Investigating Causal Relations by Econometric Models and Cross-spectral Methods

There occurs on some occasions a difficulty in deciding the direction of causality between two related variables and also whether or not feedback is occurring. Testable definitions of causality and feedback are proposed and illustrated by use of simple two-variable models. The important problem of apparent instantaneous causality is discussed and it is suggested that the problem often arises due to slowness in recording information or because a sufficiently wide class of possible causal variables has not been used. It can be shown that the cross spectrum between two variables can be decomposed into two parts, each relating to a single causal arm of a feedback situation. Measures of causal lag and causal strength can then be constructed. A generalization of this result with the partial cross spectrum is suggested.

A number of theories of causation posit that causes raise the probability of their effects. In this chapter, we survey a number of proposals for analysing causal strength in terms of probabilities. We attempt to characterize just what each one measures, discuss the relationships between the measures, and discuss a number of properties of each measure. One encounters the notion of 'causal strength' in many contexts. In linear causal models with continuous variables, the regression coefficients (or perhaps the standardized coefficients) are naturally interpreted as causal strengths. In Newtonian mechanics, the total force acting on a body can be decomposed into component forces due to different sources. Connectionist networks are governed by a system of 'synaptic weights' that are naturally interpreted as causal strengths. And in Lewis's account of 'causation as influence' (Lewis 2000), he claims that the extent to which we regard one event as a cause of another depends upon the degree to which one event 'influences' the other. In this chapter, we examine the concept of causal strength as it arises within probabilistic approaches to causation. In particular, we are interested in attempts to measure the causal strength of one binary variable for another in probabilistic terms. Our discussion parallels similar discussions in confirmation theory, in which a number of probabilistic measures of degree of confirmational support have been proposed. Fitelson (1999) and Joyce (MS) are two recent surveys of such measures.

In this paper we further develop a notion of causal predictability defined in [A. Merkle, Predictability and Uniqueness of Weak Solutions of Stochastic Differential Equations, Analele Stiintifice ale Universitatii Ovidius Constanta, 2022] as a concept of dependence which is based on Granger's definition of causality. More precisely, in [A. Merkle, Predictability and Uniqueness of Weak Solutions of Stochastic Differential Equations, Analele Stiintifice ale Universitatii Ovidius Constanta, 2022] causal predictability is defined between filtrations, but now we introduce causal predictability between stochastic processes and filtrations. Also, we provide some properties of this new concept. Then we apply the given causality concept to the uniqueness of weak solutions of the stochastic differential equations and in financial mathematics. Granger [Investigating causal relations by econometric models and cross spectral methods, Econometrica. 37 (1969), pp. 424–438] has considered causality concept between time series. In this paper we consider continuous time processes, since continuous time models represent the first step in various applications, such as in finance, econometric practice, neuroscience, epidemiology, climatology, demographic, etc.

The question “When is C a cause of E?” is well-studied in philosophy—much more than the equally important issue of quantifying the causal strength between C and E. In this chapter, we transfer methods from Bayesian Confirmation Theory to the problem of explicating causal strength. We develop axiomatic foundations for a probabilistic theory of causal strength as difference-making and proceed in three steps: First, we motivate causal Bayesian networks as an adequate framework for defining and comparing measures of causal strength. Second, we demonstrate how specific causal strength measures can be derived from a set of plausible adequacy conditions (method of representation theorems). Third, we use these results to argue for a specific measure of causal strength: the difference that interventions on the cause make for the probability of the effect. An application to outcome measures in medicine and discussion of possible objections concludes the chapter.

Predictive accuracy of simple versus complex econometric market share models: Theoretical and empirical results

