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The impact of global economies on US inflation: A test of the Phillips curve

• Published: 02 June 2022
• Volume 46 , pages 575–592, ( 2022 )

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• Hany Guirguis   ORCID: orcid.org/0000-0002-7635-7749 1 ,
• Vaneesha Boney Dutra 2 &
• Zoe McGreevy 3  

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Understanding the relationship between employment and inflation is of great interest to policymakers and market participants. This paper introduces a new global inflation measure based on the principal component analysis (PCA) of the inflation rates of major US trade partners. We find that US domestic inflation correlates strongly with global inflation in the short- and long term. Moreover, global inflation leads the US inflation and accounts for 80% of the price discovery process. Additionally, we show that the Phillips curve equation improves in-sample and out-of-sample forecasting of US inflation rates by incorporating our spill-over-based global inflation (SGI) measure. Also, the utilization of the SGI in the Phillips equation increases the responsivity of the inflation rate data to the unemployment gap by 37%. In summary, the present results support the hypothesis that global inflation is a crucial determinant of domestic (US) inflation. The paper's main findings draw vital policy implications that emphasize the need for stronger cooperation among central banks to cope with the spill-over effect of global inflations on domestic economies.

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1 Introduction

Understanding the relationship between employment and inflation is of great interest to policymakers and market participants. Recent studies have revealed a weakening trend in the traditional relationship between unemployment and inflation wherein a strengthening economy with falling unemployment tends to increase consumer demand, thus causing prices to rise. Potential explanations for the weakened relationship between inflation and economic slack discussed in the literature include non-linearity in the relationship between inflation and unemployment (Albuquerque and Baumann  2017 ), inaccurate measures of economic slack and inflation (Ball and Mazumder  2019 ), anchored inflationary expectations (Ball and Mazumder  2019 ), and globalization effects (Auer et al.  2019 ). Regardless of the underlying causes, flattening the Phillips curve has driven the US Federal Open Market Committee to re-evaluate its historical strategy of pre-emptive withdrawal of accommodation when unemployment rates drop a natural level.

Motivated by the theoretical models of Clarida et al. ( 2002 ) and Martínez-García and Wynne ( 2010 , 2013 ), we examine the spill-over effect of global inflation on US domestic inflation. First, we introduce a new measure for global inflation based on a principal component analysis (PCA) of the inflation rates of major US trade partners, including China, Canada, Mexico, Japan, Germany, Korea, and the UK. We then test how well our spill-over-based global inflation measure (SGI) correlates with domestic inflation in the short and long term. Finally, we demonstrate how incorporating our SGI parameter into the Phillips curve affects in-sample forecasts, out-of-sample forecasts, and the Phillips curve slope.

The remainder of this paper is organized into Sections  2 – 5 . Section  2 provides a theoretical review of the various explanations for the observed flattening of the slope of the Phillips curve. Section  3 estimates SGI and examines its contribution to the Phillips curve. In Section  4 , we report how incorporating the SGI index into the Phillips curve equation affects out-of-sample predictions of the US inflation rate. Finally, in Section  5 , a conclusion and policy implications of the analyses are given.

2 Theoretical review

Decades of research have questioned the relationship's stability between inflation and unemployment and its validity over time. These studies offer varying theories as to why this relationship may have changed. Some researchers attribute the weakened relationship to the nonlinear relationship between inflation and slack in the economy. Clark et al. ( 1996 ) found that when firms have excess capacity during periods of increasing demand, they have little pressure to raise prices. However, during periods of economic overheating when firms are operating close to their total capacity, strengthening demand triggers substantial price increases. Ball and Romer ( 1991 ) demonstrated that price rigidity plays a considerable role in inflation dynamics. For example, when inflation is higher, firms need to adjust price levels more frequently, which is reflected in a steepening of the Phillips curve slope. However, firms are under less pressure to adjust their prices when inflation is low. Thus, weak inflation for prolonged periods obviates the need for frequent price adjustments. Akerlof et al. ( 1996 ) have shown that employers prefer to lay off their least productive employees rather than enact wage cuts. Firm managers believe that even nominal wage cuts have a negative impact on morale, which disrupts labor efficiency. Thus, downward labor market adjustments are mediated primarily by unemployment rather than wage reductions. Because of wage rigidity, salary levels within a cycle are inversely related to the depth and length of short-term unemployment. For example, a more profound and extended period of high unemployment will suppress salary augmentation. Xu et al. ( 2015 ) observed an asymmetric, nonlinear Phillips curve with a roughly convex shape around the 75 th quantile, with linearity maintained around the 25 th quantile. In a subsequent study of the nonlinear behavior of inflation, Albuquerque and Baumann ( 2017 ) concluded that the inclusion of nonlinear specifications when generating Phillip's curve could improve the model's performance relative to the standard linear model.

Beyond the nonlinearity of the Phillips curve, several additional concerns have been raised, including the need for more accurate measures of unemployment and economic slack and the effects of anchored inflation and globalization. For the Phillips curve to provide valuable insights, policymakers must employ appropriate economic measures. For example, according to Ball and Mazumder ( 2019 ), the impact of economic slack on the labor market in the Phillips equation is better captured with short-term unemployment data than with long-term unemployment data. They argue that those who have been unemployed for more than 27 weeks and are still looking for a job are not competitive or not seriously looking for a job after being unemployed for such a long time. Thus, labor supply may be best reflected by the percentage of short-term unemployed workers in the labor market. For example, during the great recession, inflation did not fall from 2009 to 2011 as had been predicted, partly because short-term unemployment rose less sharply than total unemployment. Coibion and Gorodnichenko ( 2015 ) present an alternative explanation in which they explore the idea that firms' inflation expectations are best proxied by household expectations. They posit that an expectations-augmented Phillips curve, following Friedman's ( 1968 ) suggestion, utilizing household forecasts presents a more accurate representation of inflation events.

A third explanation for the apparent weakening relationship between employment level and inflation rate hangs on the Federal Reserve anchoring inflation expectations around its 2% target. As a result, inflation has become less responsive to fluctuations in the unemployment gap and the state of the economy. Benati ( 2008 ), Gurkaynak et al. ( 2010 ), Davis ( 2012 ), Davis ( 2014 ), and Bundick and Smith ( 2020 ) have confirmed that anchored inflation has flattened the slope of the Phillips curve by demonstrating the reduction in the responsiveness of inflation expectations to endogenous and exogenous domestic shocks. In addition, Jorgensen and Lansing ( 2019 ) have shown that accounting for anchored inflationary expectations within the framework of the New Keynesian model improves both the stability of the Phillips curve slope and inflation forecasting substantially.

