Macroeconomic Time Series Research Articles

Deep Learning in Asset Pricing

We use deep neural networks to estimate an asset pricing model for individual stock returns that takes advantage of the vast amount of conditioning information, while keeping a fully flexible form and accounting for time-variation. The key innovations are to use the fundamental no-arbitrage condition as criterion function, to construct the most informative test assets with an adversarial approach and to extract the states of the economy from many macroeconomic time series. Our asset pricing model outperforms out-of-sample all benchmark approaches in terms of Sharpe ratio, explained variation and pricing errors and identifies the key factors that drive asset prices.

Boosting nonlinear predictability of macroeconomic time series

We apply the boosting estimation method in order to investigate to what extent and at what horizons macroeconomic time series have nonlinear predictability that comes from their own history. Our results indicate that the U.S. macroeconomic time series have more exploitable nonlinear predictability than previous studies have found. On average, the most favorable out-of-sample performance is obtained via a two-stage procedure, where a conventional linear prediction model is fitted first and the boosting technique is applied to build a nonlinear model for its residuals.

Wisdom of Malaysia’s Guided Economic Globalization in the Milieu of Today’s Capitalist Globalization

Capitalist globalization is a project of market dogmatism founded on the Darwinist social philosophy of ‘survival of the fittest’. In the political economy of the world with unequal nations and imperfect competition, it is a suspect of deceptive game for the developing countries. This paper is an attempt to analyze the wisdom of Malaysia’s guided economic globalization strategy using the technique of log-linear pooled ordinary least square regression and related macroeconomic time series data for the period 1982-2016. The empirical results suggest that Malaysia’s guided globalization strategy has been found successful in achieving most of its national macroeconomic goals. But in terms of international economic competitiveness the success has been only modest, its per capita income ratios with its two most important trading and development partners - USA and Japan - are found decreasing steadily having a catching up phenomenon along the path of income convergence. However, its per capita income gaps with them in absolute term are found still large and widening to suggest that developing countries must pursue globalization very cautiously and selectively.

Generalized Forecast Averaging in Autoregressions with a Near Unit Root

Summary This paper develops a new approach to forecasting a highly persistent time series that employs feasible generalized least squares (FGLS) estimation of the deterministic components in conjunction with Mallows model averaging. Within a local-to-unity asymptotic framework, we derive analytical expressions for the asymptotic mean squared error and one-step-ahead mean squared forecast risk of the proposed estimator and show that the optimal FGLS weights are different from their ordinary least squares (OLS) counterparts. We also provide theoretical justification for a generalized Mallows averaging estimator that incorporates lag order uncertainty in the construction of the forecast. Monte Carlo simulations demonstrate that the proposed procedure yields a considerably lower finite-sample forecast risk relative to OLS averaging. An application to U.S. macroeconomic time series illustrates the efficacy of the advocated method in practice and finds that both persistence and lag order uncertainty have important implications for the accuracy of forecasts.

Macroeconomic uncertainty and forecasting macroeconomic aggregates

Abstract Can information on macroeconomic uncertainty improve the forecast accuracy for key macroeconomic time series for the US? Since previous studies have demonstrated that the link between the real economy and uncertainty is subject to nonlinearities, I assess the predictive power of macroeconomic uncertainty in both linear and nonlinear Bayesian VARs. For the latter, I use a threshold VAR that allows for regime-dependent dynamics conditional on the level of the uncertainty measure. I find that the predictive power of macroeconomic uncertainty in the linear VAR is negligible. In contrast, using information on macroeconomic uncertainty in a threshold VAR can significantly improve the accuracy of short-term point and density forecasts, especially in the presence of high uncertainty.

A Practitioner's Guide and MATLAB Toolbox for Mixed Frequency State Space Models

The use of mixed frequency data is now common in many applications, ranging from the analysis of high frequency financial time series to large cross-sections of macroeconomic time series. In this article, we show how state space methods can easily facilitate both estimation and inference in these settings. After presenting a unified treatment of the state space approach to mixed frequency data modeling, we provide a series of applications to demonstrate how our MATLAB toolbox can make the estimation and post-processing of these models straightforward.

