely reparametrized or respecified. The potential misspecification of the structural relationships give Bayesian methods an hedge over classical ones in structural estimation. SVAR approaches may face invertibility problems but simple diagnostics can help to detect and remedy these problems. A pragmatic empirical approach ought to use the flexibility of SVARs against potential misspecification of the structural relationships but must firmly tie SVARs to the class of DSGE models which could have have generated the data.DSGE models, SVAR models, Identification, Invertibility, Misspecification, Small Samples.

Inferring causal relations from multivariate time series : a fast method for large-scale gene expression data

Various multivariate time series analysis techniques have been developed with the aim of inferring causal relations between time series. Previously, these techniques have proved their effectiveness on economic and neurophysiological data, which normally consist of hundreds of samples. However, in their applications to gene regulatory inference, the small sample size of gene expression time series poses an obstacle. In this paper, we describe some of the most commonly used multivariate inference techniques and show the potential challenge related to gene expression analysis. In response, we propose a directed partial correlation (DPC) algorithm as an efficient and effective solution to causal/regulatory relations inference on small sample gene expression data. Comparative evaluations on the existing techniques and the proposed method are presented. To draw reliable conclusions, a comprehensive benchmarking on data sets of various setups is essential. Three experiments are designed to assess these methods in a coherent manner. Detailed analysis of experimental results not only reveals good accuracy of the proposed DPC method in large-scale prediction, but also gives much insight into all methods under evaluation

Modelling Italian potential output and the output gap

The aim of the paper is to estimate a reliable quarterly time-series of potential output for the Italian economy, exploiting four alternative approaches: a Bayesian unobserved component method, a univariate time-varying autoregressive model, a production function approach and a structural VAR. Based on a wide range of evaluation criteria, all methods generate output gaps that accurately describe the Italian business cycle over the past three decades. All output gap measures are subject to non-negligible revisions when new data become available. Nonetheless they still prove to be informative about the current cyclical phase and, unlike the evidence reported in most of the literature, helpful at predicting inflation compared with simple benchmarks. We assess also the performance of output gap estimates obtained by combining the four original indicators, using either equal weights or Bayesian averaging, showing that the resulting measures (i) are less sensitive to revisions; (ii) are at least as good as the originals at tracking business cycle fluctuations; (iii) are more accurate as inflation predictors.potential output, business cycle, Phillips curve, output gap

SOEPL 2009 – An Estimated Dynamic Stochastic General Equilibrium Model for Policy Analysis And Forecasting

The paper documents elements of work on the dynamic stochastic general equilibrium (DSGE) SOEPL model that has been carried out in recent years at the National Bank of Poland. In 2009 a new version of the model was developed (called SOEPL−2009) which in 2010 is to support an econometric model and experts’ forecasts in mid-term forecasting of inflation and economic activity. The paper consists of three basic parts. The first part is introductory and briefly outlines the development of macroeconometric methods which brought about the creation of new-keynesian models specified within the dynamic stochastic general equilibrium approach. The remaining two parts of the paper report specification, estimation results and some properties of the SOEPL−2009 DSGE model.

Interpreting the Hours-Technology time-varying relationship

We investigate the time varying relation between hours and technology shocks using a structural business cycle model. We propose an RBC model with a Constant Elasticity of Substitution (CES) production function that allows for capital- and labor-augmenting technology shocks. We estimate the model with Bayesian techniques. In the full sample, we find (i) evidence in favor of a less than unitary elasticity of substitution (rejecting Cobb-Douglas) and (ii) a sizable role for capital augmenting shock for business cycles fluctuations. In rolling sub-samples, we document that the transmission of technology shocks to hours worked has been varying over time. We argue that this change is due to the increase of the elasticity of factor substitution. That is, labor and capital became less complementary throughout the sample inducing a change in the sign and size of the response of hours. We conjecture that this change may have been induced by a change in the skill composition of the labor input.Hours Worked and Business Cycles, Bayesian Methods.

Structural vector autoregressions: theory of identification and algorithms for inference

Structural vector autoregressions (SVARs) are widely used for policy analysis and to provide stylized facts for dynamic general equilibrium models. Yet there have been no workable rank conditions to ascertain whether an SVAR is globally identified. When identifying restrictions such as long-run restrictions are imposed on impulse responses, there have been no efficient algorithms for small-sample estimation and inference. To fill these important gaps in the literature, this paper makes four contributions. First, we establish general rank conditions for global identification of both overidentified and exactly identified models. Second, we show that these conditions can be checked as a simple matrix-filling exercise and that they apply to a wide class of identifying restrictions, including linear and certain nonlinear restrictions. Third, we establish a very simple rank condition for exactly identified models that amounts to a straightforward counting exercise. Fourth, we develop a number of efficient algorithms for small-sample estimation and inference.Vector autoregression

The Macroeconomic Effects of Fiscal Policy in Portugal: a Bayesian SVAR Analysis

In the last twenty years Portugal struggled to keep public finances under control, notably in containing primary spending. We use a new quarterly dataset covering 1979:1-2007:4, and estimate a Bayesian Structural Autoregression model to analyze the macroeconomic effects of fiscal policy. The results show that positive government spending shocks, in general, have a negative effect on real GDP; lead to important “crowding-out” effects, by impacting negatively on private consumption and investment; and have a persistent and positive effect on the price level and the average cost of financing government debt. Positive government revenue shocks tend to have a negative impact on GDP; and lead to a fall in the price level. The evidence also shows the importance of explicitly considering the government debt dynamics in the model. Finally, a VAR counter-factual exercise confirms that unexpected positive government spending shocks lead to important “crowding-out” effects..B-SVAR, fiscal policy, debt dynamics, Portugal.

The Macroeconomic Effects of Fiscal Policy in Portugal: a Bayesian SVAR Analysis

In the last twenty years Portugal struggled to keep public finances under control, notably in containing primary spending. We use a new quarterly dataset covering 1979:1-2007:4, and estimate a Bayesian Structural Autoregression model to analyze the macroeconomic effects of fiscal policy. The results show that positive government spending shocks, in general, have a negative effect on real GDP; lead to important "crowding-out" effects, by impacting negatively on private consumption and investment; and have a persistent and positive effect on the price level and the average cost of financing government debt. Positive government revenue shocks tend to have a negative impact on GDP; and lead to a fall in the price level. The evidence also shows the importance of explicitly considering the government debt dynamics in the model. Finally, a VAR counter-factual exercise confirms that unexpected positive government spending shocks lead to important "crowding-out" effects.B-SVAR, fiscal policy, debt dynamics, Portugal.