A fourth possible explanation, which was adopted as the focus of this research, addresses the impact of globalization. Theoretically, the workhorse of the New Open Economy Macro Model (NOEM) introduced by Clarida et al. ( 2002 ) and Martínez-García and Wynne ( 2010 , 2013 ) provides a solid ground for the integration of global inflation in the formation of local inflation. Clarida et al. ( 2002 ) derived a two-country open economy version of the dynamic New Keynesian model. The authors show that the reaction function of the central banks should include both domestic and foreign inflation. Further, they show that the magnitude of the response of the domestic central bank to foreign inflation depends on the sign and the relative strength of the spillover of the foreign output gap on domestic marginal cost. Martínez-García and Wynne ( 2010 ) Extended Clarida’s model by allowing firms to set the prices of their good and services in the importing markets or what is referred to as the local currency pricing (LCP). The structure of their models incorporates three equations: open economy Phillips curve, open-economy IS, and domestic and foreign Taylor rule. Their workhorse New Open Economy Macro model (NOEM) has become the building stone of international macroeconomics. Martinez-Garcia and Wynne concluded that the Phillips curve is getting flattered as the local economies become globalized. Further, they show that the foreign output gap is an essential determinant in the Phillips curve equation. Following Martínez-García and Wynne ( 2014 ), Duncan and Martínez-garcía ( 2015 ) decompose the NOEM into two subsystems that divide local inflation into its components: global and differential inflation. Next, the authors established that local and international inflations are cointegrated.

The following papers further support the argument for including a measure of global inflation in our Phillips Curve equation. Bonello and Swartz ( 1978 ) provide a succinct overview of essays detailing the theory and economic challenges fiscal and monetary policy faces over time. Their summation highlights the notion that internationally, debt-ridden countries threaten to destabilize economies beyond their borders, thus the need to incorporate global measures when examining inflation and other indicators of domestic economic health. Black ( 1978 ) examines the impact of international historical shocks and economic disturbances on global economies. Frisch ( 1977 ) provides a comprehensive overview of inflationary theories and the Phillips curve for 1963–1975. Also, Bruno ( 1978 ) examines pricing dynamics and the price adjustment process and concludes that exchange rates and import prices significantly impact the pricing mechanism. Moreover, they directly note that a two-dimensional Phillips Curve may not be sufficient in an open economy and should include alternative measures/dimensions. Also, Cebula and Frewer ( 1980 ) analyze, empirically, whether and to what extent there is an ‘imported’ component to domestic inflation. Specifically, they examine the ‘price effect’ and whether petroleum prices/imports have contributed significantly to domestic inflation. Finally, they conclude that exogenous, international forces increasingly impact economies of developed nations and that one-way inflation is imported is via petroleum prices and imports.

Recently, Obstfeld ( 2020 ) describes the impact of globalization on domestic inflation through the global competitive environment and import pricing channels. Specifically, Obstfeld posits that the global competitive environment exerts downward pressure on domestic inflation rates by discouraging firms from increasing their prices in response to a higher marginal cost of production. Meanwhile, according to this view (Sbordone  2009 ), global competition facilitates the US economy's access to global slack within emerging and developing countries, which weakens the bargaining power of the US domestic factor of production, thereby limiting negotiations for higher compensation even when the domestic economy is operating above its potential output. Additionally, the availability of low-priced imported goods affects domestic inflation of both production and consumer goods.

Auer et al. ( 2017 ) have demonstrated that the growth of trade in intermediate goods and services has been the driving force of the increasing (decreasing) role of global (domestic) slack in the Phillips curve equation. They attribute this increased importance to expanding global value and supply chains in response to the broad worldwide integration of production processes. Auer et al. ( 2019 ) found that globalization could explain 51% of the variance in producer price inflation in a sample of 30 countries. Half of this variance was attributed to the cross-border propagation of cost shocks through international input–output linkages. Similarly, Ciccarelli and Mojon ( 2010 ) found that inflation in 22 OECD countries had a common factor accounting for 70% of these countries' inflation variance from 1960 to 2008. Incorporating this common global factor in the inflation equation for the 22 OECD countries improves the out-of-sample forecasting of inflation rates. A wide variety of variables have been incorporated into Phillips' equation in efforts to capture the growing impact of globalization, including global commodity prices, global slack, exchange rates, import and oil prices, the global output gap, and nonlinear exchange rate pass-through (Borio and Filardo 2007 ; Jašová et al. 2018 ; and Forbes 2019 ). These studies demonstrate the importance of globalization in explaining and forecasting domestic inflation while underscoring the diminishing role of domestic slack in explaining inflation rates.

In this paper, we explore the aforementioned inflation commonalities and hypothesize and show that inflation should, at least in part, be modeled as a global rather than a local phenomenon. Our research focuses on the spillover effect of international inflation on US inflation. Specifically, we examine whether incorporating global inflation data into Phillip’s curve would accurately represent the relationship between unemployment and inflation.

In sum, these papers lend further credence to our notion that, in a changing environment where there is greater global interdependence, global measures may be critical in examining the inflationary environment.

3 Estimation techniques and empirical results

3.1.1 global inflation.

We collect quarterly Consumer Price Index (CPI) data on all items for the USA and its leading trade partners (China, Canada, Mexico, Japan, Germany, Korea, and the UK) from 2003:Q1 to 2019:Q4. We then estimate each country's inflation rate (percentage) from the previous year. Descriptive statistics for the annualized quarterly inflation rates are shown in Table 1 , and US inflation rates are plotted against the inflation rates of its main trade partners in Fig.  1 . Average inflation rates for US trade partners vary from 4.08% (Mexico) to 0.257% (Japan). Notably, China exhibits the most highly variant inflation rate, fluctuating between 7.78% and -1.54%, with a standard deviation of 1.84%. Germany, which shows the second-lowest average inflation rate of 1.42%, has the most stable domestic prices with a standard deviation of only 0.68%. Jarque–Bera statistics show that inflation in China, Canada, Mexico, and Japan do not follow a normal distribution. The US inflation rate correlates strongly with inflation rates in Germany (r = 0.80), Canada (0.68), China (0.60), Korea (0.53), and UK (0.52), but not with inflation rates in Japan (0.17) and Mexico (0.03) (see Table 1 and Fig.  1 ).