Korean Abstract: 이 논문은 다변수로짓계량모형을 이용하여 1973~2000년 아시아, 아메리카, 유럽 21개국의 은행위기 및 통화위기 발생을 초래하였던 요인을 찾고 나아가 두 위기간의 상호관계를 분석한 것이다. 이 분석은 자료의 기준시점에서 비롯된 인과관계 혼란 등의 문제를 해결하는 동시에 종속변수인 은행위기 또는 통화위기와 설명변수와의 인과관계를 설정하는 방편으로 시차를 도입함으로써 두 위기를 초래하는 선행원인이 무엇인가를 살펴보고 시차를 도입하지 않은 경우와도 비교하였다. 시차를 고려하지 않은 기존방법의 분석에서는 은행위기는 경기침체, 인플레이션, 환율인상, 실질이자율 상승, 대출붐과 연관되어 있었다. 하지만 시차를 도입한 경우에는 은행위기는 경기침체, 인플레이션, 실질이자율 상승, 대출붐뿐 아니라 실제지급준비율의 상승, 자본자유화 등에 의해 초래될 수 있는 것으로 나타났다. 통화위기는 시차를 고려한 경우나 아닌 경우 큰 차이를 보이지 않았다. 시차를 고려하지 않은 기존방법의 분석에서는 통화위기는 경기침체, 경상수지 적자와 연관되어 있었다. 하지만 시차를 고려한 경우에는 통화위기가 경기침체, 경상수지 적자는 물론 은행의 대외순자산 감소(또는 순부채 증가)에 의해서도 초래될 수 있는 것으로 나타났다. 그리고 은행위기와 통화위기간의 상호관계를 보기 위해 은행위기의 설명변수로 통화위기를, 통화위기의 설명변수로 은행위기를 설정하여 분석한 결과, 선행한 통화위기가 은행위기의 한 원인은 될 수 있지만, 선행한 은행위기가 통화위기의 한 원인이 되는 것은 아니었다. 그럼에도 불구하고 은행위기와 통화위기는 의미 있는 상관관계를 가질 정도로 같은 연도에 발생하는 경우가 적지 않았다. English Abstract: This paper studies the factors associated with the emergence of banking crises and currency crises and the interrelationship between the two crises in 21 American, European and Asian countries in 1973~2000 using a multivariate logit econometric model. And this analysis attempts to introduce the lag model to solve the causality problems in banking crises and currency crises. In case of the simple binary models without lag, banking crises were associated with economic recession, inflation, depreciation, the rise of real interest rate, lending boom. But in case of lag models, banking crises were caused by economic recession, inflation, the rise of real interest rate, lending boom, the rise of reserve ratio, the liberalization of capital account. Currency crises were associated with economic recession, the larger deficit of current account relative to import in case of the simple binary models without lag, But in case of lag models, currency crises were caused by economic recession, the rapid decrease of net foreign assets of banks, and the larger deficit of current account relative to import. And this analysis shows that currency crises can be a cause of banking crises, not vice versa. Nevertheless the two types of crises have positive correlation with each other.

We address Northcott’s (2005) criticism of Pearson’s correlation coefficient ‘r’ in measuring causal strength by replacing Pearson’s linear regressions by nonparametric nonlinear kernel regressions. Although new proof shows that Suppes’ intuitive causality condition is neither necessary nor sufficient, we resurrect Suppes’ probabilistic causality theory by using nonlinear tools. We use asymmetric generalized partial correlation coefficients from Vinod [2014] as our third criterion (denoted as Cr3) in addition to two more criteria (denoted Cr1 and Cr2). We aggregate the three criteria into one unanimity index, UI in [-100; 100], quantifying causal strengths associated with causal paths: Xi -> Xj , Xj -> Xi, and Xi Xj .

Short and long run causality measures: Theory and inference

Boskin, Michael J., Mark Gertler, and Charles Taylor, Impact of Inflation on U.S. and International Competitiveness (Washington, D.C.: National Planning Association, 1980). Carlton, Dennis W., The Disruptive Effect of Inflation on the Organization of Markets, in Robert E. Hall (ed.), Inflation: Causes and Effects (Chicago: University of Chicago Press, 1982). Fama, Eugene, Short-term Interest Rates as Predictors of Inflation, American Economic Review 65 (June 1975), 269-282. Freund, William C., and Paul B. Manchester, Productivity and Inflation, in John D. Hogan (ed.), Dimensions of Research, Vol. I (Houston: American Center, 1980), 53-71. Geweke, John, Richard Meese, and Warren T. Dent, Comparing Alternative Tests of Causality in Temporal Systems: Analytic Results and Experimental Evidence, Journal of Econometrics 21 (Feb. 1983), 161-194. Granger, C. W. J., Investigating Causal Relations by Econometric Models and Cross-Spectral Methods, Econometrica 37 (July 1969), 424-438. Guilkey, David K., and Michael K. Salemi, Small Sample Properties of Three Tests for Granger-Causal Ordering in a Bivariate Stochastic System, this REVIEW 64 (Nov. 1982), 668-680. Houthakker, Hendrik S., Growth and Inflation: Analysis by Industry, Brookings Papers on Economic A ctivity (1979), 241-256. Hsiao, Cheng, Autoregressive Modelling and Money-Income Causality Detection, Journal of Monetary Economics 7 (1981), 85-106. , Autoregressive Modeling and Causal Ordering of Economic Variables, Journal of Economic Dynamics and Control 4 (1982), 243-259. Jarrett, J. Peter, and Jack G. Selody, The Productivity-Inflation Nexus in Canada, 1963-1979, this REVIEW 64 (Aug. 1982), 361-367. Kendrick, John W., and Elliot S. Grossman, in the United States: Trends and Cycles (Baltimore: Johns Hopkins University Press, 1980). Sims, Christopher A., Money, Income and Causality, American Economic Review 62 (Sept. 1972), 540-552. U.S. Department of Commerce, Bureau of Economic Analysis, National Income and Product Accounts of the United States, 1929-76, Statistical Tables (Washington, D.C.: Superintendent of Documents, 1981). U.S. Department of Labor, Bureau of Labor Statistics, tables headed Analytical Ratios for the Business Sector, All Indexes 1977 = 100, and Basic Industry Data for the Business Sector, All Persons, printout dated April 27, 1983.