What drives the FOMC’s dot plots?

The Federal Open Market Committee (FOMC) releases quarterly its members’ views about what federal funds rate will be appropriate at the end of the current and the next two or three years, and in the “longer run.” We construct constant horizon interest rate projections one, two and three years ahead and use real-time data on 32 important macroeconomic time series to study how these variables impact on the FOMC’s interest-rate setting. News regarding the labour market is particularly important at all horizons. At longer horizons, financial market, trade and output news also matter.

Business cycle asymmetry: evidence from Japan’s decade-long deflation

ABSTRACT This paper empirically examines the asymmetric behaviour of macroeconomic time series under different economic regimes, focusing especially on whether the economy is in a deflationary or non-deflationary regime, using a threshold vector autoregression model (TVAR). The results provide evidence in favour of asymmetric spillover effects from demand shocks on economic variables such as wages and consumption. In contrast to the non-deflationary period, during the deflationary period demand shocks are not clearly transmitted to the economy overall due mainly to the rigidity of nominal wages. The findings highlight that the state of the economy also matters when we consider the nature of the business cycle.

Modelování makroekonomických agregátů české a slovenské ekonomiky pomocí var modelů

The aim of the paper is to analyse, model and compare selected macroeconomic variables of the Czech and Slovak economies and their dynamics using VAR models. This article shows an application of a selected model on real-time series of chosen macroeconomic indicators using four variables (R - interest rate, M - money supply (M2), P - price level (CPI), Y - GDP). We identify and test two long-run relationships. The following hypotheses have been confirmed: H1: a change in monetary aggregate affects a change in price level and a change in economic growth; H2: there is a causal relationship between GDP and M2; H3: there is a direct correlation between interest rates and the price level anticipated in the Fisher equation. The following methods are used: Granger causality, impulse response function, cointegration and error correction models. In the end, econometric models of macroeconomic time series are compared in the Czech and Slovak economies. The calculations used EViews software.

Uncertainty Shocks and Business Cycle Research

We review the literature on uncertainty shocks and business cycle research. First, we motivate the study of uncertainty shocks by documenting the presence of time-variation in the volatility of macroeconomic time series. Second, we enumerate the mechanisms that researchers have postulated to link uncertainty shocks and business cycles. Third, we outline how we can specify uncertainty shocks. Fourth, we postulate a real business cycle model augmented with financial frictions and uncertainty shocks. Fifth, we use the model to illustrate our previous discussions and to show how uncertainty shocks can be expansionary. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

An Algorithm of Decomposing the Trend and Cyclical Components of FDI Inflows: The Case of Ukraine

Authors: Bogdan A. Moskalenko, ORCID: https://orcid.org/0000-0003-3972-1705 Joint stock company “ProCredit Bank”, Business Client Advisor, Kyiv, Ukraine Pavlin Mitev, ORCID: https://orcid.org/0000-0001-5798-4192 Joint stock company “Raiffeisenbank EAD”, Credit Risk Policy Manager, Bulgaria Pages: 95-101 Language: English DOI: https://doi.org/10.21272/fmir.4(3).95-101.2020 Download: Views: Downloads: 26 7 Abstract The article summarizes the arguments within the scientific challenge on improving approaches to country investment potential evaluation. The main objective of the research is to systematize the existing statistical methods of decomposing macroeconomic time series into growth (trend) and cyclical components. Systematization of theoretical and methodological materials on solving the problem of decomposing the trend and cyclical components of time data series showed that the use of filtering series of economic dynamics based on the Hodrick-Prescott filter allows identifying long-term growth trends or recessions. The relevance of solving this problem is that the country investment potential evaluation is often based on investigating the impact of foreign direct investment`s determinants in a domestic economy while ignoring cyclical macroeconomic processes within and outside the country, on which those determinants often have not responded yet or reacted late. The methodical tools of the research are carried out in the following logical sequence: systematization of existing statistical methods for trend component decomposing; analysis of data that will be used in the decomposition process and in further country investment potential evaluation; application of the Hodrick-Prescott filter and trend component decomposing in foreign direct investment net inflows dynamics into the economy of Ukraine. The Research methods combine in following dimensions: comparative analysis, regression analysis and univariate methodology of time series decomposing. The period from 1999 to 2019 was chosen as the research period. The object of the research is foreign direct investment net inflows into the economy of Ukraine, as they are the determining element within the country investment potential evaluation process. The article presents the results of empirical analysis, which showed that the decomposing a trend and cyclical components of foreign direct investment inflows can improve the quality of investment potential evaluation, considering the impact of current economic cycle phase. The results of the research can be useful for a more accurate investment potential evaluation on the macroeconomic level and forecasting foreign direct investment inflows for the following time periods. Keywords: business cycle synchronization; country investment potential; foreign direct investment; Hodrick-Prescott filter; national economy.