CPI of inflation for the USA and main US trade partners. Annualized quarterly inflation rates are shown from 2003:Q1 to 2019:Q4. The y axes represent inflation rate. The x axes represent time from 2003 through 2019

Consistent with the work of Bernanke et al. ( 2005 ), we use PCA to create our SGI, an independent composite factor representing global inflation. We apply PCA to the inflation rates in China, Canada, Mexico, Japan, Germany, Korea, and the UK, keeping the first principal component as our proxy variable for global inflation and find that the US inflation rate and our global inflation index SGI are strongly correlated in the short run, with a correlation coefficient of 0.79 (Fig.  2 ).

US inflation versus global inflation. The graph shows the US inflation and an index for global inflation based on the first principal component of the inflation rates in China, Canada, Mexico, Japan, Germany, Korea, and the UK. The left axis represents the inflation rate, the right y-axis represents the values of the index of global inflation, and the x-axis represents the time from 2003 through 2019

We then test for long-term correlation and price discovery between domestic inflation and the global inflation index with Johansen's ( 1988 ) cointegration test and Hasbrouck’s ( 1995 ) price discovery technique. Johansen ( 1988 ) introduced a full-information maximum likelihood technique that enables simultaneous estimation of the long-run equilibrium relationship and short-term linkages, where the results do not depend on which variable is dependent. Following Johansen and Juselius ( 1990 ), let us consider a vector X t of p non-stationary I(1) series. Such a series has the following vector autoregressive representation (VAR):

where X t is a (p × 1) vector of I(1) non-stationary p time series, T is the number of observations, n is the number of lags, and D t values are centered seasonal dummies that sum to zero over the entire sample period. If all of the time series in the VAR have a single unit root that can be removed by taking the first difference, then the VAR can be expressed as

In this framework, the cointegration hypothesis can be tested by evaluating the rank of the long-run impact matrix (Π). More specifically, the number of distinct cointegrating vectors, r, is equal to the rank of Π, or the number of characteristic roots of Π that are statistically different from zero. As stated by Juselius ( 2006 ), “If X t is ~ I(1), then ∆X t is stationary and can’t be written in terms of the non-stationary variable ΠX t−1 . Therefore, Π can either be zero or it must have the reduced rank ( \(\alpha {\beta }^{\mathrm{^{\prime}}})\) ”, where α and β are the p × r matrices of the speed of adjustment parameters and the cointegrating parameters respectively, and r is the number of cointegrating relationships (0 < r < p). Accordingly, Eq. ( 2 ) can be written in terms of the error correction feature ( \(\alpha {\beta }^{\mathrm{^{\prime}}}{X}_{t-1})\) and the VAR to form the vector error correction model as follows:

Price discovery describes how the information for a particular asset is transmitted among different markets in which the asset is traded. Generally, the efficient markets hypothesis requires that asset information be reflected quickly and fully in asset pricing. Moreover, since the introduction of modern portfolio theory in the 1950s—wherein mean–variance analysis is used to assemble a portfolio designed to maximize return for a given level of risk—academics and practitioners have been keen to better understand price discovery and the co-movement of different types of assets in order to better predict the magnitudes of benefit for particular diversification schemes. Barkham and Geltner ( 1995 ) point out that price discovery can happen first in the unsecuritized market due to the larger market size and trading volumes. More informed and specialized investors conduct trades. The estimated speed of adjustment parameters, \(\alpha\) , can then be utilized to examine price discovery in the different markets. For example, suppose all adjustment parameters are statistically significant with the correct sign for stability. In that case, none of the markets are weakly exogenous, and the adjustment process to restore equilibrium will take place in all the markets. In this case, the relative magnitude of the coefficients will determine which market leads in terms of price discovery. Conversely, suppose α is statistically significant only in the first market. In that case, the price discovery occurs in the other markets, and the first market adjusts to remove the disequilibrium in the long-term relationship.

In our paper, we utilize Hasbrouck's ( 1995 ) technique to estimate the share of each market in price discovery. First, Hasbrouck presents \(\Delta {X}_{t}\) in Eq. ( 3 ) by the following vector moving-average:

where \(\Psi \left(L\right)\) is a polynomial in the lag operator. When X attains its long-term equilibrium value (X*), Eq. ( 3 ) can be written as follows:

where \(\beta_\bot\alpha'_\bot\varepsilon_t\) is the long-term effect of the innovations of n markets. Additionally, the variance of \(\theta \Psi {\varepsilon }_{t}\) is \(\Psi \Sigma {\Psi }^{\mathrm{^{\prime}}}\) under the assumption that p markets respond identically to innovations in efficient prices in the long-run.

In accordance with the work of Hasbrouck ( 1995 ), we decompose Var( \(\theta \Psi {\varepsilon }_{t}\) ) into its n components ( \({v}_{i}^{2}\) ) attributable to each market and then derive a Cholesky decomposition of \(\Sigma\) in terms of F, where \(\Sigma\) = \(F{F}^{\mathrm{^{\prime}}}\) , and calculate \({V}_{i}^{2}\) as follows:

The share of each market in price discovery ( \({S}_{i}\) ) is then calculated by dividing \({V}_{i}^{2}\) by the total innovation variance as follows:

We commence empirical testing by examining the classical assumption of the non-stationarity of our series. To do so, we utilize the Dickey-Fuller unit root test, wherein the number of lags is determined by the Akaike information criterion. As illustrated by Table 2 , we find that taking the first difference of each series results in the removal of one-unit root from US inflation and global inflation rates (5% significance level).

We then perform Johansen’s cointegration trace test between US inflation and global inflation. To determine the stability of the underlying long-term relationships among the series, we run Johansen’s test iteratively over the samples with forward estimation. The first subsample for forward estimation is from 2003:Q1 to 2013:Q1, and the last (full) sample runs from 2003:Q1 to 2019:Q4 (36 iterations). Finally, for each two-series set that is cointegrated, we run the Hasbrouck ( 1995 ) test and calculate the contribution of each set to the process of price discovery.

The trace test for foreword interactions confirms a strong cointegration relationship between domestic and global inflation rates in forward-rolling estimates. The contribution of each inflation series to price discovery when two sets are cointegrated can be seen in Fig.  3 , which shows cointegration of the two-time series in all the forward tests and illustrates the dominance of a global inflation index. Notably, the average statistics for all forward-rolling estimates show that global inflation leads US inflation and accounts for 80% of price discovery. In summary, our analysis confirms strong short- and long-term relationships between global inflation and US domestic inflation.

