Forecasting with a state space time-varying parameter VAR model: Evidence from the Euro area

Standard VAR and Bayesian VAR models are proven to be reliable tools for modeling and forecasting, yet they are still linear and they do not consider time-variation in parameters. VAR modeling is subject to the Lucas critique and fails to take into account the inherent nonlinearities of the economy, while it can only be utilized in the analysis of stationary series and in many cases stationarity assumptions are too restrictive. A novel time-varying multivariate state-space estimation method for vector autoregression models is introduced. For the time-varying parameter model (TVP-VAR), the parameters are estimated using a multivariate specification of the standard Kalman filter (Harvey, 1990) combined with a suitable extension of the univariate methodology framework of Kim and Nelson (1999). The TVP-VAR model as well as standard VARs and Bayesian VARs, are used in a comparative investigation of their predicting performance for the monthly IP, CPI and Euribor rate of the EU economy. The total period covers 1999:1–2011:2 with an out-of-sample testing period of 2007:2 to 2011:2, which included the US sub-prime and the EU debt crisis sub-periods. The results varied across the investigated time series and indicated that the TVP-VAR model consistently outperforms the other models in case of the EU monthly CPI, while different specifications of the VAR and BVAR models for the IP and Euribor series provide with better forecasting performance. Interestingly, the robustness analysis showed that the TVP-VAR model provided with enhanced predictability in particular during “crisis times”.     

Monetary–fiscal policy interaction and fiscal inflation: A tale of three countries

We study the impact of the interaction between fiscal and monetary policy on the low-frequency relationship between the fiscal stance and inflation using cross-country data from 1965 to 1999. In a first step, we contrast the monetary–fiscal narrative for Germany, the U.S., and Italy with evidence obtained from simple regression models and a time-varying VAR. We find that the low-frequency relationship between the fiscal stance and inflation is low during periods of an independent central bank and responsible fiscal policy and more pronounced in times of non-responsible fiscal policy and accommodative monetary authorities. In a second step, we use an estimated DSGE model to interpret the low-frequency measure structurally and to illustrate the mechanisms through which fiscal actions affect inflation in the long run. The findings from the DSGE model suggest that switches in the monetary–fiscal policy interaction and accompanying variations in the propagation of structural shocks can well account for changes in the low-frequency relationship between the fiscal stance and inflation.     

A DSGE-VAR model for forecasting key South African macroeconomic variables

The paper develops a Small Open Economy New Keynesian DSGE-VAR (SOENKDSGE-VAR) model of the South African economy, characterised by incomplete pass-through of exchange rate changes, external habit formation, partial indexation of domestic prices and wages to past inflation, and staggered price and wage setting. The model is estimated using Bayesian techniques on data from the period 1980Q1 to 2003Q2, and then used to forecast output, inflation and nominal short-term interest rate for one-to eight-quarters-ahead over an out-of sample horizon of 2003Q3 to 2010Q4. When the forecast performance of the SOENKDSGE-VAR model is compared with an independently estimated DSGE model, the classical VAR and six alternative BVAR models, we find that, barring the BVAR model based on the SSVS prior on both VAR coefficients and the error covariance, the SOENKDSGE-VAR model is found to perform competitively, if not, better than all the other VAR models.     

Idiosyncratic and international transmission of shocks in the G7: Does EMU matter?

This paper analyzes the transmission of shocks across the Group of Seven industrialized countries (G7) before and after the introduction of the euro. We estimate global vector autoregressive (VAR) models for different periods to investigate changes in the domestic and international adjustment of macroeconomic variables following supply, demand, and nominal shocks. The shocks are identified with robust sign restrictions, which we derive from a small open economy dynamic stochastic general equilibrium (DSGE) model. Specifically, we analyze the adjustment of output, inflation, and the real effective exchange rate following those shocks. Our results indicate that changes in the adjustment are due to global convergence rather than to regional‐specific convergence.     

Time varying structural VARs with sign restrictions: The case of Taiwan

I apply a Bayesian approach to a time-varying structural vector autoregression model with stochastic volatility (TVP-SVAR-SV) to study the time-varying nature of the Taiwanese economy. In particular, the structural parameters are identified via the sign information in a three-variable VAR system. The estimated results show that TVP-SVAR-SV model has the best fit to the data, compared to the time-varying parameters VAR model with constant volatility and a classical VAR model with constant parameters and volatilities. Moreover, I find the time-varying contemporaneous relationship between the output growth and inflation rates, particularly significant before the year 2000. Lastly, the impulse responses and the volatilities of all the variables are found to be time-varying.     