Fiscal Policy and Uncertainty

We investigate the impact of macroeconomic uncertainty on the stimulative effect of government spending. Using historical macroeconomic time series, we show that government spending multipliers are smaller in episodes characterized by high macroeconomic uncertainty. This state dependence is found to be both statistically and economically significant. We then build a DSGE model with an agency problem subject to time varying levels of uncertainty. We solve the model via higher-order perturbation and use the model to evaluate state-dependent government stimulus. We find significant state-dependence in multipliers which qualitatively matches our empirical results. We conclude that uncertainty plays a significant role in determining the effectiveness of fiscal policy and that business cycle models which abstract from uncertainty can produce misleading policy recommendations.

Scientific research among the people and in the (urban) field: Ethnology of the city in the institute of ethnography and in Serbia during socialism

In this paper the solutions some practical problems of cointegration analysis are discussed. We mainly focus on the following phases of the Johansen procedure: the baseline model specification, t determination of the number of cointegrating vectors, the inclusion of the determines components and design of partial mode This is the most commonly used approach analyzing nonstationary macroeconomic time series, due to its complexity and simple modeling based on the computer packages. However, as the application of statistic packages may be useless when methodology is partly known, its full understanding makes computer software a reliable source. The purpose of this paper is to popularize (correct) application of cointegration methods. .

A family of extended half-distributions: Theory and applications

In this paper the solutions some practical problems of cointegration analysis are discussed. We mainly focus on the following phases of the Johansen procedure: the baseline model specification, t determination of the number of cointegrating vectors, the inclusion of the determines components and design of partial mode This is the most commonly used approach analyzing nonstationary macroeconomic time series, due to its complexity and simple modeling based on the computer packages. However, as the application of statistic packages may be useless when methodology is partly known, its full understanding makes computer software a reliable source. The purpose of this paper is to popularize (correct) application of cointegration methods. .

Nonlinear autoregressive models with optimality properties

We introduce a new class of nonlinear autoregressive models from their representation as linear autoregressive models with time-varying coefficients. The parameter updating scheme is subsequently based on the score of the predictive likelihood function at each point in time. We study in detail the information theoretic optimality properties of this updating scheme and establish the asymptotic theory for the maximum likelihood estimator of the static parameters of the model. We compare the dynamic properties of the new model with those of well-known nonlinear dynamic models such as the threshold and smooth transition autoregressive models. Finally, we study the model’s performance in a Monte Carlo study and in an empirical out-of-sample forecasting analysis for U.S. macroeconomic time series.