Forward rolling trace test and Hasbrouck test results. A. Forward rolling trace test for cointegration (y axis) between US inflation and global inflation (solid line); the 95% critical value is shown with a dotted line. B. Hasbrouck test outcome for subsamples starting on 2003:Q1 and ending from 2013:Q1 to 2019:Q4. The y axis represents percent contribution to price discovery of US inflation (solid line curve) and of global inflation (broken line curve). Subsample end dates are shown on the horizontal axis

3.1.2 Phillips curve and global inflation

This section examines the impact of accounting for global inflation on the performance of the Phillips curve equation. We follow Laubach and Williams’s specifications ( 2003 ) to estimate an augmented Phillips curve. However, we replace the core PCE with the CPI as our inflation rate measurements. The other determinants in the CPI inflation \(({\pi }_{CPI})\) equation are the expected inflation \(({\pi }_{CPI}^{e}\) ), output Gap ( \(\tilde{y })\) , crude imported oil inflation gap ( \({\pi }_{oil}-{\pi }_{PCE}^{e}),\) Core import (excluding petroleum, computers, and semiconductor), inflation gap ( \({\pi }_{import}-{\pi }_{PCE}^{e}),\) three moving average inflation \((MA3{\pi }_{CPI})\) , and five moving average inflation ( \(MA5{\pi }_{CPI}\) ). We also use a time trend (D 1 ) and a dummy that takes the value of one for 2008:Q1–2009:Q3 and zero elsewhere. Thus, the first specification of the Phillips curve can be stated as follows:

where the output gap is the difference between the unemployment rate (U3) and the natural rate of unemployment published by the congressional budget office. Meanwhile, the expected inflation at time t is the average of the four-step out-of-sample forecasts calculated from regressing inflation on a constant and three lags of the inflation rate. Next, we account for the spillover effect of global inflation by including the first principal component of global inflation rates in the Phillips equation as follows:

We then apply the ordinary least squares technique to estimate the two specifications for the entire sample from 2003:Q1 to 2019:Q4 and then correct the ordinary least square regressions for autocorrelation and heteroscedasticity using the Newey and West ( 1987 ) method. Both regressions are reported in Table 3 . Comparing the results that we obtain with and without augmentation of the Phillips equation with our global inflation index, namely SGI, reveals that the augmentation has three notable effects: (1) increasing the adjusted R 2 value by 15%, from 0.61 to 0.71; (2) increasing the absolute value of the slope of the Phillips curve by 36.8%, from -0.19 to -0.26; and (3) global inflation spillover has a positive effect on US inflation significant at the 1% level.

To examine the stability of the superior performance of the second specification, we calculate adjusted R 2 values, in-sample stability, and estimated coefficients of global inflation, output gap, and their p-values over 27 forward-rolling subperiods starting on 2003:Q1 and ending from 2013:Q1 to 2019:Q4.

The results confirm the superior performance of the Phillips curve specification augmented by the global inflation index as shown in Figs.  4 and 5 . Additionally, the slope of the Phillips curve is statistically significant at the 5% level between 2014:Q4 and 2015:Q3 only when global inflation is incorporated in the Phillips equation. Notably, from 2014:Q4 to 2015:Q3, there is an observable divergence of the stronger economies in the USA and UK from the stagnant economies in Japan and Eurozone countries.

Forward rolling Phillips curve analysis. A. Graph of estimated coefficients of global inflation. B. Graph of p-values of the estimated coefficients in the graph shown in panel A. C. Graph of output gap values between the estimated coefficients for specification I (solid line) and specification II (dotted line). D. Graph of p-values from forward rolling tests of subsamples starting on 2003:Q1 and ending from 2011:Q1 to 2019:Q4. In B and D, data for specification I and II are shown with broken and solid lines, respectively. In C and D, the dash-dot lines represent 1% (lower) and 5% (higher) significance levels

Primary comparison of the performance of specifications I and II. A. Graph of adjusted R. 2 values calculated from forward rolling regressions of specification I (dotted line), which is generated by a Phillips equation based on the US CPI, and of specification II (solid line), which is generated by a Phillips equation based on the US CPI as well as an augmentation with the presently developed SGI index of global inflation. B. Graph of in-sample forecast data generated by specification I (dotted line) and specification II (solid line), together with a plot of the CPI (dashed line) for the same time period. In both graphs, subsamples start on 2003:01 and end from 2011:Q1 to 2019:Q4

The stagnant economies in the Eurozone and Japan decreased the inflation rates in these countries, which put downward pressure on the US inflation rate and weakened its relationship with the output gap. In conclusion, accounting for the spillover effect of global inflation on US inflation in the Phillips equation stabilizes the slope of the Phillips curve, as captured by the second specification.

4 Out-of-sample predictions and properties of the prediction errors

The accuracy of inflation forecasts is limited by inconsistencies across forecasting techniques and model specifications. In their review of forecasting models and comparative analysis of the performance of Phillips curve forecasting specifications, Stock and Watson ( 2010 ) find that a univariate forecasting model tends to outperform more complex multivariate models. Alvarez-Diaz and Gupta 2016 subsequently replicated this outcome. Abdelsalam ( 2017 ), who notes inherent specification issues that can impact the predictive power of Phillips curves and analyses of augmented versions of Phillips equations that incorporate time-varying coefficients, finds that augmentations can improve forecast accuracy. Gupta et al. ( 2017 ) have shown that a factor augmented-qualitative VAR can outperform other augmented VAR models. Furthermore, Balcilar et al. ( 2017 ) have demonstrated that the VARFIMA (vector autoregressive fractionally integrated moving average) model is superior to the standard model.

A quick review of the literature indicates that there may not be a one-size-fits-all model or specification. Stock and Watson ( 2010 ) make a valid argument that this variability in utility should not be surprising given the fluctuations in US inflation dynamics that have accompanied a transforming US economy and changes in monetary policy regimes. To that end, Inoue et al. ( 2017 ) find significant evidence that a forecasting model's performance can be sensitive to estimation window size. They propose a methodology for determining an optimal estimation period that minimizes conditional mean square forecasting error (MSFE). They show that their window selection method deteriorates for models containing numerous predictors with parameters with differing time-varying patterns. They find that an unemployment-based Phillips curve has inflationary predictive power when optimal sizes are used.