Analysing the effects of fiscal policy shocks in the South African economy

This paper is the first one to analyse the effect of aggregate government spending and taxes on output for South Africa using three types of a calibrated DSGE model and more data driven models such as a structural vector error correction model (SVECM) and a time-varying parameter VAR (TVP-VAR) to capture possible asymmetries and time variation of fiscal impulses. The impulse responses indicate first, that increases in government expenditure have a positive impact, albeit (at times) less than unity, on GDP in the short run; second, over the long run, the impact of government expenditure on GDP is insignificant; and third, increases in taxes decrease GDP over the short run, while having negligible effects over longer horizons.     

TECHNOLOGY SHOCKS AND ROBUST SIGN RESTRICTIONS IN A EURO AREA SVAR*

We use a model‐based identification strategy to estimate the impact of technology shocks on hours worked and employment in the euro area. The sign restrictions applied in the vector autoregression (VAR) analysis are consistent with a large class of dynamic stochastic general equilibrium (DSGE) models and are robust to parameter uncertainty. The results are in line with the conventional Real Business Cycle (RBC) interpretation that hours worked rise as a result of a positive technology shock. By comparing the sign restrictions method to the long‐run restriction approach of Galí (Quaterly Journal of Economics (1992) 709–38) , we show that the results do not depend on the stochastic specification of the hours worked series or the data sample but only on the identification scheme.     

A small-scale DSGE-VAR model for the Romanian economy

This paper describes the theoretical structure and the estimation results for a DSGE-VAR model for the Romanian economy, an inflation targeting country since 2005. Having as benchmark the New-Keynesian model of Rabanal and Rubio-Ramirez (2005), the main additional feature introduced refers to the extension to a small open economy setting in order to account for this specific aspect of the Romanian economy.Within the inflation targeting monetary policy regime, forecasts of central macro variables, inflation in particular, play an important part. Because inflation reacts to monetary measures with a considerable lag, the central bank's policy has to be forward-looking. Based on univariate measures of forecast performance, it is shown that the VAR with DSGE model prior produces forecasts that improve on those obtained using an unrestricted VAR model and the popular Minnesota prior in case of inflation, real exchange rate and nominal interest rate. Moreover, the DSGE-VAR model is informative about the structure of the economy and can help the “story-telling” in the central banks.     

Forecasting house prices for the four census regions and the aggregate US economy in a data-rich environment

This article considers the ability of large-scale (involving 145 fundamental variables) time-series models, estimated by dynamic factor analysis and Bayesian shrinkage, to forecast real house price growth rates of the four US census regions and the aggregate US economy. Besides the standard Minnesota prior, we also use additional priors that constrain the sum of coefficients of the VAR models. We compare 1- to 24-months-ahead forecasts of the large-scale models over an out-of-sample horizon of 1995:01–2009:03, based on an in-sample of 1968:02–1994:12, relative to a random walk model, a small-scale VAR model comprising just the five real house price growth rates and a medium-scale VAR model containing 36 of the 145 fundamental variables besides the five real house price growth rates. In addition to the forecast comparison exercise across small-, medium- and large-scale models, we also look at the ability of the ‘optimal’ model (i.e. the model that produces the minimum average mean squared forecast error) for a specific region in predicting ex ante real house prices (in levels) over the period of 2009:04 till 2012:02. Factor-based models (classical or Bayesian) perform the best for the North East, Mid-West, West census regions and the aggregate US economy and equally well to a small-scale VAR for the South region. The ‘optimal’ factor models also tend to predict the downward trend in the data when we conduct an ex ante forecasting exercise. Our results highlight the importance of information content in large number of fundamentals in predicting house prices accurately.     

Forecasting the Australian economy with DSGE and BVAR models

Reflecting the importance of commodities for the Australian economy, we construct a dynamic stochastic general equilibrium (DSGE) model of the Australian economy with a commodity sector. We assess whether its forecasts can be improved by using it as a prior for an empirical Bayesian vector autoregression (BVAR). We find that the forecasts from the BVAR tend to be more accurate than those from the DSGE model. Nevertheless, for output growth these forecasts do not outperform benchmark models, such as a small open economy BVAR estimated using the standard priors for forecasting. A Bayesian factor augmented vector autoregression produces the most accurate near-term inflation forecasts.     