Boosting: Why you Can Use the HP Filter

The Hodrick-Prescott (HP) filter is one of the most widely used econometric methods in applied macroeconomic research. The technique is nonparametric and seeks to decompose a time series into a trend and a cyclical component unaided by economic theory or prior trend specification. Like all nonparametric methods, the HP filter depends critically on a tuning parameter that controls the degree of smoothing. Yet in contrast to modern nonparametric methods and applied work with these procedures, empirical practice with the HP filter almost universally relies on standard settings for the tuning parameter that have been suggested largely by experimentation with macroeconomic data and heuristic reasoning about the form of economic cycles and trends. As recent research (Phillips and Jin, 2015) has shown, standard settings may not be adequate in removing trends, particularly stochastic trends, in economic data. This paper proposes an easy-to-implement practical procedure of iterating the HP smoother that is intended to make the filter a smarter smoothing device for trend estimation and trend elimination. We call this iterated HP technique the boosted HP filter in view of its connection to L_2-boosting in machine learning. The paper develops limit theory to show that the boosted HP (bHP) filter asymptotically recovers trend mechanisms that involve unit root processes, deterministic polynomial drifts, and polynomial drifts with structural breaks, thereby covering the most common trends that appear in macroeconomic data and current modeling methodology. In doing so, the boosted filter provides a new mechanism for consistently estimating multiple structural breaks even without knowledge of the number of such breaks. A stopping criterion is used to automate the iterative HP algorithm, making it a data-determined method that is ready for modern data-rich environments in economic research. The methodology is illustrated using three real data examples that highlight the differences between simple HP filtering, the data-determined boosted filter, and an alternative autoregressive approach. These examples show that the bHP filter is helpful in analyzing a large collection of heterogeneous macroeconomic time series that manifest various degrees of persistence, trend behavior, and volatility.

Multivariate Bayesian Predictive Synthesis in Macroeconomic Forecasting

We present new methodology and a case study in use of a class of Bayesian predictive synthesis (BPS) models for multivariate time series forecasting. This extends the foundational BPS framework to the multivariate setting, with detailed application in the topical and challenging context of multistep macroeconomic forecasting in a monetary policy setting. BPS evaluates—sequentially and adaptively over time—varying forecast biases and facets of miscalibration of individual forecast densities for multiple time series, and—critically—their time-varying inter-dependencies. We define BPS methodology for a new class of dynamic multivariate latent factor models implied by BPS theory. Structured dynamic latent factor BPS is here motivated by the application context—sequential forecasting of multiple U.S. macroeconomic time series with forecasts generated from several traditional econometric time series models. The case study highlights the potential of BPS to improve of forecasts of multiple series at multiple forecast horizons, and its use in learning dynamic relationships among forecasting models or agents.Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

Improving forecast accuracy of financial vulnerability: PLS factor model approach

We present a factor augmented forecasting model for assessing the financial vulnerability in Korea. Dynamic factor models often extract latent common factors from a large panel of time series data via the method of the principal components (PC). Instead, we employ the partial least squares (PLS) method that estimates target specific common factors, utilizing covariances between predictors and the target variable. Applying PLS to 198 monthly frequency macroeconomic time series variables and the Bank of Korea's Financial Stress Index (KFSTI), our PLS factor augmented forecasting models consistently outperformed the random walk benchmark model in out-of-sample prediction exercises in all forecast horizons we considered. Our models also outperformed the autoregressive benchmark model in short-term forecast horizons. We expect our models would provide useful early warning signs of the emergence of systemic risks in Korea's financial markets.

Including News Data in Forecasting Macro Economic Performance of China

In this paper we report results of 3- and 6-months ahead forecasts of Gross Domestic Product (GDP) of China. In total we use 124 predictors from various sources and dates ranging from 2000 through 2017. We use China specific macroeconomic time series data and a large number of predictor variables. In our study we follow the latest state of the art, as outlined by, [Stock and Watson, 2016] who use principal component analysis (PCA) to reduce number of variables and apply dynamic factor model (DFM) to make predictions. The results suggest that including news sentiment significantly improves forecasts and this approach outperforms univariate autoregression. The contributions of this paper are two fold, namely, the use of news to improve forecasts and superior forecast of China's GDP.

Does modeling a structural break improve forecast accuracy?

Mean square forecast error loss implies a bias–variance trade-off that suggests that structural breaks of small magnitude should be ignored. In this paper, we provide a test to determine whether modeling a structural break improves forecast accuracy. The test is near optimal even when the date of a local-to-zero break is not consistently estimable. The results extend to forecast combinations that weight the post-break sample and the full sample forecasts by our test statistic. In a large number of macroeconomic time series, we find that structural breaks that are relevant for forecasting occur much less frequently than existing tests indicate.