To examine the effect of global inflation on an out-of-sample forecast of the US inflation rate, we conduct one-step predictions based on rolling window regressions. First, estimated coefficients from each regression are used to predict the inflation rate of the next quarter in the forward estimation period, with the first subsample being from 2003:Q1 to 2013:Q1, and the last (total) sample running from 2003:Q1 to 2019:Q3. This process generates 27 out-of-sample predictions (Fig.  6 ) for each model.

Graph of out-of-sample forecasts of the US inflation rate. The inflation rate forecasts (x axis) are calculated from the forward rolling regression of specification I (dotted line) and specification II (dashed line). The specification forecast plots are overlain on a plot of the US CPI (solid line). The subsamples start on 2003:01 and end from 2011:01 to 2019:04 (y axis is time). Specifications I and II are as defined as in Fig.  5

Following Clapp and Giaccotto's ( 2002 ) approach, we evaluate model performance according to three criteria: the desirability of prediction error properties, the relative efficiency of predictions, and the informational efficiency of projections. A desirable property for forecasting errors is that they show a normal distribution around zero with constant variance. The tendency of a model to over-predict (under-predict) can be detected by a left (right) skewed distribution with a statistically significant negative (positive) mean. Highly inaccurate forecasts can result in excessively negative kurtosis.

We test for relative efficiency by calculating Theil’s U 2 value, mean forecasting error (MFE), mean absolute forecasting error (MAFE), and root means squared forecasting error (RMSFE) for 27 forecasts, as follows:

where a Theil’s U 2 value close to one indicates that the equation can effectively predict future values of the dependent variable, and a negative Theil’s U 2 value suggests that the naive specification outperforms forecasts of the equation. To test whether our forecasts are informationally efficient, we first regress the inflation rate on its prediction and a constant term, as follows:

To explore the source of inefficiency or the forecast error, if any, we calculate the mean squared forecasting error (MSPE) based on our estimates from Eq. ( 11 ), as follows:

where ( \({\overline{{inflation}^{e}} - \overline{inflation})}^{2}\) ) refers to the average actual and forecasted series, n is the number of observations, \(\widehat{{\beta }_{i}}\) is the estimated coefficient, and ESS is the sum of squared forecasting errors. We then calculate Theil’s decomposition of the MSPE by dividing Eq. ( 12 ) by the MSPE, as follows:

where U bias is a measure of the bias in \({\beta }_{0}\) , U regression is a measure of the bias in \({\beta }_{1}\) , and U error is a measure of the portion of the forecast errors that can be attributed to equation residuals. Informational efficiency requires that \(\widehat{{\beta }_{0}}\) and \(\widehat{{\beta }_{i}}\) be statistically consistent with zero and one, respectively. If so, the bias will be captured predominantly by U error , which should be close to one.

As reported in Table 4 , the two models produce normally distributed forecasting errors with means that are statistically different from zero at the 5% percent level. There are several parameters by which specification II (CPI with SGI index of global inflation) outperforms specification I (CPI). Relative to specification I, specification II yield a smaller standard deviation of forecasting errors, exhibits less overshooting with the minimum, and produces more accurate forecasts, as indicated by smaller MAFE, MAFE, and RMFSE values.

Note that a negative Theil’s U 2 value is obtained for specification I, indicating that the specification fails to forecast its inflation rate (Table 4 ). Notably, the second specification has a positive Theil’s U 2 value, which indicates that it outperforms the forecasts generated by the naïve model. Additionally, Theil’s decomposition of MSPE shows that 73% (47%) of the MSPE in specification I (Specification II) can be attributed to U bias and U regression . Thus, Theil’s decomposition confirms the superiority of specification II, with the component of the MSPE value being attributed to U error is 53% for specification II but only 29% for specification I. The superior performance of specification II is further confirmed in the out-of-sample forecast plots shown in Fig.  5 . Thus, our forecasts indicate that accounting for spillover effects of global inflation in the Phillips equation improves out-of-sample forecasting of the Phillips curve for the US inflation rate.

5 Conclusion

This paper explores the effects of a new measure for global inflation on domestic inflation in the USA. This new measure, termed SGI, is a PCA-based independent composite factor representing global inflation. The SGI exhibits stable and robust short- and long-term correlations with the domestic inflation rate in the USA. The global inflation variable leads the US inflation rate and contributes on the average 80% to price discovery. Incorporating the SGI variable improves the in-sample overall fit by 14.5% and increases the Phillips curve slope by 37%. Furthermore, accounting for spillover effects from global inflation dynamics in the Phillips equation improves out-of-sample forecasting of the Phillips curve for the US inflation rate.

There are many policy implications of the current research. First, globalization can potentially explain the coincidence of solid growth and low inflation before the COVID-19 recession despite the US economy operating around its potential level. Thus, these results support the recent changes in the Federal Reserve's Statement on Longer-Run Goals and Strategy, which call for abandoning the longstanding principle of reducing accommodation preemptively when the unemployment rate nears its natural rate. Second, the presently documented spillover phenomenon indicates that US monetary policy should respond to global inflation in the economies of its primary trade partners. Thus, strong coordination among major central banks is vital for local and international inflation stability. Third, the social and economic cost of restoring domestic inflation to its long-term goals could increase as the domestic economy is more responsive to global exogenous shocks.

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Guirguis, H., Dutra, V.B. & McGreevy, Z. The impact of global economies on US inflation: A test of the Phillips curve. J Econ Finan 46 , 575–592 (2022). https://doi.org/10.1007/s12197-022-09583-x

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The Reporter

Inflation Dynamics during COVID-19

For over a decade, my research has explored the use of high-frequency microdata to measure inflation and other economic statistics in real time in an effort to make academic macroeconomic research more timely and useful for policymakers. The COVID-19 pandemic has provided a unique opportunity to test this methodology, particularly around the topic of inflation. After the crisis started, the United States experienced a relatively small decline in inflation in 2020, followed by a sudden surge in prices in early 2021. Understanding these inflation dynamics has been the focus of my recent papers. In particular, I studied the impact of COVID-related consumer price index (CPI) measurement distortions and supply disruptions, both of which can be quantified with new sources of high-frequency microdata.