Macroeconomic spillover effects of the Chinese economy

The slowdown of economy and widening of domestic imbalances in China bothers economists and politicians across the globe. We estimate the influence of a negative output shock in China on a number of different economies. We concentrate on China's neighboring countries. We compare the results from the Global VAR model and from the Bayesian VAR models. Also using Bayesian model averaging we search for determinants of Chinese spillovers for the global economy. We find that spillovers are stronger to economies with less flexible exchange rates, a higher share of manufacturing in gross value added and to economies which are larger.     

DSGE model-based forecasting of modelled and nonmodelled inflation variables in South Africa

Inflation forecasts are a key ingredient for monetary policy-making – especially in an inflation targeting country such as South Africa. Generally, a typical Dynamic Stochastic General Equilibrium (DSGE) only includes a core set of variables. As such, other variables, for example alternative measures of inflation that might be of interest to policy-makers, do not feature in the model. Given this, we implement a closed-economy New Keynesian DSGE model-based procedure which includes variables that do not explicitly appear in the model. We estimate such a model using an in-sample covering 1971Q2 to 1999Q4 and generate recursive forecasts over 2000Q1 to 2011Q4. The hybrid DSGE performs extremely well in forecasting inflation variables (both core and nonmodelled) in comparison with forecasts reported by other models such as AR(1). In addition, based on ex-ante forecasts over the period 2012Q1–2013Q4, we find that the DSGE model performs better than the AR(1) counterpart in forecasting actual GDP deflator inflation.     

Raising an inflation target: The Japanese experience with Abenomics

This paper draws from Japan׳s recent monetary experiment to examine the effects of an increase in the inflation target during a liquidity trap. We review Japanese data and examine through a VAR model how macroeconomic variables respond to an identified inflation target shock. We apply these findings to calibrate the effect of a shock to the inflation target in a new-Keynesian DSGE model of the Japanese economy. We argue that imperfect observability of the inflation target and a separate exchange rate shock are needed to successfully account for the behavior of nominal and real variables in Japan since late 2012. Our analysis indicates that Japan has made some progress towards overcoming deflation, but further measures are needed to raise inflation to 2 percent in a stable manner.     

Modest macroeconomic effects of monetary policy shocks during the great moderation: An alternative interpretation

Cholesky-VAR impulse responses estimated with post-1984 U.S. data predict modest macroeconomic reactions to monetary policy shocks. We interpret this evidence by employing an estimated medium-scale DSGE model of the business cycle as a Data-Generating Process in a Monte Carlo exercise in which a Cholesky-VAR econometrician is asked to estimate the effects of an unexpected, temporary increase in the policy rate. Our structural DSGE model predicts conventional macroeconomic reactions to a policy shock. In contrast, our Monte Carlo VAR results replicate our evidence obtained with actual U.S. data. Hence, modest macroeconomic effects may very well be an artifact of Cholesky-VARs. A combination of supply and demand shocks may be behind the inability of Cholesky-VARs to replicate the actual macroeconomic responses. The difference in the VAR responses obtained with Great Inflation vs. Great Moderation data may be due to instabilities in the parameters related to households’ and firms’ programs, more than to a more aggressive systematic monetary policy. A Monte Carlo assessment of sign restrictions as an alternative identification strategy is also proposed.     

Fiscal stimuli in the form of job creation subsidies

This paper examines the effects of fiscal stimuli in the form of job creation subsidies in a DSGE model with search friction and endogenous job separation. We consider two types of job creation subsidies: a subsidy for the cost of posting vacancies and a hiring subsidy. This paper finds that the effects of job creation subsides on unemployment differ between models with and without endogenous job separation. While a positive job creation subsidy shock lowers unemployment in a model without endogenous job separation, it increases unemployment in a model with endogenous job separation. We also find that while qualitatively the effects of a vacancy cost subsidy on the economy are similar to those of a hiring subsidy, quantitatively they are different.     

