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The Factor-augmented Error-Correction Model (FECM) generalizes the factor-augmented VAR (FAVAR) and the Error-Correction Model (ECM), combining error-correction, cointegration and dynamic factor models. It uses a larger set of variables compared to the ECM and incorporates the long-run information lacking from the FAVAR because of the latter’s specification in differences. In this paper, we review the specification and estimation of the FECM, and illustrate its use for forecasting and structural analysis by means of empirical applications based on Euro Area and US data.

In the context of Dynamic Factor Models, we compare point and interval estimates of the underlying unobserved factors extracted using small- and big-data procedures. Our paper differs from previous works in the related literature in several ways. First, we focus on factor extraction rather than on prediction of a given variable in the system. Second, the comparisons are carried out by implementing the procedures considered to the same data. Third, we are interested not only on point estimates but also on confidence intervals for the factors. Based on a simulated system and the macroeconomic data set popularized by Stock and Watson (2012), we show that, for a given procedure, factor estimates based on different cross-sectional dimensions are highly correlated. On the other hand, given the cross-sectional dimension, the maximum likelihood Kalman filter and smoother factor estimates are highly correlated with those obtained using hybrid procedures. The PC estimates are somehow less correlated. Finally, the PC intervals based on asymptotic approximations are unrealistically tiny.

Might the impact of the global economic policy uncertainty (GEPU) and the long-term bond yields on oil prices be asymmetric? This paper aims to consider the effects of the GEPU and the US long-term government bond yields on oil prices using quantile-based analysis and nonlinear vector autoregression (VAR) model. The author hypothesized whether the negative and positive changes in the GEPU and the long-term bond yields of the USA have different effects on oil prices.

To address this question, the author uses quantile cointegration model and the impulse response functions (IRFs) of the censored variable approach of Kilian and Vigfusson (2011).

The quantile cointegration test showed the existence of non-linear cointegration relationship, whereas Granger-causality analysis revealed that positive/negative variations in GEPU will have opposite effects on oil prices. This result was supported by the quantile regression model’s coefficients and nonlinear VAR model’s IRFs; more specifically, it was stressed that increasing/decreasing GEPU will deaccelerate/accelerate global economic activity and thus lead to a fall/rise in oil prices. On the other hand, the empirical models indicated that the impact of US 10-year government bond yields on oil prices is asymmetrical, while it was found that deterioration in the borrowing conditions in the USA may have an impact on oil prices by slowing down the global economic activity.

As a robustness check of the quantile-based analysis results, the slope-based Mork test is used.

This chapter contributes to the growing global VAR (GVAR) literature by showing how global and national shocks can be identified within a GVAR framework. The usefulness of the proposed approach is illustrated in an application to the analysis of the interactions between public debt and real output growth in a multicountry setting, and the results are compared to those obtained from standard single country VAR analysis. We find that on average (across countries) global shocks explain about one-third of the long-horizon forecast error variance of output growth, and about one-fifth of the long-run variance of the rate of change of debt-to-GDP. Evidence on the degree of cross-sectional dependence in these variables and their innovations are exploited to identify the global shocks, and priors are used to identify the national shocks within a Bayesian framework. It is found that posterior median debt elasticity with respect to output is much larger when the rise in output is due to a fiscal policy shock, as compared to when the rise in output is due to a positive technology shock. The cross-country average of the median debt elasticity is 1.45 when the rise in output is due to a fiscal expansion as compared to 0.76 when the rise in output follows from a favorable output shock.

This paper seeks to assess the impact of monetary policy on house price inflation for the nine census divisions of the US economy.

A factor‐augmented VAR (FAVAR) model is estimated using a large data set comprising of 126 quarterly series over the period 1976:01 to 2005:02.

Overall, the results of this investigation show that house price inflation responds negatively to a positive monetary policy shock, suggesting that the framework does not experience the widely observed price puzzle encountered while analyzing monetary policy shocks with standard sized VARs.

The paper only considers house price inflation and ignores other housing market variables. Moreover, given the recent economy‐wide decline in the house price growth rates, it would be worthwhile to update the data set to a more recent period, to capture the possible breakdown in the relationship of house prices with fundamentals driving the market.

The results based on the impulse response functions indicate that, in general, house price inflation responds negatively to monetary policy shock, but the responses are heterogeneous across the census divisions. In addition, the findings suggest, in particular, the importance of South Atlantic, East South Central, West South Central, Mountain and the Pacific divisions in shaping the dynamics of US house price inflation.

To the best of one's knowledge, this is the first paper to analyze the effect of monetary policy on house price inflation in the nine census divisions of the US economy using a FAVAR model.