Measurement Distortions with CPI Basket Weights

Early in the pandemic, W. Erwin Diewert and Kevin J. Fox warned that the standard fixed-basket-of-goods approach used by CPIs, which relies on category weights updated infrequently with lagged expenditure data, could introduce significant measurement bias. 1 In the US, CPI weights were last updated in December 2019, shortly before the pandemic dramatically changed consumer spending patterns. To quantify the extent of this basket bias, I relied on the work of Raj Chetty, John N. Friedman, Nathaniel Hendren, Michael Stepner, and the Opportunity Insights Team, who publish real-time consumer spending patterns based on credit and debit card transactions. 2 Using their high-frequency data, I updated the official CPI basket weights on a monthly basis, and combined them with CPIs for various categories of goods to compute an alternative index that can approximate the inflation rate of a COVID-adjusted consumption basket. 3

With this “COVID CPI,” I found that the annual CPI inflation rate in the US was significantly underestimated in 2020, as shown in Figure 1. This was because there was too much weight given to Transportation, a category initially experiencing deflation, and too little weight on Food at Home, where prices were rising. The distortion extended to the core CPI because non-energy transportation categories were impacted, as well as to the PCE (Personal Consumption Expenditures) Price Index, which has a chaining methodology that only partially adjusted to the sudden changes in consumer expenditure patterns.

The CPI basket distortion is temporary, but it is still affecting the annual inflation rate in mid-2021. In fact, US annual inflation is now being overestimated by about 0.7 percentage points. There are two reasons for this. First, the CPI was still placing too much weight on Transportation during the first half of 2021, a time when CPI categories such as Used Cars and Trucks experienced particularly strong increases in prices. Second, the base effects in the annual inflation calculation are larger than they would have been if the CPI basket had been adjusted. The base effects occur because the annual rate compares prices today to those 12 months ago. The fact that the CPI was underestimated in the second quarter of 2020 means that the annual inflation rate is higher in mid-2021.

Supply Disruptions and Product Shortages

Temporary measurement distortions only partially explain why the inflation rate is higher today. An even more important driver of recent inflation is the persistence of COVID-related supply disruptions, which can be seen in the increase in product shortages. Indeed, the rise in product stockouts has been a defining characteristic of the pandemic, and although frequently mentioned in media and policy reports, so far there has been little empirical analysis of its impact on inflation. 4

I provide direct evidence in joint work with Oleksiy Kryvtsov using microdata collected online to construct a high-frequency measure of product shortages in a wide range of consumer products. 5 We focus not just on out-of-stock signals that are visible to consumers, but also on the higher incidence of discontinued goods, which are harder to detect. Our stockout measures, shown in Figure 2, prove that shortages were widespread early on in the pandemic, affecting far more than just toilet paper and disinfecting wipes. Over time, the composition of shortages evolved from many “temporary” stockouts to mostly discontinued goods, or permanent stockouts, concentrated in fewer sectors. By early May 2021, US stockouts showed signs of improvement in some categories, but remained near record levels for Food and Beverages and Electronics.

Combining these stockout measures with micro price data, we find that their impact on inflation is significant, gradual, and transitory. This can be seen in Figure 3, which shows the estimated response of monthly inflation to a stockout shock. Our estimates imply, for example, that an increase in the stockout rate from 10 percent to 20 percent would bring about a 0.10 percentage point increase in the monthly inflation rate within two months. The impact rises gradually after two weeks, peaks around six weeks, and dissipates after three months. We find a similar response when we focus only on temporary stockout shocks, which were more significant at the beginning of the pandemic, suggesting that shortages have been putting upward pressure on prices all along, even though the effects were harder to detect with aggregate statistics when demand was falling. Furthermore, in previous work 6 I showed that online and offline prices are similar for large US retailers, which suggests that the inflation effects likely extend to brick-and-mortar store prices as well.

These results are also qualitatively similar when the shock is measured using a model-based estimation of the underlying replacement cost. To show this, we develop a model of a monopolistic firm with inventories and use it to derive an empirical specification for estimating the costs behind the observed dynamics of stockouts and prices at a sector level. We then construct empirical responses of inflation to the estimated cost shocks, and find that accounting for the endogeneity of stockouts makes the inflationary effects stronger immediately after the shocks, but also more transitory.

This estimated cost pass-through into retail prices is relatively quick when compared to that of other recent shocks, such as the rise in tariffs during the US-China trade war. For example, Gita Gopinath, Brent Neiman, Jenny Tang, and I showed in 2019 that the tariffs on Chinese goods had limited short-run effects on consumer prices. 7 We found evidence of other margins of adjustment, such as front-running of inventories and trade diversion, which were not possible in the pandemic, but also some evidence that retailers delayed the pass-through because they expected the tariffs to be temporary. We speculated that if the shock had remained for much longer, pressure on these retailers would likely have risen and the pass-through into consumer prices would have increased. Consistent with this hypothesis, in the more recent COVID stockouts paper we found that the impact on inflation is higher in sectors where the stockouts have been particularly persistent, such as Food and Electronics. This result also applies at the country level, with the US, Canada, and Germany having both more persistent stockouts and also the largest impacts on annual inflation rates.

Another explanation for the quick retail pass-through is that the COVID crisis moved a large share of transactions online. In a 2018 paper prepared for the Federal Reserve Bank of Kansas City’s Economic Policy Symposium in Jackson Hole, I showed that large traditional retailers competing with online firms tend to adjust their prices more frequently and have more uniform prices across locations. 8 This makes their prices react faster to national-level cost shocks. I argued that retail prices are becoming less insulated and that as online transactions increase any other shock that may enter the pricing algorithms used by large retailers is more likely to have a larger impact on retail prices than in the past. This is precisely the type of shock caused by COVID supply disruptions.

Looking Ahead

Overall, my recent research suggests that temporary factors, such as measurement distortions and supply disruptions, are an important driver of inflation at this stage of the pandemic. However, high-frequency online inflation indices, based on a methodology I started developing in my PhD thesis over a decade ago, 9 continue to show that inflationary pressures are abnormally high in the US, with monthly rates above 10-year averages for the past 11 months. 10

Whether inflation remains high will depend on how COVID-related supply and demand shocks evolve moving forward. But this is certain: the pandemic has highlighted the importance of high-frequency data for economic measurement during times of crisis. It has dramatically accelerated the use of online data collection at the Bureau of Labor Statistics and other statistical agencies, and could potentially lead to other changes in practice, such as an increase in the frequency of expenditure surveys and the updating of CPI weights. 11 These changes would provide a more detailed and less distorted picture of inflation during times of crisis. More importantly, the development and growing availability of new microdata will continue to improve our understanding of the mechanisms affecting inflation dynamics in the future.