Output gaps

What is the output gap? I discuss three alternative definitions: the deviation of output from its long-run stochastic trend (i.e., the “Beveridge–Nelson cycle”); the deviation of output from the level consistent with current technologies and normal utilization of capital and labor input (i.e., the “production-function approach”); and the deviation of output from “flexible-price” output (i.e., its “natural rate”). Estimates of each concept are presented from a dynamic–stochastic–general-equilibrium (DSGE) model of the U.S. economy used at the Federal Reserve Board. Four points are emphasized: The DSGE model’s estimate of the gap (for each definition) is very similar to gaps from policy institutions, but the model’s estimate of potential growth has a higher variance and substantially different covariance with GDP growth; the change in the Beveridge–Nelson trend covaries negatively with the change in the gap in the DSGE model, providing a structural model estimate of a controversial parameter; in this model, estimates of the natural-rate concept are similar to those based on the Beveridge–Nelson and production function approaches; and the estimate of the output gap, irrespective of definition, is closely related to unemployment fluctuations.     

Financial crises and time-varying risk premia in a small open economy: a Markov-switching DSGE model for Estonia

Under a currency board, the central bank relinquishes control over its monetary policy and domestic interest rates converge towards the foreign rates. Nevertheless, a spread between both usually remains. This spread can be persistently positive due to elevated risk in the economy. This paper models that feature by building a DSGE model with a currency board, where the domestic interest rate is endogenously derived as a function of the foreign rate, the external debt position and an exogenous risk premium component. Time variation in the volatility of the risk premium component is then modelled via a Markov-switching component. Estimating the model with Bayesian methods and Estonian data shows that the economy does not react much to shocks to domestic interest rates in quiet times but is much more sensitive during crises, and matches the financial and banking crises, which cannot be captured by the standard DSGE model.     

The effects of monetary policy using structural factor analysis

The traditional Vector Autoregression (VAR) method is widely used to trace out the effects of monetary policy innovations on the economy. However, this method suffers from the curse of dimensionality, so that in practice VARs are estimated on a limited number of variables, leading to a potential missing information problem. In this article we use the method of structural factor analysis to evaluate the effects of monetary policy on key macroeconomic variables in a data rich environment. This methodology allows us to extract information on monetary policy and its impact on the economy from a much larger data set than is possible with the traditional VAR method. We propose two structural factor models. One is the Structural Factor Augmented Vector Autoregressive (SFAVAR) model and the other is the Structural Factor Vector Autoregressive (SFVAR) model. Compared to the traditional VAR, both models incorporate information from hundreds of data series, series that can be and are monitored by the central bank in setting policy. Moreover, the factors used are structurally meaningful, a feature that adds to the understanding of the ‘black box’ of the monetary transmission mechanism. Both models generate qualitatively reasonable impulse response functions. For the SFVAR model, both the price puzzle and the liquidity puzzle are eliminated.     

Monetary Regime Choice and Optimal Credit Rationing at the Official Rate: The Case of Russia

ABSTRACT

Stabilizing monetary policy in a small open economy is constrained by the open economy trilemma. In this paper, we investigate whether foreign exchange market interventions and the Central Bank’s credit rationing at the official rate (CROR) may soften this constraint and improve the results of monetary policy for different monetary regimes. We construct a dynamic stochastic general equilibrium (DSGE) model appropriate for analyzing the forward-looking behavior of households facing non-zero probabilities of losing access to financial market and CROR. We have found significant credit rationing in the quarterly Russian data of 2001:Q1–2014:Q2. The probability of losing access to financial market and the probability of CROR are estimated as 22% and 66%, respectively. Using Russian data of 2001:Q1–2014:Q2 we demonstrate that CROR provoked forward-looking activity in financial market, which led to more Ruble devaluation in the crises of 2008–2009. It improved poor countercyclical performance of two Russian monetary policy rules, whereas made small effect on welfare. Welfare maximization exercises reveal a tradeoff between low-inflation and high-welfare solutions and favor of a floating exchange rate regime. We found the optimal value of the probability of CROR in both exchange rate-based and Taylor rule-based models but resulting improvement in welfare is very small.     

The limited usefulness of macroeconomic Bayesian VARs when estimating the probability of a US recession

The Bayesian VAR model provides a convenient tool for generating predictive densities and making probability statements regarding the future development of economic variables. This paper investigates the usefulness of standard macroeconomic Bayesian VAR models to estimate the probability of a US recession. Defining a recession as two quarters in a row of negative GDP growth, the probability is estimated for two quarters of the most recent US recession, namely 2008Q3–2008Q4. In contrast to judgemental probabilities from this point in time, it is found that the BVAR assigns a very low probability to such an event. This is true also when survey data, which generally are considered as good leading indicators, are included in the models. We conclude that while Bayesian VAR models are good forecasting tools in many cases, the results in this paper raise question marks regarding their usefulness for predicting recessions.