The purpose of this paper is to examine the effects of exchange rate shock on the broad spectrum of the US economy using a factor-augmented VAR model (FAVAR).

The authors developed a two-factor FAVAR model and estimated it with the single-step Bayesian likelihood approach using the Gibbs sampling technique. The two factors represented, respectively, the economic activity and price pressures. The exchange rate shock was identified with the Choleski decomposition method for VARs. The authors used the data of 117 time series for the period from 1973:02 to 2007:12. Impulse responses and variance decompositions were computed as the main results.

The authors found that exchange rate shock has pervasive effects on the US economy as the following: depreciation does not appear to help reduce the US trade deficit as both import and export rise with the depreciation shock; in the short run, depreciation appears expansionary as industrial production, manufacturing and employment all increase within a year; in the medium run, depreciation appears inflationary, as consumer price, producer price, import price and export price all increase; and in the medium run, depreciation appears contractionary as personal consumption, consumer confidence, stock price and housing start tend to fall.

Some caveats remain: first, our simple model symmetrically estimates depreciation shock and appreciation shock and, hence, cannot draw inferences for how exchange rate appreciation and depreciation may affect the US economy asymmetrically. Second, the simple model used did not distinguish the different possible sources of exchange rate depreciation shock, the knowledge of which may lead to richer policy implications and is the direction of research for the future.

This research contributes to the literature of whether exchange rate is expansionary or contractionary to the US economy using the FAVAR model. This is the first comprehensive study in the literature studying the pervasive effects of the exchange rate on the broad spectrum of the US economy in one integrated model.

The purpose of this paper is to examine financial integration across North American stock markets from January 1984 to December 2003.

The paper uses an arbitrage pricing theory framework. The risk factors considered are the three Fama and French factors augmented with momentum for both countries as well as their international counterparts. Both the domestic and international four factor models in cross section and test for partial, mild, and strong financial integration are estimated. The domestic and international model are estimated on domestic portfolios and on a subset of Canadian cross listings matched with American stocks.

Results can be summarized as follows: first, results show stronger evidence of mild rather than partial or strong integration in both domestic portfolios and interlisted stocks. Second, interlisted stocks appear at first glance to be more integrated than the domestic portfolios, but this result can be attributed to the poor explanatory power of the models applied to interlisted stocks. Once the authors rule out the case where the model does not generate statistically important risk premiums for both countries, the evidence of integration is similar in both domestic and interlisted stocks. Third, the domestic and international models have similar explanatory power, although the domestic model performs better with the Canadian interlisted stocks are found.

The results suggest that, in an international context, a portfolio manager is better off using the four factor model as a benchmark in cross sections rather than the single market. Furthermore, if the agency problem described in Karolyi is ignored, Canadian interlisted stocks and Canadian domestic portfolios have the same diversification potential.

This paper aims to empirically examine how shocks to monetary policy measures (the short-term nominal interest rate and broad money supply) affect macroeconomic aggregates, namely, output growth of the economy, national price levels and the nominal exchange rate.

Johansen’s (1995) cointegration technique and error correction models are used to explore the long-run relationship among variables. To investigate how macroeconomic aggregates respond to a one-standard deviation shock to the underlying monetary measures, the authors estimate impulse response functions based on error correction models. The study uses quarterly data covering the period 1980-2009.

The results provide evidence that there is a long-run stable relationship between the authors' monetary measures and the underlying macroeconomic aggregates. They also find that the industrial production adjusts at a faster speed relative to commodity prices and the exchange rate over the examined period. Further, they show that the short-term interest rate has relatively stronger effects on output as compared to broad money supply, whereas prices and exchange rates adjust more quickly to their long-run equilibrium when money supply is used as a measure of monetary policy. Finally, the authors find significant evidence of a price puzzle regardless of whether they consider a closed or an open economy case. However, an initial appreciation of exchange rate is observed in response to a one-standard deviation shock to money supply, indicating the overshooting hypothesis phenomenon.

The findings of the analysis suggest that the interest rate-oriented monetary policy is more effective when the monetary authorities’ objective is to enhance the output growth of the economy. However, in case of inflation targeting, the broad money supply seems a more appropriate instrument. Our findings also suggest that the monetary policy has a significant role in stabilizing both real and nominal sectors of the economy.

The main value of this paper is to examine the significance of monetary policy for a developing and relatively small open economy, namely, Pakistan. The authors use the error correction model, which improves the estimation by accounting for the long-run association. They also take into account the world oil prices by including the world commodity price index as a control variable in their empirical investigation. Finally, they utilize quarterly data rather than annual, and they cover a relatively recent sample period.