ABSTRACTThis study aims to explore the causal relationship between economic risk and foreign direct investment (FDI) inflows for the case of Turkey. With the aim of establishing robust findings for the research in mind, both traditional and modern causality techniques are utilized; time domain Granger (1969, “Investigating Causal Relations by Econometric Models and Cross-Spectral Methods.” Econometrica 37: 424–438.), Toda and Yamamoto (1995, “Statistical Inference in Vector Autoregressions with Possibly Integrated Processes.” Journal of Econometrics 66 (1–2): 225–250.), Fourier Toda-Yamamoto and frequency domain Breitung and Candelon (2006, “Testing for short- and long-run causality: A frequency-domain approach.” Journal of Econometrics 132 (2): 363–378.) spectral causality test. Our empirical findings reveal that; economic risk changes in Turkey significantly lead to changes in FDI inflows. However, there is no evidence of causality running from FDI to economic risk. The findings imply that economic risk is an essential determinant of FDI inflows in Turkey. Our findings are compatible with historical macroeconomic developments in Turkey and imply important policy implications. The results of this study can be generalized for other emerging economies that have similar macroeconomic environments, in order to create useful policy implications regarding FDI inflow.

Anderson, T.W. (1971) The Statistical Analysis of Time Series (New York: John Wiley and Sons) Auerbach, Robert D. and Jack L. Rutner (forthcoming) 'The misspecification of a non-seasonal cycle as a seasonal by the X-1 1 seasonal adjustment program.' Review of Economics and Statistics Barth, James R. and James T. Bennett (1974) 'The role of money in the Canadian economy: an empirical test.' This JOURNAL 7, 306-11 Box, George E.P. and David Pierce (1971) 'Distribution of residual autocorrelations in autoregressive-integrated moving average time series models.' Journal of the American Statistical Association 65, 1509-26 Granger, C.W.J. (1969) 'Investigating causal relations by econometric models and cross-spectral methods.' Econometrica 37, 424-38 Nerlove, Marc ( 1965) 'A comparison of a modified 'Hannan' and the BLS seasonal adjustment filters.' Journal of the American Statistical Association 60, 442-91 Pierce, David A. and Larry D. Haugh (1975) 'The assessment and detection of causality in temporal systems.' (Mimeo. available from Pierce at the Federal Reserve Board). Rutner, Jack L. (1974) 'Time series analysis of income and several definitions of money.' Monthly Review, Federal Reserve Bank of Kansas City, 9-16 Sims, Christopher A. (1972) 'Money, income, and causality.' American Economic Review 62, 540-52

On the Hsiao definition of non-causality

Causality is one of the most important topics in a Machine Learning (ML) research, and it gives insights beyond the dependency of data points. Causality is a very vital concept also for investigating the dynamic surface of our living planet. However, there are not many attempts for integrating a causal model in Remote Sensing (RS) methodologies. Hence, in this paper, we propose to use patch-based RS images and to represent each patch-based image by a single variable (e.g. entropy). Then we use a Structural Equation Model (SEM) to study their cause-effect relation. Moreover, the SEM is a simple causal model characterized by a Directed Acyclic Graph (DAG). Its nodes are causal variables, and its edges represent causal relationships among causal variables if and only if causal variables are dependent.

Eighty-four post-1990 empirical studies of international tourism demand modeling and forecasting using econometric approaches are reviewed. New developments are identified, and it is shown that applications of advanced econometric methods improve the understanding of international tourism demand. An examination of the 22 studies that compare forecasting performance suggests that no single forecasting method can outperform the alternatives in all cases. The time-varying parameter (TVP) model and structural time-series model with causal variables, however, perform consistently well.

This article examines the predictive accuracy of simple time-series and econometric models on forecasting firm performance in terms of sales turnover. Evidence from Small and Medium sized Enterprises (SMEs) in the United Kingdom are presented. The study identifies operational rules under which the class of simple econometric regression models is more accurate than simple time-series forecasting alternatives, thus more appropriate to back-up multiple investment decisions.