Alberto Cavallo is a cofounder of, and has an ownership stake in, PriceStats LLC, a private company that uses online data to compute inflation indices. He is also an unpaid member of the Technical Advisory Committee of the US Bureau of Labor Statistics, where he provides advice on various issues, including the use of online data in the construction of consumer price indexes and other statistics.

Researchers

More from nber.

“ Measuring Real Consumption and CPI Bias under Lockdown Conditions ,” Diewert WE, Fox K. NBER Working Paper 27144, May 2020.  

“ The Economic Impacts of COVID-19: Evidence from a New Public Database Built Using Private Sector Data ,” Chetty R, Friedman J, Hendren N, Stepner M, Opportunity Insights Team. NBER Working Paper 27431, June 2020.  

“ Inflation with COVID Consumption Baskets, ” Cavallo A. NBER Working Paper 27352, June 2020.  

“ Pandemic Prices: Assessing Inflation in the Months and Years Ahead ,” Bernstein J, Tedeschi E. Council of Economic Advisers, April 12, 2021.  

“ What Can Stockouts Tell Us about Inflation? Evidence from Online Micro Data ,” Cavallo A, Kryvtsov O. NBER Working Paper 29209, September 2021.  

“ Are Online and Offline Prices Similar? Evidence from Large Multi-Channel Retailers ,” Cavallo A. NBER Working Paper 22142, April 2016, and American Economic Review 107(1), January 2017, pp. 283–303.  

“ Tariff Pass-Through at the Border and at the Store: Evidence from US Trade Policy ,” Cavallo A, Gopinath G, Neiman B, Tang J. NBER Working Paper 26396, October 2019, and American Economic Review: Insights 3(1), March 2021, pp. 19–34.  

“ More Amazon Effects: Online Competition and Pricing Behaviors ,” Cavallo A. NBER Working Paper 25138, October 2018, and presented at Changing Market Structures and Implications for Monetary Policy, Jackson Hole Economic Policy Symposium , Jackson Hole, Wyoming, August 23–25, 2018.  

“ Online and Official Price Indexes: Measuring Argentina’s Inflation ,” Cavallo A. Journal of Monetary Economics 60(2), March 2013, pp. 152–165.  

“ COVID Inflation: Evidence from Real-Time Data,” Cavallo A. Princeton University Bendheim Center for Finance, Markus Academy Series, June 17, 2021.  

“ Effects of COVID-19 Pandemic and Response on the Consumer Price Index ,” US Bureau of Labor Statistics, accessed September 1, 2021.

NBER periodicals and newsletters may be reproduced freely with appropriate attribution.

In addition to working papers , the NBER disseminates affiliates’ latest findings through a range of free periodicals — the NBER Reporter , the NBER Digest , the Bulletin on Retirement and Disability , the Bulletin on Health , and the Bulletin on Entrepreneurship  — as well as online conference reports , video lectures , and interviews .

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The U.S. economy and inflation might be cooling off, but lower interest rates are still far away

By Jeffry Bartash

A slower economy could help reduce inflation and keep the likelihood of recession low

Is the economy headed in the right direction? Wall Street investors certainly think so. They drove U.S. stocks to a record high this week.

Why the optimism?

The inflation rate didn't get any worse in April, for one thing. Inflation surged in the first three months of the year in a shock to Wall Street and the Federal Reserve.

Americans also curbed their spending at retail stores last month in what some took as a sign that the economy is cooling.

A cooler economy, of course, is not usually viewed as a good thing. But the U.S. grew at an unusually fast pace in the second half of 2023, and many economists believe strong growth has helped to keep inflation elevated.

What is needed to get inflation to slow to low prepandemic levels, they say, is a U.S. economy returning to a more customary rate of growth. Somewhat less consumer demand would force businesses to freeze or even lower prices and help the Fed to get inflation back down to its 2% goal.

"We are getting back to a more normal economy," said Kathy Bostjancic, chief economist at Nationwide. "There is no sign yet that we are seeing a real slowdown."

To be sure, the April report on U.S. retail sales set off some alarm bells. Sales were flat in April and they were revised lower in the prior two months.

Some economists believe consumers have hit their spending limit. Savings are down, pandemic-era government stimulus is gone and lots of lower- and middle-income families are maxing out on credit. Now they have to get by just on their weekly paychecks.

"Many of our members are one blown transmission away from financial crisis," said Robert Frick, a corporate economist at Navy Federal Credit Union.

The manufacturing side of the economy, meanwhile, was already a weak spot and hasn't gotten any better.

Production was flat in April and companies are hesitant to ramp up, particularly with interest rates still high and consumers spending the bulk of their money on services instead of goods.

The resulting softness produced a lackluster 1.6% annual increase in gross domestic product in the first three months of 2024 after sizzling 3.4% and 4.9% increases in the final two quarters of last year.

Economists believe the top sustainable speed of the U.S. economy - how fast it can grow without stoking inflation - is around 1.8%.

"If you are looking for reasons to be concerned, you can find the argument for that," said Jim Baird, chief investment officer at Plante Moran Financial Advisors.

Baird, for his part, is not especially worried. "If you are looking for reasons to be optimistic, you can find that as well."

A big reason to be optimistic, he said, is the strong U.S. labor market.

Companies have enough business to retain most of their current employees, keeping layoffs near record lows. And many firms still are adding jobs to hold the unemployment rate below 4%.

"As long as we are adding jobs, everything will be OK," Frick said. "The backbone of the recovery is still jobs."

Frick expects companies to continue to add plenty of jobs. He points out that employment is still three million jobs below the prepandemic trend.

"At least through this year, we should have good hiring and decent spending," he said.

Consumer spending is bound to slow a bit more, though, if all households can count on is their current income.

"If you have a job, are earning income, you are going to spend," Bostjancic said. "It's just a matter of degree."

If consumer spending tapers off, the thinking goes, inflation is likely resume its downward trend toward the Fed's annual 2% goal and pave the way for the central bank to cut U.S. interest rates much later this year. Inflation has been stuck in the low- to mid-3% range.

"As long as inflation is coming down, that's OK," Baird said.

The Fed jacked up a key short-term interest rate to a 23-year high in 2022-2023 to combat the highest inflation in 40 years. Higher borrowing costs have depressed key parts of the economy, mostly notably housing and business investment.

If the Fed begins to cut rates, as markets expect, lower borrowing costs are expected to boost the economy and reduce the already-low chances of recession.

Still, investors and the Fed will need to see a lot more progress on inflation before they gain the confidence, in the words of top Fed officials, to reduce rates. The April consumer-price index was just a start.

"I don't see any indicators now telling me, oh, that there's a reason to change the stance of monetary policy now," New York Fed chief John Williams said in an interview with Reuters this week. "I don't expect to get that greater confidence that we need to see on the inflation progress toward a 2% goal in the very near term."

-Jeffry Bartash

This content was created by MarketWatch, which is operated by Dow Jones & Co. MarketWatch is published independently from Dow Jones Newswires and The Wall Street Journal.

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05-18-24 0755ET

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How would a resurgent us-china trade war affect bitcoin prices.

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WASHINGTON, DC - NOVEMBER 15: U.S. President Joe Biden participates in a virtual meeting with ... [+] Chinese President Xi Jinping at the Roosevelt Room of the White House November 15, 2021 in Washington, DC. President Biden met with his Chinese counterpart to discuss bilateral issues. (Photo by Alex Wong/Getty Images)

President Biden's recent announcement of tariffs on Chinese imports (including a 100% tariff on Chinese EVs) seems to auger a reviving US-China trade war—one that might be continued if ex-President Trump were to retake power and was coupled with an order to vacate a Chinese-owned Bitcoin Bitcoin mining facility next to a military base and a drive in Congress to ban TikTok unless it's American-owned.

My research for a book on Bitcoin + China shows that Chinese state actions and investors still play a vital role in Bitcoin's price. The trend can be seen in the increasing tension between the world's two largest economies, which will likely have many knock-on effects, including on Bitcoin. So far, the response has been muted on the Chinese side. However, the trade war will likely continue or potentially escalate with either a new Trump administration or a continued Biden Administration.

1- If US-China tensions flare, inflation might fire up, which is bearish for Bitcoin in the short term but perhaps bullish in the long term

If tariffs turn into an inflationary push, this is likely to be the most significant price impact Bitcoin will feel in the short term, though it validates Bitcoin as a long-term thesis.

Tariffs would, all things equal, increase prices for end consumers trying to buy goods. Chinese exports post-COVID are likely a globally deflationary force and a response to a slumping domestic Chinese economy. They are part of the reason for this renewed trade war with accusations of overstimulation of China's domestic manufacturing - which Europe and the United States have decried as "overcapacity." Anything that helps increase prices will decrease the probability of Fed rate decreases - though this first round of tariffs directly affects about $18bn in yearly Chinese imports, it also maintains Trump-era tariffs on $300bn in Chinese imports and will likely see a continued shift away from American imports of Chinese goods . An uptick in inflation will hurt Bitcoin in the short term as it now carries a correlation as a risk-on asset that responds more favorably to the prospect of the Fed dialing rates down.

However, in the long term, the value of the US dollar may continue to decrease compared with Bitcoin. Bitcoin might be bullish or bearish in the short term—its' 24/7 trading pattern and multiple liquid exchanges promise price volatility in response to events that equities don't have the time to respond to.

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If you look beyond the noise, Bitcoin is a counter to that thesis that is hard to see in isolation but easier to detect in years-long trend lines. Suppose governments are stuck but have no choice but to issue more debt for less economic growth. In that case, the money supply will continue growing for the foreseeable future—and that money supply will be denominated by some with assets like Bitcoin that will not see that same debasement.

2- Bitcoin miners might be subject to (increased) tariffs

Bitcoin miners may get picked up in the new semiconductor tariffs. During the Trump years, Bitcoin mining companies spent extra time trying to obtain updated mining chips since these were Chinese-sourced from companies such as Bitmain. Bitmain upped prices that American miners were still willing to pay since Bitmain has a dominant market position and is one of the only players with access to the latest semiconductors. So even though import taxes went from 0% to around 27.6%, there were still willing buyers. The Biden Administration has now upped the tariff on Chinese-source semiconductors from 25% to 50%, taking effect in 2025.

The increase in the price of Bitcoin mining equipment would have knock-on effects on the composition and competitiveness of Bitcoin miners (who are already struggling with a post-halving environment with lower transaction fees than during the halving hype) and on Bitcoin mining stock prices, many of which are public American companies sourcing Chinese-sourced mining hardware.

3- A "battle of jurisdictions" might take hold in the realm of custody of Bitcoin

With the release of Hong Kong-based ETFs for buying Bitcoin , there is a "battle of jurisdictions" to attract foreign investment into Hong Kong - positioned so that the Hong Kong ETFs are more competitive on "in-kind" redemptions and tax treatment than the American spot ETFs. This battle may continue and become more pointed as the Chinese party-state looks to go beyond banning the usage of stablecoins and Bitcoin in the Mainland, to being more open to a custodied solution -which will likely affect Bitcoin prices positively but potentially erode its utility as privacy and self-sovereignty tech.

Hong Kong is effectively trying to shut down ways to transact cash and other more privacy-preserving rails for Bitcoin, trying to force everything into a custodian of an underlying regulated Hong Kong exchange. The privacy and self-custody route being cut off for an official siren call to Bitcoin ETFs with custody by regulated exchanges is happening in Hong Kong - though this isn't a path that Hong Kong stands alone in implementing.

4- US-China tensions leading to kinetic conflict will lead to dramatic short-term Bitcoin price effects

In the (unlikely) scenario that US-China tensions lead to kinetic conflict, the 10% hourly drop in Bitcoin prices seen during the Iran crisis might be a drop in the water . Many hotspots will likely disrupt global trade, commerce, and peace worldwide. With global blocs and credible multilateralism becoming more established, the potential for escalations grows ever more. However, the prediction of immediate kinetic conflict in Taiwan may take time to come to pass. Still, even flashpoints like conflict in the South China Sea might drive news cycles in the short term enough to affect Bitcoin prices.

In the long run, Bitcoin's self-custody and "money without the state" principles may come to the fore with people demanding stores of value outside of their currencies (likely going to be increasingly in debt with established debt as well as the cost of warfare) and for people who will look to flee their country of birth.

A new US-China trade war during the grips of an election year may be a preview of the next decade. Bitcoin has finally encountered a high-rate environment and, over the long term, still shows consistent growth cycles throughout these new environments. In the short term, if a US-China trade war plays out entirely, it will affect many things, including the short-term price movement of Bitcoin. Yet those focused on holding Bitcoin for the long-term in self-custody may not notice and end up with a hard asset that can survive (and perhaps even grow) with both kinetic conflict and economic tension between the world's two largest economies.

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